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Induced point mutations are important genetic resources for their ability to create hypo- and hypermorphic alleles that are useful for understanding gene functions and breeding. However, such mutant populations have only been developed for a few temperate maize varieties, mainly B73 and W22, yet no tropical maize inbred lines have been mutagenized and made available to the public to date. We developed a novel Ethyl Methanesulfonate (EMS) induced mutation resource in maize comprising 2050 independent M2 mutant families in the elite tropical maize inbred ML10. By phenotypic screening, we showed that this population is of comparable quality with other mutagenized populations in maize. To illustrate the usefulness of this population for gene discovery, we performed rapid mapping-by-sequencing to clone a fasciated-ear mutant and identify a causal promoter deletion in ZmCLE7 (CLE7). Our mapping procedure does not require crossing to an unrelated parent, thus is suitable for mapping subtle traits and ones affected by heterosis. This first EMS population in tropical maize is expected to be very useful for the maize research community. Also, the EMS mutagenesis and rapid mapping-by-sequencing pipeline described here illustrate the power of performing forward genetics in diverse maize germplasms of choice, which can lead to novel gene discovery due to divergent genetic backgrounds.
The P22 tailspike endorhamnosidase confers the high specificity of bacteriophage P22 for some serogroups of Salmonella differing only slightly in their O-antigen polysaccharide. We used several biophysical methods to study the binding and hydrolysis of O-antigen fragments of different lengths by P22 tailspike protein. O-Antigen saccharides of defined length labeled with fluorophors could be purified with higher resolution than previously possible. Small amounts of naturally occurring variations of 0antigen fragments missing the nonreducing terminal galactose could be used to determine the contribution of this part to the free energy of binding to be similar to 7 kJ/mol. We were able to show via several independent lines of evidence that an unproductive binding mode is highly favored in binding over all other possible binding modes leading to hydrolysis. This is true even under circumstances under which the O-antigen fragment is long enough to be cleaved efficiently by the enzyme. The high-affinity unproductive binding mode results in a strong self-competitive inhibition in addition to product inhibition observed for this system. Self-competitive inhibition is observed for all substrates that have a free reducing end rhamnose. Naturally occurring O-antigen, while still attached to the bacterial outer membrane, does not have a free reducing end and therefore does not perform self-competitive inhibition.
Polymeric devices capable of releasing submicron particles (subMP) on demand are highly desirable for controlled release systems, sensors, and smart surfaces. Here, a temperature-memory polymer sheet with a programmable smooth surface served as matrix to embed and release polystyrene subMP controlled by particle size and temperature. subMPs embedding at 80 degrees C can be released sequentially according to their size (diameter D of 200 nm, 500 nm, 1 mu m) when heated. The differences in their embedding extent are determined by the various subMPs sizes and result in their distinct release temperatures. Microparticles of the same size (D approximate to 1 mu m) incorporated in films at different programming temperatures T-p (50, 65, and 80 degrees C) lead to a sequential release based on the temperature-memory effect. The change of apparent height over the film surface is quantified using atomic force microscopy and the realization of sequential release is proven by confocal laser scanning microscopy. The demonstration and quantification of on demand subMP release are of technological impact for assembly, particle sorting, and release technologies in microtechnology, catalysis, and controlled release.
Understanding the complexity of metabolic networks has implications for manipulation of their functions. The complexity of metabolic networks can be characterized by identifying multireaction dependencies that are challenging to determine due to the sheer number of combinations to consider. Here, we propose the concept of concordant complexes that captures multireaction dependencies and can be efficiently determined from the algebraic structure and operational constraints of metabolic networks. The concordant complexes imply the existence of concordance modules based on which the apparent complexity of 12 metabolic networks of organisms from all kingdoms of life can be reduced by at least 78%. A comparative analysis against an ensemble of randomized metabolic networks shows that the metabolic network of Escherichia coli contains fewer concordance modules and is, therefore, more tightly coordinated than expected by chance. Together, our findings demonstrate that metabolic networks are considerably simpler than what can be perceived from their structure alone.
Following the extinction of dinosaurs, the great adaptive radiation of mammals occurred, giving rise to an astonishing ecological and phenotypic diversity of mammalian species. Even closely related species often inhabit vastly different habitats, where they encounter diverse environmental challenges and are exposed to different evolutionary pressures. As a response, mammals evolved various adaptive phenotypes over time, such as morphological, physiological and behavioural ones. Mammalian genomes vary in their content and structure and this variation represents the molecular mechanism for the long-term evolution of phenotypic variation. However, understanding this molecular basis of adaptive phenotypic variation is usually not straightforward.
The recent development of sequencing technologies and bioinformatics tools has enabled a better insight into mammalian genomes. Through these advances, it was acknowledged that mammalian genomes differ more, both within and between species, as a consequence of structural variation compared to single-nucleotide differences. Structural variant types investigated in this thesis - such as deletion, duplication, inversion and insertion, represent a change in the structure of the genome, impacting the size, copy number, orientation and content of DNA sequences. Unlike short variants, structural variants can span multiple genes. They can alter gene dosage, and cause notable gene expression differences and subsequently phenotypic differences. Thus, they can lead to a more dramatic effect on the fitness (reproductive success) of individuals, local adaptation of populations and speciation.
In this thesis, I investigated and evaluated the potential functional effect of structural variations on the genomes of mustelid species. To detect the genomic regions associated with phenotypic variation I assembled the first reference genome of the tayra (Eira barbara) relying on linked-read sequencing technology to achieve a high level of genome completeness important for reliable structural variant discovery. I then set up a bioinformatics pipeline to conduct a comparative genomic analysis and explore variation between mustelid species living in different environments. I found numerous genes associated with species-specific phenotypes related to diet, body condition and reproduction among others, to be impacted by structural variants.
Furthermore, I investigated the effects of artificial selection on structural variants in mice selected for high fertility, increased body mass and high endurance. Through selective breeding of each mouse line, the desired phenotypes have spread within these populations, while maintaining structural variants specific to each line. In comparison to the control line, the litter size has doubled in the fertility lines, individuals in the high body mass lines have become considerably larger, and mice selected for treadmill performance covered substantially more distance. Structural variants were found in higher numbers in these trait-selected lines than in the control line when compared to the mouse reference genome. Moreover, we have found twice as many structural variants spanning protein-coding genes (specific to each line) in trait-selected lines. Several of these variants affect genes associated with selected phenotypic traits. These results imply that structural variation does indeed contribute to the evolution of the selected phenotypes and is heritable.
Finally, I suggest a set of critical metrics of genomic data that should be considered for a stringent structural variation analysis as comparative genomic studies strongly rely on the contiguity and completeness of genome assemblies. Because most of the available data used to represent reference genomes of mammalian species is generated using short-read sequencing technologies, we may have incomplete knowledge of genomic features. Therefore, a cautious structural variation analysis is required to minimize the effect of technical constraints.
The impact of structural variants on the adaptive evolution of mammalian genomes is slowly gaining more focus but it is still incorporated in only a small number of population studies. In my thesis, I advocate the inclusion of structural variants in studies of genomic diversity for a more comprehensive insight into genomic variation within and between species, and its effect on adaptive evolution.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.
Heterostyly represents a fascinating adaptation to promote outbreeding in plants that evolved multiple times independently. While L-morph individuals form flowers with long styles, short anthers, and small pollen grains, S-morph individuals have flowers with short styles, long anthers, and large pollen grains. The difference between the morphs is controlled by an S-locus "supergene" consisting of several distinct genes that determine different traits of the syndrome and are held together, because recombination between them is suppressed. In Primula, the S locus is a roughly 300-kb hemizygous region containing five predicted genes. However, with one exception, their roles remain unclear, as does the evolutionary buildup of the S locus. Here we demonstrate that the MADS-box GLOBOSA2 (GLO2) gene at the S locus determines anther position. In Primula forbesii S-morph plants, GLO2 promotes growth by cell expansion in the fused tube of petals and stamen filaments beneath the anther insertion point; by contrast, neither pollen size nor male incompatibility is affected by GLO2 activity. The paralogue GLO1, from which GLO2 arose by duplication, has maintained the ancestral B-class function in specifying petal and stamen identity, indicating that GLO2 underwent neofunctionalization, likely at the level of the encoded protein. Genetic mapping and phylogenetic analysis indicate that the duplications giving rise to the style-length-determining gene CYP734A50 and to GLO2 occurred sequentially, with the CYP734A50 duplication likely the first. Together these results provide the most detailed insight into the assembly of a plant supergene yet and have important implications for the evolution of heterostyly.
The current trends of crop yield improvements are not expected to meet the projected rise in demand. Genomic selection uses molecular markers and machine learning to identify superior genotypes with improved traits, such as growth. Plant growth directly depends on rates of metabolic reactions which transform nutrients into the building blocks of biomass. Here, we predict growth of Arabidopsis thaliana accessions by employing genomic prediction of reaction rates estimated from accession-specific metabolic models. We demonstrate that, comparing to classical genomic selection on the available data sets for 67 accessions, our approach improves the prediction accuracy for growth within and across nitrogen environments by 32.6% and 51.4%, respectively, and from optimal nitrogen to low carbon environment by 50.4%. Therefore, integration of molecular markers into metabolic models offers an approach to predict traits directly related to metabolism, and its usefulness in breeding can be examined by gathering matching datasets in crops. An increase in genomic selection (GS) accuracy can accelerate genetic gain by shortening the breeding cycles. Here, the authors introduce a network-based GS method that uses metabolic models and improves the prediction accuracy of Arabidopsis growth within and across environments.
Recent research has linked sphingolipid (SL) metabolism with cystic fibrosis transmembrane conductance regulator (CFTR) activity, affecting bioactive lipid mediator sphingosine-1-phosphate (S1P). We hypothesize that loss of CFTR function in cystic fibrosis (CF) patients influenced plasma S1P levels. Total and unbound plasma S1P levels were measured in 20 lung-transplanted adult CF patients and 20 healthy controls by mass spectrometry and enzyme-linked immunosorbent assay (ELISA). S1P levels were correlated with CFTR genotype, routine laboratory parameters, lung function and pathogen colonization, and clinical symptoms. Compared to controls, CF patients showed lower unbound plasma S1P, whereas total S1P levels did not differ. A positive correlation of total and unbound S1P levels was found in healthy controls, but not in CF patients. Higher unbound S1P levels were measured in Delta F508-homozygous compared to Delta F508-heterozygous CF patients (p = 0.038), accompanied by higher levels of HDL in Delta F508-heterozygous patients. Gastrointestinal symptoms were more common in Delta F508 heterozygotes compared to Delta F508 homozygotes. This is the first clinical study linking plasma S1P levels with CFTR function and clinical presentation in adult CF patients. Given the emerging role of immunonutrition in CF, our study might pave the way for using S1P as a novel biomarker and nutritional target in CF.