Generating mice with targeted mutations.

Journal ArticleMutational analysis is one of the most informative approaches available for the study of complex biological processes. It has been particularly successful in the analysis of the biology of bacteria, yeast, the nematode worm Caenorhabditis elegans and the fruit fly Drosophila melanogaster. Extension of this approach to the mouse, through informative, was far less successful relative to what has been achieved with these simpler model organisms. This is because it is not numerically practical in mice to use random mutagenesis to isolate mutations that affect a specified biological process of interest. Nonetheless, biological phenomena such as a sophisticated immune response, cancer, vascular disease or higher-order cognitive function, to mention just a few, must analyzed in organisms that show such phenomena, and for this reason geneticists and other researchers have turned to the mouse. Gene targeting, the means for creating mice with designed mutations in almost any gene, was developed as an alternative to the impractical use of random mutgenesis for pursing genetic analysis in the mouse. Now gene targeting has advanced the genomic manipulations possible in mice to a level that can be matched only in far simple organisms such as bacteria and yeast

Tomato protoplast DNA transformation: physical linkage and recombination of exogenous DNA sequences

Tomato protoplasts have been transformed with plasmid DNA's, containing a chimeric kanamycin resistance gene and putative tomato origins of replication. A calcium phosphate-DNA mediated transformation procedure was employed in combination with either polyethylene glycol or polyvinyl alcohol. There were no indications that the tomato DNA inserts conferred autonomous replication on the plasmids. Instead, Southern blot hybridization analysis of seven kanamycin resistant calli revealed the presence of at least one kanamycin resistance locus per transformant integrated in the tomato nuclear DNA. Generally one to three truncated plasmid copies were found integrated into the tomato nuclear DNA, often physically linked to each other. For one transformant we have been able to use the bacterial ampicillin resistance marker of the vector plasmid pUC9 to 'rescue' a recombinant plasmid from the tomato genome. Analysis of the foreign sequences included in the rescued plasmid showed that integration had occurred in a non-repetitive DNA region. Calf-thymus DNA, used as a carrier in transformation procedure, was found to be covalently linked to plasmid DNA sequences in the genomic DNA of one transformant. A model is presented describing the fate of exogenously added DNA during the transformation of a plant cell. The results are discussed in reference to the possibility of isolating DNA sequences responsible for autonomous replication in tomato.

Reconstruction of Genome-Scale Active Metabolic Networks for 69 Human Cell Types and 16 Cancer Types Using INIT

Development of high throughput analytical methods has given physicians the potential access to extensive and patient-specific data sets, such as gene sequences, gene expression profiles or metabolite footprints. This opens for a new approach in health care, which is both personalized and based on system-level analysis. Genome-scale metabolic networks provide a mechanistic description of the relationships between different genes, which is valuable for the analysis and interpretation of large experimental data-sets. Here we describe the generation of genome-scale active metabolic networks for 69 different cell types and 16 cancer types using the INIT (Integrative Network Inference for Tissues) algorithm. The INIT algorithm uses cell type specific information about protein abundances contained in the Human Proteome Atlas as the main source of evidence. The generated models constitute the first step towards establishing a Human Metabolic Atlas, which will be a comprehensive description (accessible online) of the metabolism of different human cell types, and will allow for tissue-level and organism-level simulations in order to achieve a better understanding of complex diseases. A comparative analysis between the active metabolic networks of cancer types and healthy cell types allowed for identification of cancer-specific metabolic features that constitute generic potential drug targets for cancer treatment

Stoichiometric representation of geneproteinreaction associations leverages constraint-based analysis from reaction to gene-level phenotype prediction

Genome-scale metabolic reconstructions are currently available for hundreds of organisms. Constraint-based modeling enables the analysis of the phenotypic landscape of these organisms, predicting the response to genetic and environmental perturbations. However, since constraint-based models can only describe the metabolic phenotype at the reaction level, understanding the mechanistic link between genotype and phenotype is still hampered by the complexity of gene-protein-reaction associations. We implement a model transformation that enables constraint-based methods to be applied at the gene level by explicitly accounting for the individual fluxes of enzymes (and subunits) encoded by each gene. We show how this can be applied to different kinds of constraint-based analysis: flux distribution prediction, gene essentiality analysis, random flux sampling, elementary mode analysis, transcriptomics data integration, and rational strain design. In each case we demonstrate how this approach can lead to improved phenotype predictions and a deeper understanding of the genotype-to-phenotype link. In particular, we show that a large fraction of reaction-based designs obtained by current strain design methods are not actually feasible, and show how our approach allows using the same methods to obtain feasible gene-based designs. We also show, by extensive comparison with experimental 13C-flux data, how simple reformulations of different simulation methods with gene-wise objective functions result in improved prediction accuracy. The model transformation proposed in this work enables existing constraint-based methods to be used at the gene level without modification. This automatically leverages phenotype analysis from reaction to gene level, improving the biological insight that can be obtained from genome-scale models.DM was supported by the Portuguese Foundationfor Science and Technologythrough a post-doc fellowship (ref: SFRH/BPD/111519/ 2015). This study was supported by the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic fundingof UID/BIO/04469/2013 unitand COMPETE2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145FEDER-000004) fundedby EuropeanRegional Development Fund under the scope of Norte2020Programa Operacional Regional do Norte. This project has received fundingfrom the European Union’s Horizon 2020 research and innovation programme under grant agreementNo 686070. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript