Biology subjects

Carter, G.

Publications and source records attributed to Carter, G..

8 recordsLinked to original sources

bioRxiv2023

Opportunistic evidence for impacts of Staphylococcus infection on social integration in vampire bats

Social integration can affect an individuals susceptibility to infectious disease. Conversely, infectious disease can reduce an individuals social activity. Yet, it remains unclear to what extent short-term infections can inhibit social integration and the formation of new relationships. During a previous study on relationship formation, we captured 21 female common vampire bats (Desmodus rotundus) from different wild populations and housed them together in captivity. Upon introduction, we observed an unplanned outbreak of bacterial infections that caused cutaneous lesions. After treating infected bats with an antibiotic, five bats recovered, but four others suffered lasting injuries. Given that observations of how natural infections alter relationship formation are rare, we analyzed how allogrooming rates changed over time among familiar and new dyads consisting of the nine infected and 12 asymptomatic bats. We found that (1) infected bats demonstrated reduced activity and social behavior, (2) more severely infected bats gave and received less allogrooming than asymptomatic bats, (3) the effect of infection was larger for new dyads relative to familiar dyads, and (4) this effect decreased as infected bats recovered and new dyads became more familiar. These opportunistic observations were consistent with the hypothesis that short-term infections can impact the formation of new relationships.

animal behavior and cognition↗

bioRxiv2022

Enteroviral 2C protein is an RNA-stimulated ATPase and uses a two-step mechanism for binding to RNA and ATP

The enteroviral 2C protein is a therapeutic target, but the absence of a mechanistic framework for this enzyme limits our understanding of inhibitor mechanisms. Here we use poliovirus 2C and a derivative thereof to elucidate the first biochemical mechanism for this enzyme and confirm the applicability of this mechanism to other members of the enterovirus genus. Our biochemical data are consistent with a dimer forming in solution, binding to RNA, which stimulates ATPase activity by increasing the rate of hydrolysis without impacting affinity for ATP substantially. Both RNA and DNA bind to the same or overlapping site on 2C, driven by the phosphodiester backbone, but only RNA stimulates ATP hydrolysis. We propose that RNA binds to 2C driven by the backbone, with reorientation of the ribose hydroxyls occurring in a second step to form the catalytically competent state. 2C also uses a two-step mechanism for binding to ATP. Initial binding is driven by the and {beta} phosphates of ATP. In the second step, the adenine base and other substituents of ATP are used to organize the active site for catalysis. These studies provide the first biochemical description of determinants driving specificity and catalytic efficiency of a picornaviral 2C ATPase.

biochemistry↗

bioRxiv2022

A 3D bioengineered neural tissue model generated from patient-derived iPSCs develops Alzheimer's disease-related phenotypes

BackgroundCurrent models to study Alzheimers disease (AD) include cell cultures and animal models. Human diseases, however, are often poorly reproduced in animal models. Developing techniques to differentiate human brain cells from induced pluripotent stem cells (iPSCs) provides a novel approach to studying AD. Three-dimensional (3D) cultures to model AD are represented by organoids, neurospheroids, and scaffold-based cultures. Some AD-related phenotypes have been identified across 3D models [1]. However, to our knowledge, none of these studies could recapitulate several AD-related hallmarks in one single model and establish a temporal relation among them. Furthermore, to date, the transcriptomic features of these 3D models have not been compared with those of human AD brains. These data are, in our opinion, key to understanding the pertinency of these models for studying AD-related pathomechanisms over time. MethodsWe developed a 3D bioengineered model of iPSC-derived neural tissue that combines a porous scaffold composed of silk fibroin protein with an intercalated collagen hydrogel to support the growth of neurons and glial cells into complex and functional networks. This biomaterial scaffold, designed to match the mechanical properties of brain tissue, can support 3D neural cultures for an extended time without necrosis, a fundamental requisite for aging studies. We have optimized our protocol by seeding neural precursor cells (NPCs) into these scaffolds. NPC-derived cultures were generated from iPSC lines obtained from two subjects carrying the familial AD (FAD) APP London mutation, two well-studied control lines, and an isogenic control. Cultures were analyzed at 2 and 4.5 months. ResultsAn elevated A{beta}42/40 ratio was detected in conditioned media from FAD cultures at both time points, as previously reported in 2D cultures derived from the same FAD lines. However, extracellular A{beta}42 deposition and enhanced neuronal excitability were observed in FAD culture only at 4.5 months. The increased excitability of FAD cultures correlated with extracellular A{beta}42 deposition but not with soluble A{beta}42/40 ratio levels, as they were similar at both time points. These data suggest that extracellular A{beta} deposition may trigger enhanced network activity. Notably, neuronal hyperexcitability has been described in AD patients early in the disease. Transcriptomic analysis revealed the deregulation of multiple gene sets in FAD samples. Notably, such alterations were similar to those observed in human AD brains in a large study that performed a co-expression meta-analysis of harmonized data from Accelerating Medicines Partnership for Alzheimers Disease (AMP-AD) across three independent cohorts. ConclusionsOur 3D tissue model supports the differentiation of healthy iPSC-derived cultures in a porous silk-collagen composite sponge with an optically clear central region. This design facilitates nutrient delivery to meet the metabolic demand of long-term cultures. These data provide evidence that our bioengineered model from patient-derived FAD iPSCs develops time-dependent AD-related phenotypes and establishes a temporal relation among them. Furthermore, FAD iPSC-derived neuronal tissue recapitulates transcriptomic features of AD patients. Thus, our bioengineered neural tissue represents a unique tool to model AD-related pathomechanisms over time, with several advantages compared to the existing models.

neuroscience↗

bioRxiv2020

Simultaneous monitoring of the same animals with PIT-tags and sensor nodes causes no system interference

Recent technological advances have multiplied the variety of biologgers used in wildlife research, particularly with small-bodied animals. PIT-tags have been used for decades to log visits of tagged animals at reader-equipped artificial feeders or roost boxes. More recently, novel miniaturized sensor nodes can collect data on social encounters among tagged individuals in any location. Combining these systems allows researchers to gather high-resolution foraging data on certain individuals from their long-term PIT-tagged animal populations. However, there can be a risk of interference among tracking systems. Here we tested whether radio-frequency identification (RFID) is hampered when an additional biologging sensor node is attached to the same animal, and whether data transmission by the sensor node is affected by the RFID antenna. The combination of this RFID-system and a wireless biologging network works without error, suggesting that systems using similar mechanisms should also work together properly. The combination of long-term monitoring with PIT tags and short-term tracking with biologging sensor nodes creates exciting new opportunities to gather rich social data from individuals not present at RFID reader stations.View Full Text

zoology↗

bioRxiv2019

Identifying and ranking potential driver genes of Alzheimer's Disease using multi-view evidence aggregation

MotivationLate onset Alzheimers disease (LOAD) is currently a disease with no known effective treatment options. To address this, there have been a recent surge in the generation of multi-modality data (Hodes and Buckholtz, 2016; Mueller et al., 2005) to understand the biology of the disease and potential drivers that causally regulate it. However, most analytic studies using these data-sets focus on uni-modal analysis of the data. Here we propose a data-driven approach to integrate multiple data types and analytic outcomes to aggregate evidences to support the hypothesis that a gene is a genetic driver of the disease. The main algorithmic contributions of our paper are: i) A general machine learning framework to learn the key characteristics of a few known driver genes from multiple feature-sets and identifying other potential driver genes which have similar feature representations, and ii) A flexible ranking scheme with the ability to integrate external validation in the form of Genome Wide Association Study (GWAS) summary statistics. While we currently focus on demonstrating the effectiveness of the approach using different analytic outcomes from RNA-Seq studies, this method is easily generalizable to other data modalities and analysis types. ResultsWe demonstrate the utility of our machine learning algorithm on two benchmark multi-view datasets by significantly outperforming the baseline approaches in predicting missing labels. We then use the algorithm to predict and rank potential drivers of Alzheimers. We show that our ranked genes show a significant enrichment for SNPs associated with Alzheimers, and are enriched in pathways that have been previously associated with the disease. AvailabilitySource code and link to all feature sets is availabile at https://github.com/Sage-Bionetworks/EvidenceAggregatedDriverRanking. Contactben.logsdon@sagebionetworks.org

bioinformatics↗

bioRxiv2018

Nepotism masks evidence for reciprocity in cooperation networks

Nepotism and reciprocity are not mutually exclusive explanations for cooperation, because helping decisions can depend on both kinship cues and past reciprocal help. The importance of these two factors can therefore be difficult to disentangle using observational data. We developed a resampling procedure for inferring the statistical power to detect observational evidence of nepotism and reciprocity. We first applied this procedure to simulated datasets resulting from perfect reciprocity, where the probability and duration of helping events from individual A to B equaled that from B to A. We then assessed how the probability of detecting correlational evidence of reciprocity was influenced by (1) the number of helping observations and (2) varying degrees of simultaneous nepotism. Last, we applied the same analysis to empirical data on food sharing in vampire bats and allogrooming in mandrills and Japanese macaques. We show that at smaller sample sizes, the effect of kinship was easier to detect and the relative role of kinship was overestimated compared to the effect of reciprocal help in both simulated and empirical data, even with data simulating perfect reciprocity and imperfect nepotism. We explain the causes and consequences of this difference in power for detecting the roles of kinship versus reciprocal help. To compare the relative importance of genetic and social relationships, we therefore suggest that researchers measure the relative reliability of both coefficients in the model by plotting these coefficients and their detection probability as a function of sampling effort. We provide R scripts to allow others to do this power analysis with their own datasets.

animal behavior and cognition↗

bioRxiv2017

Real-time genomic and epidemiological investigation of a multi-institution outbreak of KPC-producing Enterobacteriaceae: a translational study

BackgroundUntil recently, KPC-producing Enterobacteriaceae were rarely identified in Australia. Following an increase in the number of incident cases across the state of Victoria, we undertook a real-time combined genomic and epidemiological investigation. The scope of this study included identifying risk factors and routes of transmission, and investigating the utility of genomics to enhance traditional field epidemiology for informing management of established widespread outbreaks.\n\nMethods and FindingsAll KPC-producing Enterobacteriaceae isolates referred to the state reference laboratory from 2012 onwards were included. Whole-genome sequencing (WGS) was performed in parallel with a detailed descriptive epidemiological investigation of each case, using Illumina sequencing on each isolate. This was complemented with PacBio long-read sequencing on selected isolates to establish high-quality reference sequences and interrogate characteristics of KPC-encoding plasmids. Initial investigations indicated the outbreak was widespread, with 86 KPC-producing Enterobacteriaceae isolates (K. pneumoniae 92%) identified from 35 different locations across metropolitan and rural Victoria between 2012-2015. Initial combined analyses of the epidemiological and genomic data resolved the outbreak into distinct nosocomial transmission networks, and identified healthcare facilities at the epicentre of KPC transmission. New cases were assigned to transmission networks in real-time, allowing focussed infection control efforts. PacBio sequencing confirmed a secondary transmission network arising from inter-species plasmid transmission. Insights from Bayesian transmission inference and analyses of within-host diversity informed the development of state-wide public health and infection control guidelines, including interventions such as an intensive approach to screening contacts following new case detection to minimise unrecognised colonisation.\n\nConclusionsA real-time combined epidemiological and genomic investigation proved critical to identifying and defining multiple transmission networks of KPC Enterobacteriaceae, while data from either investigation alone were inconclusive. The investigation was fundamental to informing infection control measures in real-time and the development of state-wide public health guidelines on carbapenemase producing Enterobacteriaceae management.

genomics↗

bioRxiv2017

A Supervised Statistical Learning Approach For Accurate Legionella pneumophila Source Attribution During Outbreaks

Public health agencies are increasingly relying on genomics during Legionnaires disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on\n\nL. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia between 1994 and 2014. This collection included one of the largest reported Legionnaires disease outbreaks, involving 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures, and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires disease within a UK hospital and observed model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.

microbiology↗