Biology subjects

Evans, J. R.

Publications and source records attributed to Evans, J. R..

26 records · Page 2Linked to original sources

bioRxiv2021

Exploring functional diversity of Nicotiana tabacum Aquaporins

Aquaporins (AQPs) are multifunctional membrane proteins which have greatly diversified in number and function in the plant Kingdom. In plants, AQPs have evolved to comprise a dynamic solute transport network occurring in all tissues and facilitating transport of water and vital solutes across various cellular membranes. Plant AQPs are involved in a multitude of plant physiological processes, however a better understanding is required of AQP structure-function relationships, multifunctionality and cell membrane localisation in order to begin to describe putative functional roles for the numerous plant AQP gene isoforms. Using an integrated approach, we characterised nine diverse Nicotiana tabacum (tobacco) aquaporins, spanning the 3 largest AQP subfamilies (PIP, TIP, and NIP) and with varied gene expression profiles. High-throughput yeast-based functional screens identified novel candidates for water, hydrogen peroxide (H2O2), boric acid (BA) and urea transport across the 3 AQP subfamilies. Using GFP translational fusions, AQPs observed in planta were localised to the plasma membrane, tonoplast and endoplasmic reticulum. AlphaFold protein models illustrated differences in pore shape and size across subfamilies. Our analysis supports the importance of functional data for deciphering unknown AQP structure-function relationships and uncovering novel candidates for in planta solute transport.

plant biology↗

bioRxiv2021

HNF4α isoforms regulate the circadian balance between carbohydrate and lipid metabolism in the liver

Hepatocyte Nuclear Factor 4 (HNF4), a master regulator of hepatocyte differentiation, is regulated by two promoters (P1 and P2). P1-HNF4 is the major isoform in the adult liver while P2-HNF4 is thought to be expressed only in fetal liver and liver cancer. Here, we show that P2- HNF4 is expressed at ZT9 and ZT21 in the normal adult liver and orchestrates a distinct transcriptome and metabolome via unique chromatin and protein-protein interactions. We demonstrate that while P1-HNF4 drives gluconeogenesis, P2-HNF4 drives ketogenesis and is required for elevated levels of ketone bodies in females. Exon swap mice expressing only P2- HNF4 exhibit subtle differences in circadian gene regulation and disruption of the clock increases expression of P2-HNF4. Taken together, we propose that the highly conserved two-promoter structure of the Hnfa gene is an evolutionarily conserved mechanism to maintain the balance between gluconeogenesis and ketogenesis in the liver in a circadian fashion.

genomics↗

bioRxiv2020

Highly enriched hiPSC-derived midbrain dopaminergic neurons robustly models Parkinson's disease.

The development of human induced pluripotent stem cells (hiPSC) has greatly aided our ability to model neurodegenerative diseases. However, generation of midbrain dopaminergic (mDA) neurons is a major challenge and protocols are variable. Here, we developed a method to differentiate hiPSCs into enriched populations (>80%) of mDA neurons using only small molecules. We confirmed the identity of the mDA neurons using single-cell RNA-sequencing and detection of classical markers. Single-cell live imaging demonstrated neuronal calcium signalling and functional dopamine transport. Electrophysiology measures highlighted the ability to form synapses and networks in culture. Patient-specific hiPSC lines differentiated to produce functional mDA neurons that exhibit the hallmarks of synucleinopathy including: aggregate formation, oxidative stress as well as mitochondrial dysfunction and impaired lysosomal dynamics. In summary, we establish a robust differentiation paradigm to generate enriched mDA neurons from hiPSCs, which can be used to faithfully model key aspects of Parkinsons disease (PD), providing the potential to further elucidate molecular mechanisms contributing to disease development.

neuroscience↗

bioRxiv2020

Uncovering candidate genes involved in photosynthetic capacity using unexplored genetic variation in Spring Wheat

To feed an ever-increasing population we must leverage advances in genomics and phenotyping to harness the variation in wheat breeding populations for traits like photosynthetic capacity which remains unoptimized. Here we survey a diverse set of wheat germplasm containing elite, introgression and synthetic derivative lines uncovering previously uncharacterised variation. We demonstrate how strategic integration of exotic material alleviates the D genome genetic bottleneck in wheat, increasing SNP rate by 62% largely due to Ae. tauschii synthetic wheat donors. Across the panel, 67% of the Ae. tauschii donor genome is represented as introgressions in elite backgrounds. We show how observed genetic variation together with hyperspectral reflectance data can be used to identify candidate genes for traits relating to photosynthetic capacity using association analysis. This demonstrates the value of genomic methods in uncovering hidden variation in wheat and how that variation can assist breeding efforts and increase our understanding of complex traits.

plant biology↗

bioRxiv2020

Effect of leaf temperature on estimating physiological traits of wheat leaves from hyperspectral reflectance

A growing number of leaf traits can be predicted from hyperspectral reflectance data. These include structural and compositional traits, such as leaf mass per area, nitrogen and chlorophyll content, but also physiological traits such a Rubisco carboxylation activity, electron transport rate and respiration rate. Since physiological traits vary with leaf temperature, how does this impact on predictions made from reflectance measurements? We investigated this with two wheat varieties, by repeatedly measuring each leaf through a sequence of temperatures imposed by varying the air temperature in a growth room. The function predicting Rubisco capacity normalised to 25 {degrees}C predicted the same value, regardless of leaf temperatures ranging from 20 to 35{degrees}C. Leaf temperature affected none of the predicted traits: Vcmax25, J, chlorophyll content, LMA, N content per unit leaf area or Vcmax25/N. However, as others have derived models to predict Rubisco activity that includes variation associated with leaf temperature, we discuss whether these functions may include a temperature signal within the reflectance spectra.

plant biology↗

bioRxiv2020

Stress reactivity elicits a tissue-specific reduction in telomere length in ageing zebrafish (Danio rerio)

Telomere length reflects cellular ageing. Increased telomere shortening in leukocytes is associated with a range of neurodegenerative and cardiovascular diseases, the onset and progression of which may be mediated by behavioural traits such as anxiety and stress reactivity. However, the effects of the hypothalamus-pituitary-adrenal axis stress response are shown to be tissue specific. As such, leukocyte telomere length may not give an accurate measure of the relationship between stress-reactivity and telomere length in disease relevant tissues. To test the hypothesis that stress-reactivity contributes to age-related telomere shortening in a tissue specific manner, we examined the correlation between telomere length in heart and brain tissue and stress-reactivity in a population of young (6-9 month) and ageing (18 month) zebrafish. Stress-reactivity was assessed by tank diving, a zebrafish version of the rodent open-field test, and through gene expression. Telomere length was assessed using quantitative polymerase chain reaction. We show that ageing zebrafish have shorter telomeres in both heart and brain. Telomere length is inversely related to stress-reactivity in heart but not brain of ageing individuals. These data support the hypotheses that an anxious predisposition contributes to telomere shortening in heart tissue, and by extension age-related heart disease, and that stress-reactivity contributes to age-related telomere shortening in a tissue-specific manner.

physiology↗

bioRxiv2019

Machine learning based detection of genetic and drug class variant impact on functionally conserved protein binding dynamics

The application of statistical methods to comparatively framed questions about protein dynamics can potentially enable investigations of biomolecular function beyond the current sequence and structural methods in bioinformatics. However, chaotic behavior in single protein trajectories requires statistical inference be obtained from large ensembles of molecular dynamic (MD) simulations representing the comparative functional states of a given protein. Meaningful interpretation of such a complex form of big data poses serious challenges to users of MD. Here, we announce DROIDS v3.0, a molecular dynamic (MD) method + software package for comparative protein dynamics, incorporating many new features including maxDemon v1.0, a multi-method machine learning application that trains on large ensemble comparisons of concerted protein motions in opposing functional states and deploys learned classifications of these states onto newly generated protein dynamic simulations. Local canonical correlations in learning patterns generated from self-similar MD runs are used to identify regions of functionally conserved protein dynamics. Subsequent impacts of genetic and drug class variants on conserved dynamics can also be analyzed by deploying the classifiers on variant MD runs and quantifying how often these altered protein systems display the opposing functional states. Here, we present several case studies of complex changes in functional protein dynamics caused by temperature, genetic mutation, and binding interaction with nucleic acids and small molecules. We studied the impact of genetic variation on functionally conserved protein dynamics in ubiquitin and TATA binding protein and demonstrate that our learning algorithm can properly identify regions of conserved dynamics. We also report impacts to dynamics that correspond well with predicted disruptive effects of a variety of genetic mutations. In addition, we studied the impact of drug class variation on the ATP binding region of Hsp90, similarly identifying conserved dynamics and impacts that rank accordingly with how closely various Hsp90 inhibitors mimic natural ATP binding.\n\nStatement of significanceWe propose a statistical method as well as offer a user-friendly graphical interfaced software pipeline for comparing simulations of the complex motions (i.e. dynamics) of proteins in different functional states. We also provide both method and software to apply artificial intelligence (i.e. machine learning methods) that enable the computer to recognize complex functional differences in protein dynamics on new simulations and report them to the user. This method can identify dynamics important for protein function, as well as to quantify how the motions of molecular variants differ from these important functional dynamic states. For the first time, this method of analysis allows the impacts of different genetic backgrounds or drug classes to be examined within the context of functional motions of the specific protein system under investigation.

biophysics↗

bioRxiv2017

Water and temperature stress define the optimal flowering period for wheat in south-eastern Australia

Across the Australian wheat belt, the time at which wheat flowers is a critical determinant of yield. In all environments an optimal flowering period (OFP) exists which is defined by decreasing frost risk, and increasing water and heat stress. Despite their critical importance, OFPs have not been comprehensively defined across south eastern Australias (SEA) cropping zone using yield estimates incorporating temperature, radiation and water-stress. In this study, the widely validated cropping systems model APSIM was used to simulate wheat yield and flowering date, with reductions in yield applied for frost and heat damage based on air temperatures during sensitive periods. Simulated crops were sown at weekly intervals from April 1 to July 15 of each year. The relationship between flowering date and grain yield was established for 28 locations using 51-years (1963-2013) of climate records. We defined OFPs as the flowering period which was associated with a mean yield of [≥] 95% of maximum yield from the combination of 51 seasons and 16 sowing dates. OFPs for wheat in SEA varied with site and season and were largely driven by seasonal water supply and demand, with extremes of heat and temperature having a secondary though auto-correlated effect. Quantifying OFPs will be a vital first step to identify suitable genotype x sowing date combinations to maximise yield in different locations, particularly given recent and predicted regional climate shifts including the decline in autumn rainfall.

plant biology↗