Genome-Scale Reconstruction and Analysis of the Pseudomonas putida KT2440 Metabolic Network Facilitates Applications in Biotechnology

A cornerstone of biotechnology is the use of microorganisms for the efficient production of chemicals and the elimination of harmful waste. Pseudomonas putida is an archetype of such microbes due to its metabolic versatility, stress resistance, amenability to genetic modifications, and vast potential for environmental and industrial applications. To address both the elucidation of the metabolic wiring in P. putida and its uses in biocatalysis, in particular for the production of non-growth-related biochemicals, we developed and present here a genome-scale constraint-based model of the metabolism of P. putida KT2440. Network reconstruction and flux balance analysis (FBA) enabled definition of the structure of the metabolic network, identification of knowledge gaps, and pin-pointing of essential metabolic functions, facilitating thereby the refinement of gene annotations. FBA and flux variability analysis were used to analyze the properties, potential, and limits of the model. These analyses allowed identification, under various conditions, of key features of metabolism such as growth yield, resource distribution, network robustness, and gene essentiality. The model was validated with data from continuous cell cultures, high-throughput phenotyping data, 13C-measurement of internal flux distributions, and specifically generated knock-out mutants. Auxotrophy was correctly predicted in 75% of the cases. These systematic analyses revealed that the metabolic network structure is the main factor determining the accuracy of predictions, whereas biomass composition has negligible influence. Finally, we drew on the model to devise metabolic engineering strategies to improve production of polyhydroxyalkanoates, a class of biotechnologically useful compounds whose synthesis is not coupled to cell survival. The solidly validated model yields valuable insights into genotype–phenotype relationships and provides a sound framework to explore this versatile bacterium and to capitalize on its vast biotechnological potential

Prehistoric palaeodemographics and regional land cover change in eastern Iberia

Much attention has been placed on the drivers of vegetation change on the Iberian Peninsula. While climate plays a key role in determining the species pools within different regions and exerts a strong influence on broad vegetation patterning, the role of humans, particularly during prehistory, is less clear. The aim of this paper is to assess the influence of prehistoric population change on shaping vegetation patterns in eastern Iberia and the Balearic Islands between the start of the Neolithic and the late Bronze Age. In all, 3385 radiocarbon dates have been compiled across the study area to provide a palaeodemographic proxy (radiocarbon summed probability distributions (SPDs)). Modelled trends in palaeodemographics are compared with regional-scale vegetation patterns deduced from analysis of 30 fossil pollen sequences. The pollen sequences have been standardised with count data aggregated into contiguous 200-year time windows from 11,000 cal. yr BP to the present. Samples have been classified using cluster analysis to determine the predominant regional land cover types through the Holocene. Regional human impact indices and diversity metrics have been derived for north-east and south-east Spain and the Balearic Islands. The SPDs show characteristic boom-and-bust cycles of population growth and collapse, but there is no clear synchronism between north-east and south-east Spain other than the rise of Neolithic farming. In north-east Iberia, patterns of demographic change are strongly linked to changes in vegetation diversity and human impact indicator groups. In the south-east, increases in population throughout the Chalcolithic and early Bronze Age result in more open landscapes and increased vegetation diversity. The demographic maximum occurred early in the 3rd millennium cal. BP on the Balearic Islands and is associated with the highest levels of human impact indicator groups. The results demonstrate the importance of population change in shaping the abundance and diversity of taxa within broad climatically determined biomes

Assessing changes in global fire regimes

PAGES, Past Global Changes, is funded by the Swiss Academy of Sciences and the Chinese Academy of Sciences and supported in kind by the University of Bern, Switzerland. Financial support was provided by the U.S. National Science Foundation award numbers 1916565, EAR-2011439, and EAR-2012123. Additional support was provided by the Utah Department of Natural Resources Watershed Restoration Initiative. SSS was supported by Brigham Young University Graduate Studies. MS was supported by National Science Centre, Poland (grant no. 2018/31/B/ST10/02498 and 2021/41/B/ST10/00060). JCA was supported by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101026211. PF contributed within the framework of the FCT-funded project no. UIDB/04033/2020. SGAF acknowledges support from Trond Mohn Stiftelse (TMS) and University of Bergen for the startup grant ‘TMS2022STG03’. JMP participation in this research was supported by the Forest Research Centre, a research unit funded by Fundação para a Ciência e a Tecnologia I.P. (FCT), Portugal (UIDB/00239/2020). A.-LD acknowledge PAGES, PICS CNRS 06484 project, CNRS-INSU, Région Nouvelle-Aquitaine, University of Bordeaux DRI and INQUA for workshop support.Background The global human footprint has fundamentally altered wildfire regimes, creating serious consequences for human health, biodiversity, and climate. However, it remains difficult to project how long-term interactions among land use, management, and climate change will affect fire behavior, representing a key knowledge gap for sustainable management. We used expert assessment to combine opinions about past and future fire regimes from 99 wildfire researchers. We asked for quantitative and qualitative assessments of the frequency, type, and implications of fire regime change from the beginning of the Holocene through the year 2300. Results Respondents indicated some direct human influence on wildfire since at least ~ 12,000 years BP, though natural climate variability remained the dominant driver of fire regime change until around 5,000 years BP, for most study regions. Responses suggested a ten-fold increase in the frequency of fire regime change during the last 250 years compared with the rest of the Holocene, corresponding first with the intensification and extensification of land use and later with anthropogenic climate change. Looking to the future, fire regimes were predicted to intensify, with increases in frequency, severity, and size in all biomes except grassland ecosystems. Fire regimes showed different climate sensitivities across biomes, but the likelihood of fire regime change increased with higher warming scenarios for all biomes. Biodiversity, carbon storage, and other ecosystem services were predicted to decrease for most biomes under higher emission scenarios. We present recommendations for adaptation and mitigation under emerging fire regimes, while recognizing that management options are constrained under higher emission scenarios. Conclusion The influence of humans on wildfire regimes has increased over the last two centuries. The perspective gained from past fires should be considered in land and fire management strategies, but novel fire behavior is likely given the unprecedented human disruption of plant communities, climate, and other factors. Future fire regimes are likely to degrade key ecosystem services, unless climate change is aggressively mitigated. Expert assessment complements empirical data and modeling, providing a broader perspective of fire science to inform decision making and future research priorities.Peer reviewe

Pathway-Consensus Approach to Metabolic Network Reconstruction for Pseudomonas putida KT2440 by Systematic Comparison of Published Models

Over 100 genome-scale metabolic networks (GSMNs) have been published in recent years and widely used for phenotype prediction and pathway design. However, GSMNs for a specific organism reconstructed by different research groups usually produce inconsistent simulation results, which makes it difficult to use the GSMNs for precise optimal pathway design. Therefore, it is necessary to compare and identify the discrepancies among networks and build a consensus metabolic network for an organism. Here we proposed a process for systematic comparison of metabolic networks at pathway level. We compared four published GSMNs of Pseudomonas putida KT2440 and identified the discrepancies leading to inconsistent pathway calculation results. The mistakes in the models were corrected based on information from literature so that all the calculated synthesis and uptake pathways were the same. Subsequently we built a pathway-consensus model and then further updated it with the latest genome annotation information to obtain modelPpuQY1140 for P. putida KT2440, which includes 1140 genes, 1171 reactions and 1104 metabolites. We found that even small errors in a GSMN could have great impacts on the calculated optimal pathways and thus may lead to incorrect pathway design strategies. Careful investigation of the calculated pathways during the metabolic network reconstruction process is essential for building proper GSMNs for pathway design