Structure and function of a flavin-dependent S-monooxygenase from garlic (Allium sativum).

Allicin is a component of the characteristic smell and flavor of garlic (Allium sativum). A flavin-containing monooxygenase (FMO) produced by A. sativum (AsFMO) was previously proposed to oxidize S-allyl-l-cysteine (SAC) to alliin, an allicin precursor. Here, we present a kinetic and structural characterization of AsFMO that suggests a possible contradiction to this proposal. Results of steady-state kinetic analyses revealed that AsFMO exhibited negligible activity with SAC; however, the enzyme was highly active with l-cysteine, N-acetyl-l-cysteine, and allyl mercaptan. We found that allyl mercaptan with NADPH was the preferred substrate-cofactor combination. Rapid-reaction kinetic analyses showed that NADPH binds tightly (KD of ∼2 µm) to AsFMO and that the hydride transfer occurs with pro-R stereospecificity. We detected the formation of a long-wavelength band when AsFMO was reduced by NADPH, probably representing the formation of a charge-transfer complex. In the absence of substrate, the reduced enzyme, in complex with NADP+, reacted with oxygen and formed an intermediate with a spectrum characteristic of C4a-hydroperoxyflavin, which decays several orders of magnitude more slowly than the kcat The presence of substrate enhanced C4a-hydroperoxyflavin formation and, upon hydroxylation, oxidation occurred with a rate constant similar to the kcat The structure of AsFMO complexed with FAD at 2.08-Å resolution features two domains for binding of FAD and NADPH, representative of class B flavin monooxygenases. These biochemical and structural results are consistent with AsFMO being an S-monooxygenase involved in allicin biosynthesis through direct formation of sulfenic acid and not SAC oxidation.

Soft windowing application to improve analysis of high-throughput phenotyping data.

MOTIVATION: High-throughput phenomic projects generate complex data from small treatment and large control groups that increase the power of the analyses but introduce variation over time. A method is needed to utlize a set of temporally local controls that maximizes analytic power while minimizing noise from unspecified environmental factors. RESULTS: Here we introduce 'soft windowing', a methodological approach that selects a window of time that includes the most appropriate controls for analysis. Using phenotype data from the International Mouse Phenotyping Consortium (IMPC), adaptive windows were applied such that control data collected proximally to mutants were assigned the maximal weight, while data collected earlier or later had less weight. We applied this method to IMPC data and compared the results with those obtained from a standard non-windowed approach. Validation was performed using a resampling approach in which we demonstrate a 10% reduction of false positives from 2.5 million analyses. We applied the method to our production analysis pipeline that establishes genotype-phenotype associations by comparing mutant versus control data. We report an increase of 30% in significant P-values, as well as linkage to 106 versus 99 disease models via phenotype overlap with the soft-windowed and non-windowed approaches, respectively, from a set of 2082 mutant mouse lines. Our method is generalizable and can benefit large-scale human phenomic projects such as the UK Biobank and the All of Us resources. AVAILABILITY AND IMPLEMENTATION: The method is freely available in the R package SmoothWin, available on CRAN http://CRAN.R-project.org/package=SmoothWin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Principles and application of LIMS in mouse clinics.

Large-scale systemic mouse phenotyping, as performed by mouse clinics for more than a decade, requires thousands of mice from a multitude of different mutant lines to be bred, individually tracked and subjected to phenotyping procedures according to a standardised schedule. All these efforts are typically organised in overlapping projects, running in parallel. In terms of logistics, data capture, data analysis, result visualisation and reporting, new challenges have emerged from such projects. These challenges could hardly be met with traditional methods such as pen & paper colony management, spreadsheet-based data management and manual data analysis. Hence, different Laboratory Information Management Systems (LIMS) have been developed in mouse clinics to facilitate or even enable mouse and data management in the described order of magnitude. This review shows that general principles of LIMS can be empirically deduced from LIMS used by different mouse clinics, although these have evolved differently. Supported by LIMS descriptions and lessons learned from seven mouse clinics, this review also shows that the unique LIMS environment in a particular facility strongly influences strategic LIMS decisions and LIMS development. As a major conclusion, this review states that there is no universal LIMS for the mouse research domain that fits all requirements. Still, empirically deduced general LIMS principles can serve as a master decision support template, which is provided as a hands-on tool for mouse research facilities looking for a LIMS.

Molecular classification of infiltrating breast cancer: toward personalized therapy.

Breast cancer is a heterogeneous disease, which comprises several molecular and genetic subtypes, each with characteristic clinicobiologic behavior and imaging patterns. Traditional classification of breast cancer is based on the histopathologic features but offers limited prognostic value. Novel molecular characterization of breast cancer with cellular markers has allowed a new classification that offers prognostic value, with predictive categories of disease aggressiveness. These molecular signatures also open the door to personalized therapeutic options, with new receptor-targeted therapies. For example, invasive cancer subtypes such as the luminal A and B subtypes show better prognosis and response to hormone receptor-targeted therapies compared with the triple-negative subtypes; on the other hand, triple-negative tumors respond better than luminal tumors to chemotherapy. Tumors that display amplification of the oncogene ERBB2 (also known as the HER2/neu oncogene) respond to drugs directed against this oncogene, such as trastuzumab. The imaging aspects of tumors correlate with molecular subgroups, as well as other pathologic features such as nuclear grade. Smooth tumor margins at mammography may be suggestive of a triple-negative breast cancer, and a human epidermal growth factor receptor 2 (HER2)-positive tumor is characteristically a spiculated mass with calcifications. Low-grade ductal carcinoma in situ (DCIS) is better detected with mammography, although magnetic resonance (MR) imaging may allow better characterization of high-grade DCIS. MR imaging diffusion sequences show higher values for the apparent diffusion coefficient for triple-negative and HER2-positive subtypes, compared with luminal A and B tumors. MR imaging is also a useful tool in the prediction of tumor response after chemotherapy, especially for triple-negative and HER2-positive subtypes.

Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project.

Two large-scale phenotyping efforts, the European Mouse Disease Clinic (EUMODIC) and the Wellcome Trust Sanger Institute Mouse Genetics Project (SANGER-MGP), started during the late 2000s with the aim to deliver a comprehensive assessment of phenotypes or to screen for robust indicators of diseases in mouse mutants. They both took advantage of available mouse mutant lines but predominantly of the embryonic stem (ES) cells resources derived from the European Conditional Mouse Mutagenesis programme (EUCOMM) and the Knockout Mouse Project (KOMP) to produce and study 799 mouse models that were systematically analysed with a comprehensive set of physiological and behavioural paradigms. They captured more than 400 variables and an additional panel of metadata describing the conditions of the tests. All the data are now available through EuroPhenome database (www.europhenome.org) and the WTSI mouse portal (http://www.sanger.ac.uk/mouseportal/), and the corresponding mouse lines are available through the European Mouse Mutant Archive (EMMA), the International Knockout Mouse Consortium (IKMC), or the Knockout Mouse Project (KOMP) Repository. Overall conclusions from both studies converged, with at least one phenotype scored in at least 80% of the mutant lines. In addition, 57% of the lines were viable, 13% subviable, 30% embryonic lethal, and 7% displayed fertility impairments. These efforts provide an important underpinning for a future global programme that will undertake the complete functional annotation of the mammalian genome in the mouse model.

EuroPhenome: a repository for high-throughput mouse phenotyping data.

The broad aim of biomedical science in the postgenomic era is to link genomic and phenotype information to allow deeper understanding of the processes leading from genomic changes to altered phenotype and disease. The EuroPhenome project (http://www.EuroPhenome.org) is a comprehensive resource for raw and annotated high-throughput phenotyping data arising from projects such as EUMODIC. EUMODIC is gathering data from the EMPReSSslim pipeline (http://www.empress.har.mrc.ac.uk/) which is performed on inbred mouse strains and knock-out lines arising from the EUCOMM project. The EuroPhenome interface allows the user to access the data via the phenotype or genotype. It also allows the user to access the data in a variety of ways, including graphical display, statistical analysis and access to the raw data via web services. The raw phenotyping data captured in EuroPhenome is annotated by an annotation pipeline which automatically identifies statistically different mutants from the appropriate baseline and assigns ontology terms for that specific test. Mutant phenotypes can be quickly identified using two EuroPhenome tools: PhenoMap, a graphical representation of statistically relevant phenotypes, and mining for a mutant using ontology terms. To assist with data definition and cross-database comparisons, phenotype data is annotated using combinations of terms from biological ontologies.

Ice nucleation in a Gram-positive bacterium isolated from precipitation depends on a polyketide synthase and non-ribosomal peptide synthetase.

Earth's radiation budget and frequency and intensity of precipitation are influenced by aerosols with ice nucleation activity (INA), i.e., particles that catalyze the formation of ice. Some bacteria, fungi, and pollen are among the most efficient ice nucleators but the molecular basis of INA is poorly understood in most of them. Lysinibacillus parviboronicapiens (Lp) was previously identified as the first Gram-positive bacterium with INA. INA of Lp is associated with a secreted, nanometer-sized, non-proteinaceous macromolecule or particle. Here a combination of comparative genomics, transcriptomics, and a mutant screen showed that INA in Lp depends on a type I iterative polyketide synthase and a non-ribosomal peptide synthetase (PKS-NRPS). Differential filtration in combination with gradient ultracentrifugation revealed that the product of the PKS-NRPS is associated with secreted particles of a density typical of extracellular vesicles and electron microscopy showed that these particles consist in "pearl chain"-like structures not resembling any other known bacterial structures. These findings expand our knowledge of biological INA, may be a model for INA in other organisms for which the molecular basis of INA is unknown, and present another step towards unraveling the role of microbes in atmospheric processes.

Large-Scale Functional Assessment of Genes Involved in Rare Diseases with Intellectual Disabilities Unravels Unique Developmental and Behaviour Profiles in Mouse Models.

Major progress has been made over the last decade in identifying novel genes involved in neurodevelopmental disorders, although the task of elucidating their corresponding molecular and pathophysiological mechanisms, which are an essential prerequisite for developing therapies, has fallen far behind. We selected 45 genes for intellectual disabilities to generate and characterize mouse models. Thirty-nine of them were based on the frequency of pathogenic variants in patients and literature reports, with several corresponding to de novo variants, and six other candidate genes. We used an extensive screen covering the development and adult stages, focusing specifically on behaviour and cognition to assess a wide range of functions and their pathologies, ranging from basic neurological reflexes to cognitive abilities. A heatmap of behaviour phenotypes was established, together with the results of selected mutants. Overall, three main classes of mutant lines were identified based on activity phenotypes, with which other motor or cognitive deficits were associated. These data showed the heterogeneity of phenotypes between mutation types, recapitulating several human features, and emphasizing the importance of such systematic approaches for both deciphering genetic etiological causes of ID and autism spectrum disorders, and for building appropriate therapeutic strategies.

ProMetIS, deep phenotyping of mouse models by combined proteomics and metabolomics analysis.

Genes are pleiotropic and getting a better knowledge of their function requires a comprehensive characterization of their mutants. Here, we generated multi-level data combining phenomic, proteomic and metabolomic acquisitions from plasma and liver tissues of two C57BL/6 N mouse models lacking the Lat (linker for activation of T cells) and the Mx2 (MX dynamin-like GTPase 2) genes, respectively. Our dataset consists of 9 assays (1 preclinical, 2 proteomics and 6 metabolomics) generated with a fully non-targeted and standardized approach. The data and processing code are publicly available in the ProMetIS R package to ensure accessibility, interoperability, and reusability. The dataset thus provides unique molecular information about the physiological role of the Lat and Mx2 genes. Furthermore, the protocols described herein can be easily extended to a larger number of individuals and tissues. Finally, this resource will be of great interest to develop new bioinformatic and biostatistic methods for multi-omics data integration.

High-throughput DNA Extraction and Genotyping of 3dpf Zebrafish Larvae by Fin Clipping.

Zebrafish (Danio rerio) possess orthologues for 84% of the genes known to be associated with human diseases. In addition, these animals have a short generation time, are easy to handle, display a high reproductive rate, low cost, and are easily amenable to genetic manipulations by microinjection of DNA in embryos. Recent advances in gene editing tools are enabling precise introduction of mutations and transgenes in zebrafish. Disease modeling in zebrafish often leads to larval phenotypes and early death which can be challenging to interpret if genotypes are unknown. This early identification of genotypes is also needed in experiments requiring sample pooling, such as in gene expression or mass spectrometry studies. However, extensive genotypic screening is limited by traditional methods, which in most labs are performed only on adult zebrafish or in postmortem larvae. We addressed this problem by adapting a method for the isolation of PCR-ready genomic DNA from live zebrafish larvae that can be achieved as early as 72 h post-fertilization (hpf). This time and cost-effective technique, improved from a previously published genotyping protocol, allows the identification of genotypes from microscopic fin biopsies. The fins quickly regenerate as the larvae develop. Researchers are then able to select and raise the desired genotypes to adulthood by utilizing this high-throughput PCR-based genotyping procedure.

Mouse Phenotype Database Integration Consortium: integration [corrected] of mouse phenome data resources.

Understanding the functions encoded in the mouse genome will be central to an understanding of the genetic basis of human disease. To achieve this it will be essential to be able to characterize the phenotypic consequences of variation and alterations in individual genes. Data on the phenotypes of mouse strains are currently held in a number of different forms (detailed descriptions of mouse lines, first-line phenotyping data on novel mutations, data on the normal features of inbred lines) at many sites worldwide. For the most efficient use of these data sets, we have initiated a process to develop standards for the description of phenotypes (using ontologies) and file formats for the description of phenotyping protocols and phenotype data sets. This process is ongoing and needs to be supported by the wider mouse genetics and phenotyping communities to succeed. We invite interested parties to contact us as we develop this process further.