Autotrophic growth of Escherichia coli is achieved by a small number of genetic changes.

Synthetic autotrophy is a promising avenue to sustainable bioproduction from CO2. Here, we use iterative laboratory evolution to generate several distinct autotrophic strains. Utilising this genetic diversity, we identify that just three mutations are sufficient for Escherichia coli to grow autotrophically, when introduced alongside non-native energy (formate dehydrogenase) and carbon-fixing (RuBisCO, phosphoribulokinase, carbonic anhydrase) modules. The mutated genes are involved in glycolysis (pgi), central-carbon regulation (crp), and RNA transcription (rpoB). The pgi mutation reduces the enzyme's activity, thereby stabilising the carbon-fixing cycle by capping a major branching flux. For the other two mutations, we observe down-regulation of several metabolic pathways and increased expression of native genes associated with the carbon-fixing module (rpiB) and the energy module (fdoGH), as well as an increased ratio of NADH/NAD+ - the cycle's electron-donor. This study demonstrates the malleability of metabolism and its capacity to switch trophic modes using only a small number of genetic changes and could facilitate transforming other heterotrophic organisms into autotrophs.

gUMI-BEAR, a modular, unsupervised population barcoding method to track variants and evolution at high resolution.

Cellular lineage tracking provides a means to observe population makeup at the clonal level, allowing exploration of heterogeneity, evolutionary and developmental processes and individual clones' relative fitness. It has thus contributed significantly to understanding microbial evolution, organ differentiation and cancer heterogeneity, among others. Its use, however, is limited because existing methods are highly specific, expensive, labour-intensive, and, critically, do not allow the repetition of experiments. To address these issues, we developed gUMI-BEAR (genomic Unique Molecular Identifier Barcoded Enriched Associated Regions), a modular, cost-effective method for tracking populations at high resolution. We first demonstrate the system's application and resolution by applying it to track tens of thousands of Saccharomyces cerevisiae lineages growing together under varying environmental conditions applied across multiple generations, revealing fitness differences and lineage-specific adaptations. Then, we demonstrate how gUMI-BEAR can be used to perform parallel screening of a huge number of randomly generated variants of the Hsp82 gene. We further show how our method allows isolation of variants, even if their frequency in the population is low, thus enabling unsupervised identification of modifications that lead to a behaviour of interest.

Evolthon: A community endeavor to evolve lab evolution.

In experimental evolution, scientists evolve organisms in the lab, typically by challenging them to new environmental conditions. How best to evolve a desired trait? Should the challenge be applied abruptly, gradually, periodically, sporadically? Should one apply chemical mutagenesis, and do strains with high innate mutation rate evolve faster? What are ideal population sizes of evolving populations? There are endless strategies, beyond those that can be exposed by individual labs. We therefore arranged a community challenge, Evolthon, in which students and scientists from different labs were asked to evolve Escherichia coli or Saccharomyces cerevisiae for an abiotic stress-low temperature. About 30 participants from around the world explored diverse environmental and genetic regimes of evolution. After a period of evolution in each lab, all strains of each species were competed with one another. In yeast, the most successful strategies were those that used mating, underscoring the importance of sex in evolution. In bacteria, the fittest strain used a strategy based on exploration of different mutation rates. Different strategies displayed variable levels of performance and stability across additional challenges and conditions. This study therefore uncovers principles of effective experimental evolutionary regimens and might prove useful also for biotechnological developments of new strains and for understanding natural strategies in evolutionary arms races between species. Evolthon constitutes a model for community-based scientific exploration that encourages creativity and cooperation.

Levocarnitine for the Treatment of Valproic Acid-Induced Hyperammonemic Encephalopathy in Children: The Experience of a Large, Tertiary Care Pediatric Hospital and a Poison Center.

BACKGROUND: Although rare, symptomatic hyperammonemia is sometimes associated with valproic acid (VPA), especially in children. L-carnitine (levocarnitine), sometimes classified as an essential amino acid, is vital to mitochondrial utilization of fatty acids and can be helpful in treating this condition. The data supporting this, however, are limited. STUDY QUESTION: The aim of the study was to illustrate the role of L-carnitine in the treatment of patients with VPA-induced hyperammonemic encephalopathy (VPE) at 2 different institutions. METHODS: Medical records of affected patients were reviewed; data collected included exposure history, clinical manifestations, physical examination, and laboratory values. RESULTS: There were 13 cases of VPE; 12 were associated with therapeutic dosing and 1 with an overdose. The maximum ammonia concentration was 557 µmol/L, and blood concentrations of VPA ranged from 68 to 600 µg/mL (therapeutic range 50-100 µg/mL). In all cases, liver function tests were normal or only mildly increased. In this study, 12 patients received a daily dose of L-carnitine 100 mg/kg, and 1 received 200 mg/kg (intravenous infusion over 30 minutes) divided every 8 hours until clinical improvement. All patients made a full recovery. None developed adverse effects or reactions, and no cases of toxicity were reported. CONCLUSION: Our series suggests that intravenous L-carnitine, at a dose of 100 mg·kg·d in 3 divided doses each over 30 minutes until clinical improvement occurs, is a safe and effective treatment in the management of VPE in children.

A Bacterial Growth Law out of Steady State.

Bacterial growth follows simple laws in constant conditions. However, bacteria in nature often face fluctuating environments. We therefore ask whether there are growth laws that apply to changing environments. We derive a law for upshifts using an optimal resource-allocation model: the post-shift growth rate equals the geometrical mean of the pre-shift growth rate and the growth rate on saturating carbon. We test this using chemostat and batch culture experiments, as well as previous data from several species. The increase in growth rate after an upshift indicates that ribosomes have spare capacity (SC). We demonstrate theoretically that SC has the cost of slow steady-state growth but is beneficial after an upshift because it prevents large overshoots in intracellular metabolites and allows rapid response to change. We also provide predictions for downshifts. The present study quantifies the optimal degree of SC, which rises the slower the growth rate, and suggests that SC can be precisely regulated.