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Objective To evaluate the impact of catch-up human papillomavirus (HPV) vaccination on conization rates in France in a large population-based study. Methods We conducted a retrospective real-life cohort study on data collected prospectively by French National Health Insurance. Echantillon généralistes des bénéficiaires (EGB) is a database composed of demographic and health care utilization data for a 1/97th sample of the French population. We extracted data about all women born between 1983 and 1991, corresponding to the catch-up population (vaccination after 14 years old) at the time of implementation of HPV vaccination. The primary outcome was the occurrence of conization (all types of procedures) compared between vaccinated and non-vaccinated women. Results The cohort consisted of 42,452 women. Vaccination coverage (at least one dose) was low (9.8%, n = 4,129), but increased with time from vaccine implementation, from 0% in the 1983 cohort to 31% in the 1991 cohort. The conization rate was 1% for the overall population. The risk of conization for women between the ages of 19 and 30 years was reduced in the vaccinated group with a Hazard Ratio (HR) of 0.59 (95% CI[0.39–0.90]; p = 0.043). Conclusions With a 10-year follow-up, catch-up HPV vaccination is associated with risk reduction of conization between the ages of 19 and 30.

ObjectiveTo evaluate the impact of catch-up human papillomavirus (HPV) vaccination on conization rates in France in a large population-based study.MethodsWe conducted a retrospective real-life cohort study on data collected prospectively by French National Health Insurance. Echantillon généralistes des bénéficiaires (EGB) is a database composed of demographic and health care utilization data for a 1/97th sample of the French population. We extracted data about all women born between 1983 and 1991, corresponding to the catch-up population (vaccination after 14 years old) at the time of implementation of HPV vaccination. The primary outcome was the occurrence of conization (all types of procedures) compared between vaccinated and non-vaccinated women.ResultsThe cohort consisted of 42,452 women. Vaccination coverage (at least one dose) was low (9.8%, n = 4,129), but increased with time from vaccine implementation, from 0% in the 1983 cohort to 31% in the 1991 cohort. The conization rate was 1% for the overall population. The risk of conization for women between the ages of 19 and 30 years was reduced in the vaccinated group with a Hazard Ratio (HR) of 0.59 (95% CI[0.39-0.90]; p = 0.043).ConclusionsWith a 10-year follow-up, catch-up HPV vaccination is associated with risk reduction of conization between the ages of 19 and 30.

In October of 2020, in response to the Coronavirus Disease 2019 (COVID-19) pandemic, our team hosted our first fully online workshop teaching the QIIME 2 microbiome bioinformatics platform. We had 75 enrolled participants who joined from at least 25 different countries on 6 continents, and we had 22 instructors on 4 continents. In the 5-day workshop, participants worked hands-on with a cloud-based shared compute cluster that we deployed for this course. The event was well received, and participants provided feedback and suggestions in a postworkshop questionnaire. In January of 2021, we followed this workshop with a second fully online workshop, incorporating lessons from the first. Here, we present details on the technology and protocols that we used to run these workshops, focusing on the first workshop and then introducing changes made for the second workshop. We discuss what worked well, what didn't work well, and what we plan to do differently in future workshops.

[This corrects the article DOI: 10.1371/journal.pntd.0002880.].

The analysis of single-cell genomics data presents several statistical challenges, and extensive efforts have been made to produce methods for the analysis of this data that impute missing values, address sampling issues and quantify and correct for noise. In spite of such efforts, no consensus on best practices has been established and all current approaches vary substantially based on the available data and empirical tests. The k-Nearest Neighbor Graph (kNN-G) is often used to infer the identities of, and relationships between, cells and is the basis of many widely used dimensionality-reduction and projection methods. The kNN-G has also been the basis for imputation methods using, e.g., neighbor averaging and graph diffusion. However, due to the lack of an agreed-upon optimal objective function for choosing hyperparameters, these methods tend to oversmooth data, thereby resulting in a loss of information with regard to cell identity and the specific gene-to-gene patterns underlying regulatory mechanisms. In this paper, we investigate the tuning of kNN- and diffusion-based denoising methods with a novel non-stochastic method for optimally preserving biologically relevant informative variance in single-cell data. The framework, Denoising Expression data with a Weighted Affinity Kernel and Self-Supervision (DEWÄKSS), uses a self-supervised technique to tune its parameters. We demonstrate that denoising with optimal parameters selected by our objective function (i) is robust to preprocessing methods using data from established benchmarks, (ii) disentangles cellular identity and maintains robust clusters over dimension-reduction methods, (iii) maintains variance along several expression dimensions, unlike previous heuristic-based methods that tend to oversmooth data variance, and (iv) rarely involves diffusion but rather uses a fixed weighted kNN graph for denoising. Together, these findings provide a new understanding of kNN- and diffusion-based denoising methods. Code and example data for DEWÄKSS is available at https://gitlab.com/Xparx/dewakss/-/tree/Tjarnberg2020branch.

Health, disease, and mortality vary greatly at the county level, and there are strong geographical trends of disease in the United States. Healthcare is and has been a top priority for voters in the U.S., and an important political issue. Consequently, it is important to determine what relationship voting patterns have with health, disease, and mortality, as doing so may help guide appropriate policy. We performed a comprehensive analysis of the relationship between voting patterns and over 150 different public health and wellbeing variables at the county level, comparing all states, including counties in 2016 battleground states, and counties in states that flipped from majority Democrat to majority Republican from 2012 to 2016. We also investigated county-level health trends over the last 30+ years and find statistically significant relationships between a number of health measures and the voting patterns of counties in presidential elections. Collectively, these data exhibit a strong pattern: counties that voted Republican in the 2016 election had overall worse health outcomes than those that voted Democrat. We hope that this strong relationship can guide improvements in healthcare policy legislation at the county level.

Many scientific disciplines rely on computational methods for data analysis, model generation, and prediction. Implementing these methods is often accomplished by researchers with domain expertise but without formal training in software engineering or computer science. This arrangement has led to underappreciation of sustainability and maintainability of scientific software tools developed in academic environments. Some software tools have avoided this fate, including the scientific library Rosetta. We use this software and its community as a case study to show how modern software development can be accomplished successfully, irrespective of subject area. Rosetta is one of the largest software suites for macromolecular modeling, with 3.1 million lines of code and many state-of-the-art applications. Since the mid 1990s, the software has been developed collaboratively by the RosettaCommons, a community of academics from over 60 institutions worldwide with diverse backgrounds including chemistry, biology, physiology, physics, engineering, mathematics, and computer science. Developing this software suite has provided us with more than two decades of experience in how to effectively develop advanced scientific software in a global community with hundreds of contributors. Here we illustrate the functioning of this development community by addressing technical aspects (like version control, testing, and maintenance), community-building strategies, diversity efforts, software dissemination, and user support. We demonstrate how modern computational research can thrive in a distributed collaborative community. The practices described here are independent of subject area and can be readily adopted by other software development communities.

Gene regulatory networks are composed of sub-networks that are often shared across biological processes, cell-types, and organisms. Leveraging multiple sources of information, such as publicly available gene expression datasets, could therefore be helpful when learning a network of interest. Integrating data across different studies, however, raises numerous technical concerns. Hence, a common approach in network inference, and broadly in genomics research, is to separately learn models from each dataset and combine the results. Individual models, however, often suffer from under-sampling, poor generalization and limited network recovery. In this study, we explore previous integration strategies, such as batch-correction and model ensembles, and introduce a new multitask learning approach for joint network inference across several datasets. Our method initially estimates the activities of transcription factors, and subsequently, infers the relevant network topology. As regulatory interactions are context-dependent, we estimate model coefficients as a combination of both dataset-specific and conserved components. In addition, adaptive penalties may be used to favor models that include interactions derived from multiple sources of prior knowledge including orthogonal genomics experiments. We evaluate generalization and network recovery using examples from Bacillus subtilis and Saccharomyces cerevisiae, and show that sharing information across models improves network reconstruction. Finally, we demonstrate robustness to both false positives in the prior information and heterogeneity among datasets.

Wolbachia are maternally inherited endosymbiotic bacteria, widespread among arthropods thanks to host reproductive manipulations that increase their prevalence into host populations. The most commonly observed manipulation is cytoplasmic incompatibility (CI). CI leads to embryonic death in crosses between i) infected males and uninfected females and ii) individuals infected with incompatible Wolbachia strains. CI can be conceptualized as a toxin-antidote system where a toxin deposited by Wolbachia in the sperm would induce embryonic death unless countered by an antidote produced by Wolbachia present in the eggs. In Drosophila melanogaster, transgenic expression of Wolbachia effector cidB revealed its function of CI-inducing toxin. Moreover in Culex pipiens, the diversity of cidB variants present in wPip strains accounts for the diversity in crossing-types. We conducted cytological analyses to determine the CI mechanisms that lead to embryonic death in C. pipiens, and assess whether diversity in crossing-types could be based on variations in these mechanisms. We revealed that paternal chromatin condensation and segregation defects during the first embryonic division are always responsible for embryonic death. The strongest observed defects lead to an exclusion of the paternal chromatin from the first zygotic division, resulting in haploid embryos unable to hatch. The proportion of unhatched haploid embryos, developing with only maternal chromatin, which reflects the frequency of strong defects can be considered as a proxy of CI intensity at the cellular level. We thus studied the putative effect of variations in crossing types and cidB diversification on CI defects intensity. Incompatible crosses involving distinct wPip strains revealed that CI defects intensity depends on the Wolbachia strains hosted by the males and is linked to the diversity of cidB genes harbored in their genomes. These results support that, additionally to its implication in C. pipiens crossing type variability, cidB diversification also influences the strength of CI embryonic defects.

The treatment of hepatitis C virus (HCV) infection by combination of direct acting antivirals (DAA), with different mode of action, has made substantial progress in the past few years. However, appearance of resistance and high cost of the therapy is still an obstacle in the achievement of the therapy, more specifically in developing countries. In this context, search for affordable antivirals with new mechanisms of action is still needed. Tea, after water, is the most popular drink worldwide. Polyphenols extracted from green tea have already shown anti-HCV activity as entry inhibitors. Here, three different theaflavins, theaflavin (TF1), theaflavin-3'-monogallate (TF2), and theaflavin-3-3'-digallate (TF3), which are major polyphenols from black tea, were tested against HCV in cell culture. The results showed that all theaflavins inhibit HCV infection in a dose-dependent manner in an early step of infection. Results obtained with HCV pseudotyped virions confirmed their activity on HCV entry and demonstrated their pan-genotypic action. No effect on HCV replication was observed by using HCV replicon. Investigation on the mechanism of action of black tea theaflavins showed that they act directly on the virus particle and are able to inhibit cell-to-cell spread. Combination study with inhibitors most widely used in anti-HCV treatment regimen demonstrated that TF3 exerts additive effect. In conclusion, theaflavins, that are present in high quantity in black tea, are new inhibitors of HCV entry and hold promise for developing in therapeutic arsenal for HCV infection.

Conversion of wild habitats to human dominated landscape is a major cause of biodiversity loss. An approach to mitigate the impact of habitat loss consists of designating reserves where habitat is preserved and managed. Determining the most valuable areas to preserve in a landscape is called the reserve design problem. There exists several possible formulations of the reserve design problem, depending on the objectives and the constraints. In this article, we considered the dynamic problem of designing a reserve that contains a desired area of several key habitats. The dynamic case implies that the reserve cannot be designed in one time step, due to budget constraints, and that habitats can be lost before they are reserved, due for example to climate change or human development. We proposed two heuristics strategies that can be used to select sites to reserve each year for large reserve design problem. The first heuristic is a combination of the Marxan and site-ordering algorithms and the second heuristic is an augmented version of the common naive myopic heuristic. We evaluated the strategies on several simulated examples and showed that the augmented greedy heuristic is particularly interesting when some of the habitats to protect are particularly threatened and/or the compactness of the network is accounted for.

Determining the three dimensional arrangement of proteins in a complex is highly beneficial for uncovering mechanistic function and interpreting genetic variation in coding genes comprising protein complexes. There are several methods for determining co-complex interactions between proteins, among them co-fractionation / mass spectrometry (CF-MS), but it remains difficult to identify directly contacting subunits within a multi-protein complex. Correlation analysis of CF-MS profiles shows promise in detecting protein complexes as a whole but is limited in its ability to infer direct physical contacts among proteins in sub-complexes. To identify direct protein-protein contacts within human protein complexes we learn a sparse conditional dependency graph from approximately 3,000 CF-MS experiments on human cell lines. We show substantial performance gains in estimating direct interactions compared to correlation analysis on a benchmark of large protein complexes with solved three-dimensional structures. We demonstrate the method's value in determining the three dimensional arrangement of proteins by making predictions for complexes without known structure (the exocyst and tRNA multi-synthetase complex) and by establishing evidence for the structural position of a recently discovered component of the core human EKC/KEOPS complex, GON7/C14ORF142, providing a more complete 3D model of the complex. Direct contact prediction provides easily calculable additional structural information for large-scale protein complex mapping studies and should be broadly applicable across organisms as more CF-MS datasets become available.

Dry immersion (DI), a ground-based model of microgravity previously used in Russia, has been recently implemented in France. The aim of this study was to analyze early events in a short-term DI model in which all conditions are met to investigate who is first challenged from osteo- or adipo-kines and to what extent they are associated to insulin-regulating hormones.Twelve healthy men were submitted to a 3-day DI. Fasting blood was collected during pre-immersion phase for the determination of the baseline data collection (BDC), daily during DI (DI24h, DI48H and DI72h), then after recovery (R+3h and R+24h). Markers of bone turnover, phosphocalcic metabolism, adipokines and associated factors were measured.Bone resorption as assessed by tartrate-resistant acid phosphatase isoform 5b and N-terminal crosslinked telopeptide of type I collagen levels increased as early as DI24h. At the same time, total procollagen type I N- and C-terminal propeptides and osteoprotegerin, representing bone formation markers, decreased. Total osteocalcin [OC] was unaffected, but its undercarboxylated form [Glu-OC] increased from DI24h to R+3h. The early and progressive increase in bone alkaline phosphatase activities suggested an increased mineralization. Dickkopf-1 and sclerostin, as negative regulators of the Wnt-β catenin pathway, were unaltered. No change was observed either in phosphocalcic homeostasis (calcium and phosphate serum levels, 25-hydroxyvitamin D, fibroblast growth factor 23 [FGF23]) or in inflammatory response. Adiponectemia was unchanged, whereas circulating leptin concentrations increased. Neutrophil gelatinase-associated lipocalin [lipocalin-2], a potential regulator of bone homeostasis, was found elevated by 16% at R+3h compared to DI24h. The secretory form of nicotinamide phosphoribosyl-transferase [visfatin] concentrations almost doubled after one day of DI and remained elevated. Serum insulin-like growth factor 1 levels progressively increased. Fasting insulin concentrations increased during the entire DI, whereas fasting glucose levels tended to be higher only at DI24h and then returned to BDC values. Changes in bone resorption parameters negatively correlated with changes in bone formation parameters. Percent changes of ultra-sensitive C-reactive protein positively correlated with changes in osteopontin, lipocalin-2 and fasting glucose. Furthermore, a positive correlation was found between changes in FGF23 and Glu-OC, the two main osteoblast-/osteocyte-derived hormones.Our results demonstrated that DI induced an unbalanced remodeling activity and the onset of insulin resistance. This metabolic adaptation was concomitant with higher levels of Glu-OC. This finding confirms the role of bone as an endocrine organ in humans. Furthermore, visfatin for which a great responsiveness was observed could represent an early and sensitive marker of unloading in humans.

To validate a model predicting the risk of threatened preterm delivery and to establish the optimal threshold for this risk scoring system.Two cohorts were studied: one of singleton pregnancies without preterm premature rupture of membranes (PPROM) and no cervical cerclage (cohort 1) and one of twin pregnancies without PPROM and no cervical cerclage (cohort 2). Patients were included from January 1st 2013 until December 31st 2013 by the Regional Perinatal Network of Ile de France with patients transferred because of threatened preterm delivery at 22 to 32 weeks of gestation. The individual probability of delivery within 48 hours of admission was calculated using the nomogram for every patient. Discrimination and calibration of the nomogram as well as the optimal threshold were determined using R studio.The nomogram accurately predicted obstetric outcome. Discrimination and calibration were excellent, with an area under the curve (AUC) of 0.88 (95% CI 0.86-0.90) for cohort 1 and 0.73 (95% CI 0.66-0.80) for cohort 2. The optimal threshold would be 15% for cohort 1 and 10% for cohort 2. Using these thresholds, the performance characteristics of the nomogram were: sensitivity 80% (cohort 1) and 69% (cohort 2), negative predictive value 94.8% (cohort 1) and 91.3% (cohort 2). Use of the nomogram would avoid 253 unnecessary transfers in cohort 1.The nomogram was efficient and clinically relevant in our high risk population. A threshold set at 15% would help minimize the risk of preterm deliveries in singleton pregnancies and should reduce unnecessary, costly and stressful in utero transfer.

Iron (Fe) uptake in graminaceous plant species occurs via the release and uptake of Fe-chelating compounds known as mugineic acid family phytosiderophores (MAs). In the MAs biosynthetic pathway, nicotianamine aminotransferase (NAAT) and deoxymugineic acid synthase (DMAS) enzymes catalyse the formation of 2'-deoxymugineic acid (DMA) from nicotianamine (NA). Here we describe the identification and characterisation of six TaNAAT and three TaDMAS1 genes in bread wheat (Triticum aestivum L.). The coding sequences of all six TaNAAT homeologs consist of seven exons with ≥88.0% nucleotide sequence identity and most sequence variation present in the first exon. The coding sequences of the three TaDMAS1 homeologs consist of three exons with ≥97.8% nucleotide sequence identity. Phylogenetic analysis revealed that the TaNAAT and TaDMAS1 proteins are most closely related to the HvNAAT and HvDMAS1 proteins of barley and that there are two distinct groups of TaNAAT proteins-TaNAAT1 and TaNAAT2 -that correspond to the HvNAATA and HvNAATB proteins, respectively. Quantitative reverse transcription-PCR analysis revealed that the TaNAAT2 genes are expressed at highest levels in anther tissues whilst the TaNAAT1 and TaDMAS1 genes are expressed at highest levels in root tissues of bread wheat. Furthermore, the TaNAAT1, TaNAAT2 and TaDMAS1 genes were differentially regulated by plant Fe status and their expression was significantly upregulated in root tissues from day five onwards during a seven-day Fe deficiency treatment. The identification and characterization of the TaNAAT1, TaNAAT2 and TaDMAS1 genes provides a valuable genetic resource for improving bread wheat growth on Fe deficient soils and enhancing grain Fe nutrition.

Understanding gene regulatory networks is critical to understanding cellular differentiation and response to external stimuli. Methods for global network inference have been developed and applied to a variety of species. Most approaches consider the problem of network inference independently in each species, despite evidence that gene regulation can be conserved even in distantly related species. Further, network inference is often confined to single data-types (single platforms) and single cell types. We introduce a method for multi-source network inference that allows simultaneous estimation of gene regulatory networks in multiple species or biological processes through the introduction of priors based on known gene relationships such as orthology incorporated using fused regression. This approach improves network inference performance even when orthology mapping and conservation are incomplete. We refine this method by presenting an algorithm that extracts the true conserved subnetwork from a larger set of potentially conserved interactions and demonstrate the utility of our method in cross species network inference. Last, we demonstrate our method's utility in learning from data collected on different experimental platforms.

4C-Seq has proven to be a powerful technique to identify genome-wide interactions with a single locus of interest (or "bait") that can be important for gene regulation. However, analysis of 4C-Seq data is complicated by the many biases inherent to the technique. An important consideration when dealing with 4C-Seq data is the differences in resolution of signal across the genome that result from differences in 3D distance separation from the bait. This leads to the highest signal in the region immediately surrounding the bait and increasingly lower signals in far-cis and trans. Another important aspect of 4C-Seq experiments is the resolution, which is greatly influenced by the choice of restriction enzyme and the frequency at which it can cut the genome. Thus, it is important that a 4C-Seq analysis method is flexible enough to analyze data generated using different enzymes and to identify interactions across the entire genome. Current methods for 4C-Seq analysis only identify interactions in regions near the bait or in regions located in far-cis and trans, but no method comprehensively analyzes 4C signals of different length scales. In addition, some methods also fail in experiments where chromatin fragments are generated using frequent cutter restriction enzymes. Here, we describe 4C-ker, a Hidden-Markov Model based pipeline that identifies regions throughout the genome that interact with the 4C bait locus. In addition, we incorporate methods for the identification of differential interactions in multiple 4C-seq datasets collected from different genotypes or experimental conditions. Adaptive window sizes are used to correct for differences in signal coverage in near-bait regions, far-cis and trans chromosomes. Using several datasets, we demonstrate that 4C-ker outperforms all existing 4C-Seq pipelines in its ability to reproducibly identify interaction domains at all genomic ranges with different resolution enzymes.

16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC-EASI outperforms state-of-the-art methods to recover edges and network properties on synthetic data under a variety of scenarios. SPIEC-EASI also reproducibly predicts previously unknown microbial associations using data from the American Gut project.

Social media have provided instrumental means of communication in many recent political protests. The efficiency of online networks in disseminating timely information has been praised by many commentators; at the same time, users are often derided as "slacktivists" because of the shallow commitment involved in clicking a forwarding button. Here we consider the role of these peripheral online participants, the immense majority of users who surround the small epicenter of protests, representing layers of diminishing online activity around the committed minority. We analyze three datasets tracking protest communication in different languages and political contexts through the social media platform Twitter and employ a network decomposition technique to examine their hierarchical structure. We provide consistent evidence that peripheral participants are critical in increasing the reach of protest messages and generating online content at levels that are comparable to core participants. Although committed minorities may constitute the heart of protest movements, our results suggest that their success in maximizing the number of online citizens exposed to protest messages depends, at least in part, on activating the critical periphery. Peripheral users are less active on a per capita basis, but their power lies in their numbers: their aggregate contribution to the spread of protest messages is comparable in magnitude to that of core participants. An analysis of two other datasets unrelated to mass protests strengthens our interpretation that core-periphery dynamics are characteristically important in the context of collective action events. Theoretical models of diffusion in social networks would benefit from increased attention to the role of peripheral nodes in the propagation of information and behavior.

Phosphatidylinositol phosphate kinases (PIPKs) are lipid kinases that generate phosphatidylinositol 4,5-bisphosphate (PI(4,5)P2), a critical lipid signaling molecule that regulates diverse cellular functions, including the activities of membrane channels and transporters. IRBIT (IP3R-binding protein released with inositol 1,4,5-trisphosphate) is a multifunctional protein that regulates diverse target proteins. Here, we report that IRBIT forms signaling complexes with members of the PIPK family. IRBIT bound to all PIPK isoforms in heterologous expression systems and specifically interacted with PIPK type Iα (PIPKIα) and type IIα (PIPKIIα) in mouse cerebellum. Site-directed mutagenesis revealed that two conserved catalytic aspartate residues of PIPKIα and PIPKIIα are involved in the interaction with IRBIT. Furthermore, phosphatidylinositol 4-phosphate, Mg2+, and/or ATP interfered with the interaction, suggesting that IRBIT interacts with catalytic cores of PIPKs. Mutations of phosphorylation sites in the serine-rich region of IRBIT affected the selectivity of its interaction with PIPKIα and PIPKIIα. The structural flexibility of the serine-rich region, located in the intrinsically disordered protein region, is assumed to underlie the mechanism of this interaction. Furthermore, in vitro binding experiments and immunocytochemistry suggest that IRBIT and PIPKIα interact with the Na+/HCO3- cotransporter NBCe1-B. These results suggest that IRBIT forms signaling complexes with PIPKIα and NBCe1-B, whose activity is regulated by PI(4,5)P2.

We recently identified sphingosine-1-phosphate (S1P) signaling and the cystic fibrosis transmembrane conductance regulator (CFTR) as prominent regulators of myogenic responsiveness in rodent resistance arteries. However, since rodent models frequently exhibit limitations with respect to human applicability, translation is necessary to validate the relevance of this signaling network for clinical application. We therefore investigated the significance of these regulatory elements in human mesenteric and skeletal muscle resistance arteries. Mesenteric and skeletal muscle resistance arteries were isolated from patient tissue specimens collected during colonic or cardiac bypass surgery. Pressure myography assessments confirmed endothelial integrity, as well as stable phenylephrine and myogenic responses. Both human mesenteric and skeletal muscle resistance arteries (i) express critical S1P signaling elements, (ii) constrict in response to S1P and (iii) lose myogenic responsiveness following S1P receptor antagonism (JTE013). However, while human mesenteric arteries express CFTR, human skeletal muscle resistance arteries do not express detectable levels of CFTR protein. Consequently, modulating CFTR activity enhances myogenic responsiveness only in human mesenteric resistance arteries. We conclude that human mesenteric and skeletal muscle resistance arteries are a reliable and consistent model for translational studies. We demonstrate that the core elements of an S1P-dependent signaling network translate to human mesenteric resistance arteries. Clear species and vascular bed variations are evident, reinforcing the critical need for further translational study.

Leber's hereditary optic neuropathy (LHON) is a maternally inherited blinding disorder, which in over 90% of cases is due to one of three primary mitochondrial DNA (mtDNA) point mutations (m.11778G>A, m.3460G>A and m.14484T>C, respectively in MT-ND4, MT-ND1 and MT-ND6 genes). However, the spectrum of mtDNA mutations causing the remaining 10% of cases is only partially and often poorly defined.In order to improve such a list of pathological variants, we completely sequenced the mitochondrial genomes of suspected LHON patients from Italy, France and Germany, lacking the three primary common mutations. Phylogenetic and conservation analyses were performed. Sixteen mitochondrial genomes were found to harbor at least one of the following nine rare LHON pathogenic mutations in genes MT-ND1 (m.3700G>A/p.A132T, m.3733G>A-C/p.E143K-Q, m.4171C>A/p.L289M), MT-ND4L (m.10663T>C/p.V65A) and MT-ND6 (m.14459G>A/p.A72V, m.14495A>G/p.M64I, m.14482C>A/p.L60S, and m.14568C>T/p.G36S). Phylogenetic analyses revealed that these substitutions were due to independent events on different haplogroups, whereas interspecies comparisons showed that they affected conserved amino acid residues or domains in the ND subunit genes of complex I.Our findings indicate that these nine substitutions are all primary LHON mutations. Therefore, despite their relative low frequency, they should be routinely tested for in all LHON patients lacking the three common mutations. Moreover, our sequence analysis confirms the major role of haplogroups J1c and J2b (over 35% in our probands versus 6% in the general population of Western Europe) and other putative synergistic mtDNA variants in LHON expression.

SHANK genes code for scaffold proteins located at the post-synaptic density of glutamatergic synapses. In neurons, SHANK2 and SHANK3 have a positive effect on the induction and maturation of dendritic spines, whereas SHANK1 induces the enlargement of spine heads. Mutations in SHANK genes have been associated with autism spectrum disorders (ASD), but their prevalence and clinical relevance remain to be determined. Here, we performed a new screen and a meta-analysis of SHANK copy-number and coding-sequence variants in ASD. Copy-number variants were analyzed in 5,657 patients and 19,163 controls, coding-sequence variants were ascertained in 760 to 2,147 patients and 492 to 1,090 controls (depending on the gene), and, individuals carrying de novo or truncating SHANK mutations underwent an extensive clinical investigation. Copy-number variants and truncating mutations in SHANK genes were present in ∼1% of patients with ASD: mutations in SHANK1 were rare (0.04%) and present in males with normal IQ and autism; mutations in SHANK2 were present in 0.17% of patients with ASD and mild intellectual disability; mutations in SHANK3 were present in 0.69% of patients with ASD and up to 2.12% of the cases with moderate to profound intellectual disability. In summary, mutations of the SHANK genes were detected in the whole spectrum of autism with a gradient of severity in cognitive impairment. Given the rare frequency of SHANK1 and SHANK2 deleterious mutations, the clinical relevance of these genes remains to be ascertained. In contrast, the frequency and the penetrance of SHANK3 mutations in individuals with ASD and intellectual disability-more than 1 in 50-warrant its consideration for mutation screening in clinical practice.

Negative examples - genes that are known not to carry out a given protein function - are rarely recorded in genome and proteome annotation databases, such as the Gene Ontology database. Negative examples are required, however, for several of the most powerful machine learning methods for integrative protein function prediction. Most protein function prediction efforts have relied on a variety of heuristics for the choice of negative examples. Determining the accuracy of methods for negative example prediction is itself a non-trivial task, given that the Open World Assumption as applied to gene annotations rules out many traditional validation metrics. We present a rigorous comparison of these heuristics, utilizing a temporal holdout, and a novel evaluation strategy for negative examples. We add to this comparison several algorithms adapted from Positive-Unlabeled learning scenarios in text-classification, which are the current state of the art methods for generating negative examples in low-density annotation contexts. Lastly, we present two novel algorithms of our own construction, one based on empirical conditional probability, and the other using topic modeling applied to genes and annotations. We demonstrate that our algorithms achieve significantly fewer incorrect negative example predictions than the current state of the art, using multiple benchmarks covering multiple organisms. Our methods may be applied to generate negative examples for any type of method that deals with protein function, and to this end we provide a database of negative examples in several well-studied organisms, for general use (The NoGO database, available at: bonneaulab.bio.nyu.edu/nogo.html).

Soil-transmitted helminths colonize more than 1.5 billion people worldwide, yet little is known about how they interact with bacterial communities in the gut microbiota. Differences in the gut microbiota between individuals living in developed and developing countries may be partly due to the presence of helminths, since they predominantly infect individuals from developing countries, such as the indigenous communities in Malaysia we examine in this work. We compared the composition and diversity of bacterial communities from the fecal microbiota of 51 people from two villages in Malaysia, of which 36 (70.6%) were infected by helminths. The 16S rRNA V4 region was sequenced at an average of nineteen thousand sequences per samples. Helminth-colonized individuals had greater species richness and number of observed OTUs with enrichment of Paraprevotellaceae, especially with Trichuris infection. We developed a new approach of combining centered log-ratio (clr) transformation for OTU relative abundances with sparse Partial Least Squares Discriminant Analysis (sPLS-DA) to enable more robust predictions of OTU interrelationships. These results suggest that helminths may have an impact on the diversity, bacterial community structure and function of the gut microbiota.

Only species belonging to the Fabid clade, limited to four classes and ten families of Angiosperms, are able to form nitrogen-fixing root nodule symbioses (RNS) with soil bacteria. This concerns plants of the legume family (Fabaceae) and Parasponia (Cannabaceae) associated with the Gram-negative proteobacteria collectively called rhizobia and actinorhizal plants associated with the Gram-positive actinomycetes of the genus Frankia. Calcium and calmodulin-dependent protein kinase (CCaMK) is a key component of the common signaling pathway leading to both rhizobial and arbuscular mycorrhizal symbioses (AM) and plays a central role in cross-signaling between root nodule organogenesis and infection processes. Here, we show that CCaMK is also needed for successful actinorhiza formation and interaction with AM fungi in the actinorhizal tree Casuarina glauca and is also able to restore both nodulation and AM symbioses in a Medicago truncatula ccamk mutant. Besides, we expressed auto-active CgCCaMK lacking the auto-inhibitory/CaM domain in two actinorhizal species: C. glauca (Casuarinaceae), which develops an intracellular infection pathway, and Discaria trinervis (Rhamnaceae) which is characterized by an ancestral intercellular infection mechanism. In both species, we found induction of nodulation independent of Frankia similar to response to the activation of CCaMK in the rhizobia-legume symbiosis and conclude that the regulation of actinorhiza organogenesis is conserved regardless of the infection mode. It has been suggested that rhizobial and actinorhizal symbioses originated from a common ancestor with several independent evolutionary origins. Our findings are consistent with the recruitment of a similar genetic pathway governing rhizobial and Frankia nodule organogenesis.

The Rosetta molecular modeling software package provides experimentally tested and rapidly evolving tools for the 3D structure prediction and high-resolution design of proteins, nucleic acids, and a growing number of non-natural polymers. Despite its free availability to academic users and improving documentation, use of Rosetta has largely remained confined to developers and their immediate collaborators due to the code's difficulty of use, the requirement for large computational resources, and the unavailability of servers for most of the Rosetta applications. Here, we present a unified web framework for Rosetta applications called ROSIE (Rosetta Online Server that Includes Everyone). ROSIE provides (a) a common user interface for Rosetta protocols, (b) a stable application programming interface for developers to add additional protocols, (c) a flexible back-end to allow leveraging of computer cluster resources shared by RosettaCommons member institutions, and (d) centralized administration by the RosettaCommons to ensure continuous maintenance. This paper describes the ROSIE server infrastructure, a step-by-step 'serverification' protocol for use by Rosetta developers, and the deployment of the first nine ROSIE applications by six separate developer teams: Docking, RNA de novo, ERRASER, Antibody, Sequence Tolerance, Supercharge, Beta peptide design, NCBB design, and VIP redesign. As illustrated by the number and diversity of these applications, ROSIE offers a general and speedy paradigm for serverification of Rosetta applications that incurs negligible cost to developers and lowers barriers to Rosetta use for the broader biological community. ROSIE is available at http://rosie.rosettacommons.org.

Peptidomimetics are classes of molecules that mimic structural and functional attributes of polypeptides. Peptidomimetic oligomers can frequently be synthesized using efficient solid phase synthesis procedures similar to peptide synthesis. Conformationally ordered peptidomimetic oligomers are finding broad applications for molecular recognition and for inhibiting protein-protein interactions. One critical limitation is the limited set of design tools for identifying oligomer sequences that can adopt desired conformations. Here, we present expansions to the ROSETTA platform that enable structure prediction and design of five non-peptidic oligomer scaffolds (noncanonical backbones), oligooxopiperazines, oligo-peptoids, [Formula: see text]-peptides, hydrogen bond surrogate helices and oligosaccharides. This work is complementary to prior additions to model noncanonical protein side chains in ROSETTA. The main purpose of our manuscript is to give a detailed description to current and future developers of how each of these noncanonical backbones was implemented. Furthermore, we provide a general outline for implementation of new backbone types not discussed here. To illustrate the utility of this approach, we describe the first tests of the ROSETTA molecular mechanics energy function in the context of oligooxopiperazines, using quantum mechanical calculations as comparison points, scanning through backbone and side chain torsion angles for a model peptidomimetic. Finally, as an example of a novel design application, we describe the automated design of an oligooxopiperazine that inhibits the p53-MDM2 protein-protein interaction. For the general biological and bioengineering community, several noncanonical backbones have been incorporated into web applications that allow users to freely and rapidly test the presented protocols (http://rosie.rosettacommons.org). This work helps address the peptidomimetic community's need for an automated and expandable modeling tool for noncanonical backbones.

The intracellular microenvironment is essential for the efficiency of photo-induced therapies, as short-lived reactive oxygen species generated must diffuse through their intracellular surrounding medium to reach their cellular target. Here, by combining measurements of local cytoplasmic dissipation and active trafficking, we found that photosensitizers activation induced small changes in surrounding viscosity but a massive decrease in diffusion. These effects are the signature of a return to thermodynamic equilibrium of the system after photo-activation and correlated with depolymerization of the microtubule network, as shown in a reconstituted system. These mechanical measurements were performed with two intracellular photosensitizing chlorins having similar quantum yield of singlet oxygen production but different intracellular localizations (cytoplasmic for mTHPC, endosomal for TPCS2a). These two agents demonstrated different intracellular impact.

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders with a complex inheritance pattern. While many rare variants in synaptic proteins have been identified in patients with ASD, little is known about their effects at the synapse and their interactions with other genetic variations. Here, following the discovery of two de novo SHANK2 deletions by the Autism Genome Project, we identified a novel 421 kb de novo SHANK2 deletion in a patient with autism. We then sequenced SHANK2 in 455 patients with ASD and 431 controls and integrated these results with those reported by Berkel et al. 2010 (n = 396 patients and n = 659 controls). We observed a significant enrichment of variants affecting conserved amino acids in 29 of 851 (3.4%) patients and in 16 of 1,090 (1.5%) controls (P = 0.004, OR = 2.37, 95% CI = 1.23-4.70). In neuronal cell cultures, the variants identified in patients were associated with a reduced synaptic density at dendrites compared to the variants only detected in controls (P = 0.0013). Interestingly, the three patients with de novo SHANK2 deletions also carried inherited CNVs at 15q11-q13 previously associated with neuropsychiatric disorders. In two cases, the nicotinic receptor CHRNA7 was duplicated and in one case the synaptic translation repressor CYFIP1 was deleted. These results strengthen the role of synaptic gene dysfunction in ASD but also highlight the presence of putative modifier genes, which is in keeping with the "multiple hit model" for ASD. A better knowledge of these genetic interactions will be necessary to understand the complex inheritance pattern of ASD.

Muscle potentially represents the most abundant source of autoantigens of the body and can be targeted by a variety of severe autoimmune diseases. Yet, the mechanisms of immunological tolerance toward muscle autoantigens remain mostly unknown. We investigated this issue in transgenic SM-Ova mice that express an ovalbumin (Ova) neo-autoantigen specifically in skeletal muscle. We previously reported that antigen specific CD4(+) T cell are immunologically ignorant to endogenous Ova in this model but can be stimulated upon immunization. In contrast, Ova-specific CD8(+) T cells were suspected to be either unresponsive to Ova challenge or functionally defective. We now extend our investigations on the mechanisms governing CD8(+) tolerance in SM-Ova mice. We show herein that Ova-specific CD8(+) T cells are not detected upon challenge with strongly immunogenic Ova vaccines even after depletion of regulatory T cells. Ova-specific CD8(+) T cells from OT-I mice adoptively transferred to SM-Ova mice started to proliferate in vivo, acquired CD69 and PD-1 but subsequently down-regulated Bcl-2 and disappeared from the periphery, suggesting a mechanism of peripheral deletion. Peripheral deletion of endogenous Ova-specific cells was formally demonstrated in chimeric SM-Ova mice engrafted with bone marrow cells containing T cell precursors from OT-I TCR-transgenic mice. Thus, the present findings demonstrate that immunological tolerance to muscle autoantigens involves peripheral deletion of autoreactive CD8(+) T cells.

Gene expression profiling of the blood cell response induced early after vaccination has previously been demonstrated to predict the immunogenicity of vaccines. In this study, we evaluated whether the analysis of the gene expression profile of skin-migrated dendritic cells (DCs) could be informative for the in vitro prediction of immunogenicity of vaccine, using canine adenovirus serotype 2 (CAV2) as vaccine vector. CAV2 has been shown to induce immunity to transgenes in several species including sheep and is an interesting alternative to human adenovirus-based vectors, based on the safety records of the parental strain in dogs and the lack of pre-existing immunity in non-host species. Skin-migrated DCs were collected from pseudo-afferent lymph in sheep. Both the CD11b(+) -type and CD103(+) -type skin-migrated DCs were transduced by CAV2. An analysis of the global gene response to CAV2 in the two skin DC subsets showed that the gene response in CD11b(+) -type DCs was far higher and broader than in the CD103(+) -type DCs. A newly released integrative analytic tool from Ingenuity systems revealed that the CAV2-modulated genes in the CD11b(+) -type DCs clustered in several activated immunogenicity-related functions, such as immune response, immune cell trafficking and inflammation. Thus gene profiling in skin-migrated DC in vitro indicates that the CD11b(+) DC type is more responsive to CAV2 than the CD103(+) DC type, and provides valuable information to help in evaluating and possibly improving viral vector vaccine effectiveness.

Noncanonical amino acids (NCAAs) can be used in a variety of protein design contexts. For example, they can be used in place of the canonical amino acids (CAAs) to improve the biophysical properties of peptides that target protein interfaces. We describe the incorporation of 114 NCAAs into the protein-modeling suite Rosetta. We describe our methods for building backbone dependent rotamer libraries and the parameterization and construction of a scoring function that can be used to score NCAA containing peptides and proteins. We validate these additions to Rosetta and our NCAA-rotamer libraries by showing that we can improve the binding of a calpastatin derived peptides to calpain-1 by substituting NCAAs for native amino acids using Rosetta. Rosetta (executables and source), auxiliary scripts and code, and documentation can be found at (http://www.rosettacommons.org/).

The increasing abundance of large-scale, high-throughput datasets for many closely related organisms provides opportunities for comparative analysis via the simultaneous biclustering of datasets from multiple species. These analyses require a reformulation of how to organize multi-species datasets and visualize comparative genomics data analyses results. Recently, we developed a method, multi-species cMonkey, which integrates heterogeneous high-throughput datatypes from multiple species to identify conserved regulatory modules. Here we present an integrated data visualization system, built upon the Gaggle, enabling exploration of our method's results (available at http://meatwad.bio.nyu.edu/cmmr.html). The system can also be used to explore other comparative genomics datasets and outputs from other data analysis procedures - results from other multiple-species clustering programs or from independent clustering of different single-species datasets. We provide an example use of our system for two bacteria, Escherichia coli and Salmonella Typhimurium. We illustrate the use of our system by exploring conserved biclusters involved in nitrogen metabolism, uncovering a putative function for yjjI, a currently uncharacterized gene that we predict to be involved in nitrogen assimilation.

Increased plasticity, migratory and immunosuppressive abilities characterize mesenchymal stromal cells (MSC) which enable them to be active participants in the development of hypoxic solid tumours. Our understanding of the oncogenic adaptation of MSC to hypoxia however lacks the identification and characterization of specific biomarkers. In this study, we assessed the hypoxic regulation of 3BP2/SH3BP2 (Abl SH3-binding protein 2), an immune response adaptor/scaffold protein which regulates leukocyte differentiation and motility. Gene silencing of 3BP2 abrogated MSC migration in response to hypoxic cues and generation of MSC stably expressing the transcription factor hypoxia inducible factor 1alpha (HIF-1α) resulted in increased endogenous 3BP2 expression as well as cell migration. Analysis of the 3BP2 promoter sequence revealed only one potential HIF-1α binding site within the human but none in the murine sequence. An alternate early signalling cascade that regulated 3BP2 expression was found to involve membrane type-1 matrix metalloproteinase (MT1-MMP) transcriptional regulation which gene silencing abrogated 3BP2 expression in response to hypoxia. Collectively, we provide evidence for a concerted HIF-1α/MT1-MMP signalling axis that explains the induction of adaptor protein 3BP2 and which may link protein binding partners together and stimulate oncogenic MSC migration. These mechanistic observations support the potential for malignant transformation of MSC within hypoxic tumour stroma and may contribute to evasion of the immune system by a tumour.

Temperature-sensitive (ts) mutations are mutations that exhibit a mutant phenotype at high or low temperatures and a wild-type phenotype at normal temperature. Temperature-sensitive mutants are valuable tools for geneticists, particularly in the study of essential genes. However, finding ts mutations typically relies on generating and screening many thousands of mutations, which is an expensive and labor-intensive process. Here we describe an in silico method that uses Rosetta and machine learning techniques to predict a highly accurate "top 5" list of ts mutations given the structure of a protein of interest. Rosetta is a protein structure prediction and design code, used here to model and score how proteins accommodate point mutations with side-chain and backbone movements. We show that integrating Rosetta relax-derived features with sequence-based features results in accurate temperature-sensitive mutation predictions.

Transcutaneous delivery of vaccines to specific skin dendritic cells (DC) subsets is foreseen as a promising strategy to induce strong and specific types of immune responses such as tolerance, cytotoxicity or humoral immunity. Because of striking histological similarities between human and pig skin, pig is recognized as the most suitable model to study the cutaneous delivery of medicine. Therefore improving the knowledge on swine skin DC subsets would be highly valuable to the skin vaccine field. In this study, we showed that pig skin DC comprise the classical epidermal langerhans cells (LC) and dermal DC (DDC) that could be divided in 3 subsets according to their phenotypes: (1) the CD163(neg)/CD172a(neg), (2) the CD163(high)CD172a(pos) and (3) the CD163(low)CD172a(pos) DDC. These subtypes have the capacity to migrate from skin to lymph node since we detected them in pseudo-afferent lymph. Extensive phenotyping with a set of markers suggested that the CD163(high) DDC resemble the antibody response-inducing human skin DC/macrophages whereas the CD163(neg)CD172(low) DDC share properties with the CD8(+) T cell response-inducing murine skin CD103(pos) DC. This work, by showing similarities between human, mouse and swine skin DC, establishes pig as a model of choice for the development of transcutaneous immunisation strategies targeting DC.

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/.

Many current works aiming to learn regulatory networks from systems biology data must balance model complexity with respect to data availability and quality. Methods that learn regulatory associations based on unit-less metrics, such as Mutual Information, are attractive in that they scale well and reduce the number of free parameters (model complexity) per interaction to a minimum. In contrast, methods for learning regulatory networks based on explicit dynamical models are more complex and scale less gracefully, but are attractive as they may allow direct prediction of transcriptional dynamics and resolve the directionality of many regulatory interactions.We aim to investigate whether scalable information based methods (like the Context Likelihood of Relatedness method) and more explicit dynamical models (like Inferelator 1.0) prove synergistic when combined. We test a pipeline where a novel modification of the Context Likelihood of Relatedness (mixed-CLR, modified to use time series data) is first used to define likely regulatory interactions and then Inferelator 1.0 is used for final model selection and to build an explicit dynamical model.Our method ranked 2nd out of 22 in the DREAM3 100-gene in silico networks challenge. Mixed-CLR and Inferelator 1.0 are complementary, demonstrating a large performance gain relative to any single tested method, with precision being especially high at low recall values. Partitioning the provided data set into four groups (knock-down, knock-out, time-series, and combined) revealed that using comprehensive knock-out data alone provides optimal performance. Inferelator 1.0 proved particularly powerful at resolving the directionality of regulatory interactions, i.e. "who regulates who" (approximately of identified true positives were correctly resolved). Performance drops for high in-degree genes, i.e. as the number of regulators per target gene increases, but not with out-degree, i.e. performance is not affected by the presence of regulatory hubs.

By sensing changes in one or few environmental factors biological systems can anticipate future changes in multiple factors over a wide range of time scales (daily to seasonal). This anticipatory behavior is important to the fitness of diverse species, and in context of the diurnal cycle it is overall typical of eukaryotes and some photoautotrophic bacteria but is yet to be observed in archaea. Here, we report the first observation of light-dark (LD)-entrained diurnal oscillatory transcription in up to 12% of all genes of a halophilic archaeon Halobacterium salinarum NRC-1. Significantly, the diurnally entrained transcription was observed under constant darkness after removal of the LD stimulus (free-running rhythms). The memory of diurnal entrainment was also associated with the synchronization of oxic and anoxic physiologies to the LD cycle. Our results suggest that under nutrient limited conditions halophilic archaea take advantage of the causal influence of sunlight (via temperature) on O(2) diffusivity in a closed hypersaline environment to streamline their physiology and operate oxically during nighttime and anoxically during daytime.

Saccharomyces cerevisiae is one of the best-studied model organisms, yet the three-dimensional structure and molecular function of many yeast proteins remain unknown. Yeast proteins were parsed into 14,934 domains, and those lacking sequence similarity to proteins of known structure were folded using the Rosetta de novo structure prediction method on the World Community Grid. This structural data was integrated with process, component, and function annotations from the Saccharomyces Genome Database to assign yeast protein domains to SCOP superfamilies using a simple Bayesian approach. We have predicted the structure of 3,338 putative domains and assigned SCOP superfamily annotations to 581 of them. We have also assigned structural annotations to 7,094 predicted domains based on fold recognition and homology modeling methods. The domain predictions and structural information are available in an online database at http://rd.plos.org/10.1371_journal.pbio.0050076_01.