Biology subjects

Jones, P.

Publications and source records attributed to Jones, P..

5 recordsLinked to original sources

bioRxiv2018

Synthetic metabolic pathways for photobiological conversion of CO2 into hydrocarbon fuel

Liquid fuels sourced from fossil sources are the dominant energy form for mobile transport today. The consumption of fossil fuels is still increasing, resulting in a continued search for more sustainable methods to renew our supply of liquid fuel. Photosynthetic microorganisms naturally accumulate hydrocarbons that could serve as a replacement for fossil fuel, however productivities remain low. We report successful introduction of five synthetic metabolic pathways in two green cell factories, prokaryotic cyanobacteria and eukaryotic algae. Heterologous thioesterase expression enabled high-yield conversion of native acyl-ACP into free fatty acids (FFA) in Synechocystis sp. PCC 6803 but not in Chlamydomonas reinhardtii where the polar lipid fraction instead was enhanced. Despite no increase in measurable FFA in Chlamydomonas, genetic recoding and over-production of the native fatty acid photodecarboxylase (FAP) resulted in increased accumulation of 7-heptadecene. Implementation of a carboxylic acid reductase (CAR) and aldehyde deformylating oxygenase (ADO) dependent synthetic pathway in Synechocystis resulted in the accumulation of fatty alcohols and a decrease in the native saturated alkanes. In contrast, the replacement of CAR and ADO with Pseudomonas mendocina UndB (so named as it is responsible for 1-undecene biosynthesis in Pseudomonas) or Chlorella variabilis FAP resulted in high-yield conversion of thioesterase-liberated FFAs into corresponding alkenes and alkanes, respectively. At best, the engineering resulted in an increase in hydrocarbon accumulation of 8- (from 1 to 8.5 mg/g dell dry weight) and 19-fold (from 4 to 77 mg/g cell dry weight) for Chlamydomonas and Synechocystis, respectively. In conclusion, reconstitution of the eukaryotic algae pathway in the prokaryotic cyanobacteria host generated the most effective system, highlighting opportunities for mix-and-match synthetic metabolism. These studies describe functioning synthetic metabolic pathways for hydrocarbon fuel synthesis in photosynthetic microorganisms for the first time, moving us closer to the commercial implementation of photobiocatalytic systems that directly convert CO2 into infrastructure-compatible fuels.\n\nHighlightsO_LISynthetic metabolic pathways for hydrocarbon fuels were engineered in algae\nC_LIO_LIFree fatty acids were effectively converted into alkenes and alkanes\nC_LIO_LITransfer of algal pathway into cyanobacteria was the most effective\nC_LIO_LIAlkane yield was enhanced 19-fold in Synechocystis spp. PCC 6803\nC_LIO_LIAlkene yield was enhanced 8-fold in Chlamydomonas reinhardtii\nC_LI

synthetic biology↗

bioRxiv2018

Pleiotropic and Epistatic Network-Based Discovery: Integrated Networks for Target Gene Discovery

Biological organisms are complex systems that are composed of functional networks of interacting molecules and macromolecules. Complex phenotypes are the result of orchestrated, hierarchical, heterogeneous collections of expressed genomic variants. However, the effects of these variants are the result of historic selective pressure and current environmental and epigenetic signals, and, as such, their co-occurrence can be seen as genome-wide correlations in a number of different manners. Biomass recalcitrance (i.e., the resistance of plants to degradation or deconstruction, which ultimately enables access to a plants sugars) is a complex polygenic phenotype of high importance to biofuels initiatives. This study makes use of data derived from the re-sequenced genomes from over 800 different Populus trichocarpa genotypes in combination with metabolomic and pyMBMS data across this population, as well as co-expression and co-methylation networks in order to better understand the molecular interactions involved in recalcitrance, and identify target genes involved in lignin biosynthesis/degradation. A Lines Of Evidence (LOE) scoring system is developed to integrate the information in the different layers and quantify the number of lines of evidence linking genes to lignin-related lignin-phenotypes across the network layers. The resulting Genome Wide Association Study networks, integrated with Single Nucleotide Polymorphism (SNP) correlation, co-methylation and co-expression networks through the LOE scores are proving to be a powerful approach to determine the pleiotropic and epistatic relationships underlying cellular functions and, as such, the molecular basis for complex phenotypes, such as recalcitrance.

bioinformatics↗

bioRxiv2017

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network (SCN) from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we use this to define, transcriptomic brain networks (TBN) by estimating gene co-expression between pairs of cortical regions. Finally, we explore the hypothesis that TBN and the SCN are coupled.\n\nTBN and SCN were correlated across connection weights and showed qualitatively similar complex topological properties. There were differences between networks in degree and distance distributions. However, cortical areas connected to each other within modules of the SCN network had significantly higher levels of whole genome co-expression than expected by chance.\n\nNodes connected in the SCN had significantly higher levels of expression and co-expression of a Human Supragranular Enriched (HSE) gene set that are known to be important for large-scale cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not completely related to the common constraint of physical distance on both networks.

neuroscience↗

bioRxiv2017

Adolescent Tuning Of Association Cortex In Human Structural Brain Networks

Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organisation of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N=297 healthy participants, aged 14-24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

neuroscience↗

bioRxiv2017

Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N=204 (N=32 imaging) on two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N=176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and {Omega}nyx).\n\nHighlightsO_LIWe describe Latent change score modelling as a flexible statistical tool\nC_LIO_LIKey developmental questions can be readily formalized using LCS models\nC_LIO_LIWe provide accessible open source code and software examples to fit LCS models\nC_LIO_LIWhite matter structural change is negatively correlated with processing speed gains\nC_LIO_LIFrontal lobe thinning in adolescence is more variable in males than females\nC_LI

neuroscience↗