Biology subjects

Lercher, M.

Publications and source records attributed to Lercher, M..

4 recordsLinked to original sources

bioRxiv2018

Modeling Cellular Resource Allocation Reveals Low Phenotypic Plasticity of C4 Plants and Infers Environments of C4 Photosynthesis Evolution

- The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extend do they reflect ancestral environments? Here, we explore these questions for C3, C4, and C3-C4 intermediate plants of the model genus Flaveria.\n- We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns.\n- To achieve maximal CO2 fixation rates under growth conditions differing from those experienced during their evolution, C4 species need to re-allocate significantly more nitrogen between photosynthetic components than their C3 relatives. As this is linked to a limited phenotypic plasticity, observed resource distributions in C4 plants still reflect optimality in ancestral environments, allowing their quantitative inference.\n- Our work allows us to quantify environmental effects on resource allocation and performance of photosynthetic organisms. This understanding paves the way for interpreting present photosynthetic physiology in the light of evolutionary history.

plant biology↗

bioRxiv2018

Different alleles of the same gene vary in pleiotropy, often mediated through currency metabolite production

A major obstacle to the mapping of genotype-phenotype relationships is pleiotropy, the tendency of mutations to affect seemingly unrelated traits. Pleiotropy has major implications for evolution, development, ageing, and disease. Except for disease data, pleiotropy is almost exclusively estimated from full gene knockouts. However, most deleterious alleles segregating in natural populations do not fully abolish gene function, and the degree to which a polymorphism reduces protein function may influence the number of traits it affects. Utilizing genome-scale metabolic models for Escherichia coli and the bakers yeast Saccharomyces cerevisiae, we show that most fitness-reducing full gene knockouts of metabolic genes have pleiotropic effects, i.e., they compromise the production of multiple biomass components. Alleles of the same gene with increasingly reduced enzyme function typically affect an increasing number of biomass components. This increasing pleiotropy is often mediated through effects on the generation of currency metabolites such as ATP or NADPH. We conclude that the physiological effects observed in full gene knockouts will in most cases not be representative for alleles with only partially reduced enzyme capacity or expression level.

systems biology↗

bioRxiv2018

Automated high-quality reconstruction of metabolic networks from high-throughput data

While new genomes are sequenced at ever increasing rates, their phenotypic analysis remains a major bottleneck of biomedical research. The generation of genome-scale metabolic models capable of accurate phenotypic predictions is a labor-intensive endeavor; accordingly, such models are available for only a small percentage of sequenced species. The standard metabolic reconstruction process starts from a (semi-)automatically generated draft model, which is then refined through extensive manual curation. Here, we present a novel strategy suitable for full automation, which exploits high-throughput gene knockout or nutritional growth data. We test this strategy by reconstructing accurate genome-scale metabolic models for three strains of Streptococcus, a major human pathogen. The resulting models contain a lower proportion of reactions unsupported by genomic evidence than the most widely used E. coli model, but reach the same accuracy in terms of knockout prediction. We confirm the models predictive power by analyzing experimental data for auxotrophy, additional nutritional environments, and double gene knockouts, and we generate a list of potential drug targets. Our results demonstrate the feasibility of reconstructing high-quality genome-scale metabolic models from high-throughput data, a strategy that promises to massively accelerate the exploration of metabolic phenotypes.\n\nSignificance statementReading bacterial genomes has become a cheap, standard laboratory procedure. A genome by itself, however, is of little information value - we need a way to translate its abstract letter sequence into a model that describes the capabilities of its carrier. Until now, this endeavor required months of manual work by experts. Here, we show how this process can be automated by utilizing high-throughput experimental data. We use our novel strategy to generate highly accurate metabolic models for three strains of Streptococcus, a major threat to human health.

systems biology↗

bioRxiv2017

The adaptive acquisition of single DNA segments drives metabolic evolution across E. coli lineages

Even closely related prokaryotes show an astounding diversity in their ability to grow in different nutritional environments1,2. Mechanistically, this diversity arises predominantly through horizontal gene transfer, the exchange of DNA between individuals from different strains3. It has been hypothesized that complex metabolic adaptations - those requiring the acquisition of multiple distinct DNA segments - can evolve via selectively neutral intermediate steps4,5; an alternative explanation rests on the existence of intermediate environments that make each individual DNA acquisition adaptive6 However, it is unclear how important changing environments6 are compared to neutral explorations of phenotype space5; more fundamentally, it is unknown what fraction of metabolic adaptations are indeed complex. Here, we use metabolic network simulations to show that all 3,363 phenotypic innovations observed in the evolutionary history of 53 E. coli strains arose through the acquisition of a single DNA segment; while we found no evidence for the contribution of selectively neutral processes, 10.6% of adaptations to previously unviable environments relied on the support of DNA acquisitions on earlier phylogenetic branches. 97.0% of all metabolic phenotypes accessible for the E. coli pan-genome could be bestowed on any ancestral strain by transferring a single DNA segment from one of the extant strains. These results demonstrate an amazing ability of the E. coli lineage to quickly adapt to previously inaccessible environments through a single DNA acquisition, an ability likely to be mirrored in other clades of generalist bacteria.

systems biology↗