C4 photosynthesis represents a most remarkable case of convergent evolution of a complex trait, which includes the reprogramming of the expression patterns of thousands of genes. Anatomical, physiological, and phylogenetic and analyses as well as computational modeling indicate that the establishment of a photorespiratory carbon pump (termed C2 photosynthesis) is a prerequisite for the evolution of C4. However, a mechanistic model explaining the tight connection between the evolution of C4 and C2 photosynthesis is currently lacking. Here we address this question through comparative transcriptomic and biochemical analyses of closely related C3, C3–C4, and C4 species, combined with Flux Balance Analysis constrained through a mechanistic model of carbon fixation. We show that C2 photosynthesis creates a misbalance in nitrogen metabolism between bundle sheath and mesophyll cells. Rebalancing nitrogen metabolism requires anaplerotic reactions that resemble at least parts of a basic C4 cycle. Our findings thus show how C2 photosynthesis represents a pre-adaptation for the C4 system, where the evolution of the C2 system establishes important C4 components as a side effect.DOI: http://dx.doi.org/10.7554/eLife.02478.001

An ultimate goal of evolutionary biology is the prediction and experimental verification of adaptive trajectories on macroevolutionary timescales. This aim has rarely been achieved for complex biological systems, as models usually lack clear correlates of organismal fitness. Here, we simulate the fitness landscape connecting two carbon fixation systems: C3 photosynthesis, used by most plant species, and the C4 system, which is more efficient at ambient CO2 levels and elevated temperatures and which repeatedly evolved from C3. Despite extensive sign epistasis, C4 photosynthesis is evolutionarily accessible through individually adaptive steps from any intermediate state. Simulations show that biochemical subtraits evolve in modules; the order and constitution of modules confirm and extend previous hypotheses based on species comparisons. Plant-species-designated C3-C4 intermediates lie on predicted evolutionary trajectories, indicating that they indeed represent transitory states. Contrary to expectations, we find no slowdown of adaptation and no diminishing fitness gains along evolutionary trajectories.

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.

Mycobacterium tuberculosis is a serious human pathogen threat exhibiting complex evolution of antimicrobial resistance (AMR). Accordingly, the many publicly available datasets describing its AMR characteristics demand disparate data-type analyses. Here, we develop a reference strain-agnostic computational platform that uses machine learning approaches, complemented by both genetic interaction analysis and 3D structural mutation-mapping, to identify signatures of AMR evolution to 13 antibiotics. This platform is applied to 1595 sequenced strains to yield four key results. First, a pan-genome analysis shows that M. tuberculosis is highly conserved with sequenced variation concentrated in PE/PPE/PGRS genes. Second, the platform corroborates 33 genes known to confer resistance and identifies 24 new genetic signatures of AMR. Third, 97 epistatic interactions across 10 resistance classes are revealed. Fourth, detailed structural analysis of these genes yields mechanistic bases for their selection. The platform can be used to study other human pathogens.