Enhancement of essential cofactors for in vivo biocatalysis.

A scarcity of cofactors, necessary metabolites or substrates for in vivo enzymatic reactions, is among the major barriers for product synthesis in metabolically engineered cells. This work compares our recently developed cofactor-boosting strategy, which uses xylose reductase (XR) and lactose to increase the intracellular levels of reduced or oxidized nicotinamide adenine dinucleotide (phosphate) (NAD(P)H), adenosine triphosphate (ATP) and acetyl coenzymeA (acetyl-CoA), with other previously reported methods. We demonstrated that the XR/lactose approach enhances levels of sugar alcohols and sugar phosphates, which leads to elevated levels of crucial cofactors required by specific metabolic pathways. The patterns of cofactor enhancement are not uniform and depend upon the specific pathway components that are overexpressed. We term this model the "user-pool" model. Here, we investigated metabolite alteration in the fatty-alcohol-producing system in the presence of XR/lactose within an early time frame (5 min after the bioconversion started). All metabolite data were analyzed using untargeted metabolomics. We found that the XR/lactose system could improve fatty-alcohol production as early as 5 min after the bioconversion started. The enhancement of key cofactors and intermediates, such as hexitol, NAD(P)H, ATP, 3-phosphoglycerate, acetyl-CoA, 6-phosphogluconate (6-PG) and glutathione, was consistent with those previously reported on a longer time scale (after 1 h). However, measurements performed at the early time reported here showed detectable differences in metabolite enhancement patterns, such as those of ATP, NADPH, acetyl-CoA and glutathione. These data could serve as a basis for future analysis of metabolic flux alteration by the XR/lactose system. Comparative analysis of the cofactor enhancement by XR and other methods suggests that XR/lactose can serve as a simple tool to increase levels of various cofactors for microbial cell factories.

RBAtools: a programming interface for Resource Balance Analysis models.

Motivation: Efficient resource allocation can contribute to an organism's fitness and can improve evolutionary success. Resource Balance Analysis (RBA) is a computational framework that models an organism's growth-optimal proteome configurations in various environments. RBA software enables the construction of RBA models on genome scale and the calculation of medium-specific, growth-optimal cell states including metabolic fluxes and the abundance of macromolecular machines. However, existing software lacks a simple programming interface for non-expert users, easy to use and interoperable with other software. Results: The python package RBAtools provides convenient access to RBA models. As a flexible programming interface, it enables the implementation of custom workflows and the modification of existing genome-scale RBA models. Its high-level functions comprise simulation, model fitting, parameter screens, sensitivity analysis, variability analysis and the construction of Pareto fronts. Models and data are represented as structured tables and can be exported to common data formats for fluxomics and proteomics visualization. Availability and implementation: RBAtools documentation, installation instructions and tutorials are available at https://sysbioinra.github.io/rbatools/. General information about RBA and related software can be found at rba.inrae.fr.

Control of COVID-19 Outbreaks under Stochastic Community Dynamics, Bimodality, or Limited Vaccination.

Reaching population immunity against COVID-19 is proving difficult even in countries with high vaccination levels. Thus, it is critical to identify limits of control and effective measures against future outbreaks. The effects of nonpharmaceutical interventions (NPIs) and vaccination strategies are analyzed with a detailed community-specific agent-based model (ABM). The authors demonstrate that the threshold for population immunity is not a unique number, but depends on the vaccination strategy. Prioritizing highly interactive people diminishes the risk for an infection wave, while prioritizing the elderly minimizes fatalities when vaccinations are low. Control over COVID-19 outbreaks requires adaptive combination of NPIs and targeted vaccination, exemplified for Germany for January-September 2021. Bimodality emerges from the heterogeneity and stochasticity of community-specific human-human interactions and infection networks, which can render the effects of limited NPIs uncertain. The authors' simulation platform can process and analyze dynamic COVID-19 epidemiological situations in diverse communities worldwide to predict pathways to population immunity even with limited vaccination.