Biology subjects

Biggs, P.

Publications and source records attributed to Biggs, P..

5 recordsLinked to original sources

bioRxiv2025

Glutamatergic Dysfunction of Astrocytes in Paraventricular Nucleus of Thalamus Contributes to Adult Anxiety Susceptibility in Adolescent Ethanol Exposed Mice

Repeated ethanol exposure during adolescence increases the risk for displaying anxiogenic phenotype in adulthood, but the underlying mechanisms are not fully understood. The paraventricular nucleus of thalamus (PVT) has been considered a hub brain area for controlling the anxiety network in the brain. Recent structural and functional investigations indicate that the PVT exhibits diverse neural signals aligned with early-life events, which are highly linked with anxiety-like behaviors. However, it remains unknown if repeated ethanol exposure during adolescence will affect the coordinated brain activities of the PVT in adulthood, and consequent behavioral adaptation. Here we show that adolescent repeated intermittent ethanol exposure (AIE) triggers anxiety-like behaviors and parallelly induces the glutamatergic adaptation in the PVT after four weeks withdrawal from the last ethanol exposure. The firing rates, along with the spatiotemporal calcium transients in the PVT neurons during behavior were increased in the AIE mice compared to those in the counterpart control mice. Importantly, with the chemogenetic inhibition of the PVT neurons, we found alleviated the anxiety-like behavior in the AIE mice. The increased neuronal activities in the PVT of AIE mice was, at least partly, via the reduction of GLT1 (an astrocyte dominant glutamate transporter, known as EAAT2, slc1a2). Our non-invasive magnetic resonance spectroscopy (MRS) measures also showed an increase in glutamate/GABA ratio in the thalamic area including the PVT of the GLT1 conditional knock-down mice, which exhibited the heightened anxiety-like behavior. In addition, while the selective knock-out of GLT1 in the astrocytes of PVT in the alcohol naive mice induces anxiogenic phenotypes, the selective upregulation of GLT1 in the PVT astrocytes of the mice that were treated with AIE paradigm alleviated the anxiety-like behaviors as well. These findings highlight the significant role of PVT astrocytic GLT1 in the anxiogenic phenotype in adulthood induced by withdrawal from adolescent repeated ethanol exposure, suggesting that GLT1 in the PVT could serve as a therapeutic target for alcohol use disorder and comorbid emotional disorders.

neuroscience↗

bioRxiv2022

Species-wide genomics of kakapo provides transformational tools to accelerate recovery

The k[a]k[a]p[o] is a critically endangered, intensively managed, long-lived nocturnal parrot endemic to Aotearoa New Zealand. We generated and analyzed whole-genome sequence data for nearly all individuals living in early 2018 (169 individuals) to generate a high-quality species-wide genetic variant callset. We leverage extensive long-term metadata to quantify genome-wide diversity of the species over time and present new approaches using probabilistic programming, combined with a phenotype dataset spanning five decades, to disentangle phenotypic variance into environmental and genetic effects while quantifying uncertainty in small populations. We find associations for growth, disease susceptibility, clutch size, and egg fertility within genic regions previously shown to influence these traits in other species. Finally, we generate breeding values to predict phenotype and illustrate that active management over the past 45 years has maintained both genome-wide diversity and diversity in breeding values, and hence, evolutionary potential. We provide new pathways for informing future conservation management decisions for k[a]k[a]p[o], including prioritizing individuals for translocation and monitoring individuals with poor growth or high disease risk. Overall, by explicitly addressing the challenge of small sample size, we provide a template for the inclusion of genomic data that will be transformational for species recovery efforts around the globe.

genomics↗

bioRxiv2020

Characterising the methylome of Legionellalongbeachae serogroup 1 clinical isolates and assessing geo-temporal genetic diversity

Legionella longbeachae is an environmental bacterium that is commonly found in soil and composted plant material. In New Zealand (NZ) it is the most clinically significant Legionella species causing around two-thirds of all notified cases of Legionnaires disease. Here we report the sequencing and analysis of the geo-temporal genetic diversity of 54 L. longbeachae serogroup 1 (sg1) clinical isolates that were derived from cases from around NZ over a 22-year period, including one complete genome and its associated methylome. Our complete genome consisted of a 4.1 Mb chromosome and a 108 kb plasmid. The genome was highly methylated with two known epigenetic modifications, m4C and m6A, occurring in particular sequence motifs within the genome. Phylogenetic analysis demonstrated the 54 sg1 isolates belonged to two main clades that last shared a common ancestor between 108 BCE and 1608 CE. These isolates also showed diversity at the genome-structural level, with large-scale arrangements occurring in some regions of the chromosome and evidence of extensive chromosomal and plasmid recombination. This includes the presence of plasmids derived from recombination and horizontal gene transfer between various Legionella species, indicating there has been both intra-species and inter-species gene flow. However, because similar plasmids were found among isolates within each clade, plasmid recombination events may pre-empt the emergence of new L. longbeachae strains. Our high-quality reference genome and extensive genetic diversity data will serve as a platform for future work linking genetic, epigenetic and functional diversity in this globally important emerging environmental pathogen. Author SummaryLegionnaires disease is a serious, sometimes fatal pneumonia caused by bacteria of the genus Legionella. In New Zealand, the species that causes the majority of disease is Legionella longbeachae. Although the analyses of pathogenic bacterial genomes is an important tool for unravelling evolutionary relationships and identifying genes and pathways that are associated with their disease-causing ability, until recently genomic data for L. longbeachae has been sparse. Here, we conducted a large-scale genomic analysis of 54 L. longbeachae isolates that had been obtained from people hospitalised with Legionnaires disease between 1993 and 2015 from 8 regions around New Zealand. Based on our genome analysis the isolates could be divided into two main groups that persisted over time and last shared a common ancestor up to 1700 years ago. Analysis of the bacterial chromosome revealed areas of high modification through the addition of methyl groups and these were associated with particular DNA sequence motifs. We also found there have been large-scale rearrangements in some regions of the chromosome, producing variability between the different L. longbeacahe strains, as well as evidence of gene-flow between the various Legionella species via the exchange of plasmid DNA.

microbiology↗

bioRxiv2019

Investigation of the validity of two Bayesian ancestral state reconstruction models for estimating Salmonella transmission during outbreaks

Ancestral state reconstruction models use genetic data to characterize a group of organisms common ancestor. These models have been applied to salmonellosis outbreaks to estimate the number of transmissions between different animal species that share similar geographical locations, with animal host as the state. However, as far as we are aware, no studies have validated these models for outbreak analysis. In this study, salmonellosis outbreaks were simulated using a stochastic Susceptible-Infected-Recovered model, and the host population and transmission parameters of these simulated outbreaks were estimated using Bayesian ancestral state reconstruction models (discrete trait analysis (DTA) and structured coalescent (SC)). These models were unable to accurately estimate the number of transmissions between the host populations or the amount of time spent in each host population. The DTA model was inaccurate because it assumed the number of isolates sampled from each host population was proportional to the number of individuals infected within each host population. The SC model was inaccurate possibly because it assumed that each host populations effective population size was constant over the course of the simulated outbreaks. This study highlights the need for phylodynamic models that can take into consideration factors that influence the characteristics and behavior of outbreaks, e.g. changing effective population sizes, variation in infectious periods, intra-population transmissions, and disproportionate sampling of infected individuals.

epidemiology↗

bioRxiv2018

Which osteoarthritic gait features recover following Total Knee Replacement surgery?

BackgroundGait analysis can be used to measure variations in joint function in patients with knee osteoarthritis (OA), and is useful when observing longitudinal biomechanical changes following Total Knee Replacement (TKR) surgery. The Cardiff Classifier is an objective classification tool applied previously to examine the extent of biomechanical recovery following TKR. In this study, it is further developed to reveal the salient features that contribute to recovery towards healthy function.\n\nMethodsGait analysis was performed on 30 patients before and after TKR surgery, and 30 healthy controls. Median TKR follow-up time was 13 months. The combined application of principal component analysis (PCA) and the Cardiff Classifier defined 18 biomechanical features that discriminated OA from healthy gait. Statistical analysis tested whether these features were affected by TKR surgery and, if so, whether they recovered to values found for the controls.\n\nResultsThe Cardiff Classifier successfully discriminated between OA and healthy gait in all 60 cases. Of the 18 discriminatory features, only six (33%) were significantly affected by surgery, including features in all three planes of the ground reaction force (p<0.001), ankle dorsiflexion moment (p<0.001), hip adduction moment (p=0.003), and transverse hip angle (p=0.007). All but two (89%) of these features remained significantly different to those of the control group after surgery.\n\nConclusionsThis approach was able to discriminate gait biomechanics associated with knee OA. The ground reaction force provided the strongest discriminatory features. Despite increased gait velocity and improvements in self-reported pain and function, which would normally be clinical indicators of recovery, the majority of features were not affected by TKR surgery. This TKR cohort retained pre-operative gait patterns; reduced sagittal hip and knee moments, decreased knee flexion, increased hip flexion, and reduced hip adduction. The changes that were associated with surgery were predominantly found at the ankle and hip, rather than at the knee.

bioengineering↗