Biology subjects

Barrett, R.

Publications and source records attributed to Barrett, R..

7 recordsLinked to original sources

bioRxiv2025

TL1A overexpression in Crohn's Disease and mice alters Paneth cells and microbiota promoting ileal inflammation

Paneth cells regulate host-microbial homeostasis and defects in autophagy and host defense pathways have been associated with inflammatory bowel diseases (IBD). Genetic variants in TL1A (TNFSF15) and its receptor DR3 (TNFRSF25) have been associated with IBD. TL1A expression is increased in IBD patients, particularly in TL1A risk allele carriers. However, effects of TL1A on Paneth cells, resident microbiota, and development of ileitis remain unknown. TL1A overexpression in mice induces Paneth cell hyperplasia and morphological abnormalities preceding the development of ileitis. In Crohns disease (CD) patients, ileal TL1A expression was associated with abnormal Paneth cell phenotypes. We confirmed direct effects of TL1A on Paneth cells in human iPSC-derived human intestinal organoids and mouse Paneth cell-enriched organoids. Resident microbiota was required for TL1A-mediated Paneth cell dysfunction, and ileitis. Tl1a-tg mice were enriched in short chain fatty acid-producing bacteria and the metabolite acetate. Acetate supplementation in WT or Tl1a-tg mice caused ileal inflammation, suggesting that acetate is sufficient to cause ileitis. DR3-deficiency in Paneth cells resulted in Paneth cell abnormalities and microbiome composition changes. Our findings provide a mechanistic link between overexpression of TL1A in CD patients, Paneth cell dysfunction, and enrichment of acetate-producing bacteria and acetate that promotes ileal inflammation. Brief SummaryOverexpression of TL1A drives Paneth cell dysfunction in Crohns Disease and mice leading to microbial and metabolomic changes that promote small bowel inflammation.

immunology↗

bioRxiv2024

Destabilized host-parasite dynamics in newly founded populations

When species disperse into previously unoccupied habitats, new populations encounter unfamiliar species interactions such as altered parasite loads. Theory predicts that newly founded populations should exhibit destabilized eco-evolutionary fluctuations in infection rates and immune traits. However, to understand founder effects biologists typically rely on retrospective studies of range expansions, missing early-generation infection dynamics. To remedy this, we experimentally founded whole-lake populations of threespine stickleback. Infection rates were temporally stable in native source lakes. In contrast, newly founded populations exhibit destabilized host-parasite dynamics: high starting infection rates led to increases in a heritable immune trait (peritoneal fibrosis), suppressing infection rates. The resulting temporal auto-correlation between infection and immunity suggest that newly founded populations can exhibit rapid host-parasite eco-evolutionary dynamics.

evolutionary biology↗

bioRxiv2022

Estimating alpha, beta, and gamma diversity through deep learning

The reliable mapping of species richness is a crucial step for the identification of areas of high conservation priority, alongside other value considerations. This is commonly done by overlapping range maps of individual species, which requires dense availability of occurrence data or relies on assumptions about the presence of species in unsampled areas deemed suitable by environmental niche models. Here we present a deep learning approach that directly estimates species richness, skipping the step of estimating individual species ranges. We train a neural network model based on species lists from inventory plots, which provide ground truthing for supervised machine learning. The model learns to predict species richness based on spatially associated variables, including climatic and geographic predictors, as well as counts of available species records from online databases. We assess the empirical utility of our approach by producing independently verifiable maps of alpha, beta and gamma plant diversity at high spatial resolutions for Australia, a continent with highly contrasting diversity patterns. Our deep learning framework provides a powerful and flexible new approach for estimating biodiversity patterns.

evolutionary biology↗

bioRxiv2021

Automatic Detection of Larval Zebrafish ECG: Computational Tool for High-throughput Cardiac Activity Analysis

Automatic analysis of larval zebrafish electrocardiographs (ECG) is essential for high-throughput measurements in environmental toxicity assays, cardiotoxicity measurements and drug screening. We have developed a MATLAB based software is built on methods that have previously been used to analyze human ECG, such as the Pan-Tompkins algorithm and Wavelet. For the first time these sophisticated tools have been applied to larval zebrafish ECG to automatically characterize the heart-beat waveforms. The ability of the automated algorithm to detect the QT interval for both normal and pharmacologically altered larval ECG is found and compared to previously used software that is built into LabChart(R) (AD Instruments). Using wavelet transforms it is shown that the typical larval ECG features are within the frequency range of 1 to 31 Hz. It is also shown that the automated software is capable of detecting QTc (heartrate corrected heartbeat interval) changes within pharmacologically altered zebrafish larval ECG. The automated process is a significant improvement on the approaches that were previously applied to the zebrafish ECG. The automated process described here that is based on established techniques of analyzing ECG can sensitively measure pharmacologically induced changes in the ECG. The novel, automated software is faster, more sensitive at detecting ECG changes and less dependent on user involvement, thus minimizing user error and human bias. The automated process can also be applied to human ECG.

physiology↗

bioRxiv2020

A nonsense mutation in TFEC is the likely cause of the recessive piebald phenotype in ball pythons ( Python regius)

Captive-bred ball pythons (Python regius) represent a powerful model system for studying the genetic basis of colour variation and Mendelian phenotypes in vertebrates. Although hundreds of Mendelian phenotypes (colour morphs) affecting colouration and patterning have been described for ball pythons, the genes causing these colour morphs remain unknown. Here, we used crowdsourcing of samples from commercial ball python breeders to investigate the genetic basis of a classic phenotype found in the pet trade, the piebald [characterized by dorsolateral patches of unpigmented (white) skin]. We used whole-genome sequencing of pooled samples followed by population genetic methods to delineate the genomic region containing the causal gene. We identified TFEC of the MIT-family of transcription factors as a candidate gene. Functional annotation of SNPs identified a nonsense mutation in TFEC, which we conclude is the likely causal variant for the piebald phenotype. Our work shows that ball python colour morphs have the potential to be an excellent model system for studying the genetic basis of pigment variation in vertebrates, and highlights how collaborations with commercial breeders can accelerate discoveries.

genomics↗

bioRxiv2020

Widespread agrochemicals differentially affect zooplankton biomass and community structure

Anthropogenic environmental change is causing habitat deterioration at unprecedented rates in freshwater ecosystems. Despite increasing more rapidly than other agents of global change, synthetic chemical pollution -including agrochemicals such as pesticides- has received relatively little attention in freshwater biotic assessments. Determining the effects of multiple agrochemicals on complex community and ecosystem properties remains a major challenge, requiring a cross-field integration of ecology and ecotoxicology. Using a large-scale array of experimental ponds, we investigated the response of zooplankton community properties (biomass, composition, diversity metrics) to the individual and joint presence of three widespread agrochemicals: the herbicide glyphosate, the neonicotinoid insecticide imidacloprid, and fertilisers. We tracked temporal variation in community biomass and structure (i.e., composition, diversity metrics) along single and combined pesticide gradients (each spanning eight levels), under low (mesotrophic) and high (eutrophic) nutrient-enriched conditions, and quantified (i) agrochemical interactions, (ii) response threshold concentrations, and (iii) community resistance and recovery. We found that major zooplankton groups differed in their sensitivity to pesticides: [≥]3 {micro}g/L imidacloprid impaired copepods, rotifers collapsed at glyphosate levels [≥]0.3 mg/L, whereas some cladocerans were highly tolerant to pesticide contamination. Glyphosate was the most influential driver of community properties, with biomass and community structure responding rapidly but recovering unequally over time. Zooplankton biomass showed little resistance when first exposed to glyphosate, but rapidly recovered and even increased with glyphosate concentration; in contrast, richness declined in more contaminated ponds but failed to recover. Our results show that the biomass of tolerant taxa compensated for the loss of sensitive species, conferring greater resistance upon subsequent exposure; a rare example of pollution-induced community tolerance in freshwater metazoans. Overall, zooplankton biomass appears to be more resilient to agrochemical pollution than community structure, yet all community properties measured in this study were affected at glyphosate levels below common water quality guidelines in North America.

ecology↗

bioRxiv2020

Color Data v2: a user-friendly, open-access database with hereditary cancer and hereditary cardiovascular conditions datasets

Publicly-available genetic databases promote data sharing and fuel scientific discoveries for the prevention, treatment, and management of disease. In 2018, we built Color Data, a user-friendly, open access database containing genotypic and self-reported phenotypic information from 50,000 individuals who were sequenced for 30 genes associated with hereditary cancer. In a continued effort to promote access to these types of data, we launched Color Data v2, an updated version of the Color Data database. This new release includes additional clinical genetic testing results from more than 18,000 individuals who were sequenced for 30 genes associated with hereditary cardiovascular conditions, as well as polygenic risk scores for breast cancer, coronary artery disease, and atrial fibrillation. In addition, we used self-reported phenotypic information to implement the following four clinical risk models: Gail Model for five-year risk of breast cancer, Claus Model for lifetime risk of breast cancer, simple office-based Framingham Coronary Heart Disease Risk Score for ten-year risk of coronary heart disease, and CHARGE-AF simple score for five-year risk of atrial fibrillation. These new features and capabilities are highlighted through two sample queries in the database. We hope that the broad dissemination of this data will help researchers continue to explore genotype-phenotype correlations and identify novel variants for functional analysis, enabling scientific discoveries in the field of population genomics. Database URL: https://data.color.com/

genetics↗