Biology subjects

McIntyre, A.

Publications and source records attributed to McIntyre, A..

3 recordsLinked to original sources

bioRxiv2020

Developmental exposure to the organochlorine pesticide dieldrin causes male-specific exacerbation of α-synuclein-preformed fibril-induced toxicity and motor deficits

Human and animal studies have shown that exposure to the organochlorine pesticide dieldrin is associated with increased risk of Parkinsons disease (PD). Previous work showed that developmental dieldrin exposure increased neuronal susceptibility to MPTP toxicity in male C57BL/6 mice, possibly via changes in dopamine (DA) packaging and turnover. However, the relevance of the MPTP model to PD pathophysiology has been questioned. We therefore studied dieldrin-induced neurotoxicity in the -synuclein (-syn)-preformed fibril (PFF) model, which better reflects the -syn pathology and toxicity observed in PD pathogenesis. Specifically, we used a "two-hit" model to determine whether developmental dieldrin exposure increases susceptibility to -syn PFF-induced synucleinopathy. Dams were fed either dieldrin (0.3 mg/kg, every 3-4 days) or vehicle corn oil starting 1 month prior to breeding and continuing through weaning of pups at postnatal day 22. At 12 weeks of age, male and female offspring received intrastriatal PFF or control saline injections. Consistent with the male-specific increased susceptibility to MPTP, our results demonstrate that developmental dieldrin exposure exacerbates PFF-induced toxicity in male mice only. Specifically, in male offspring, dieldrin exacerbated PFF-induced motor deficits on the challenging beam and increased DA turnover in the striatum 6 months after PFF injection. However, male offspring showed neither exacerbation of phosphorylated -syn (pSyn) aggregation in the substantia nigra (SN) at 1 or 2 months post-PFF injection, nor exacerbation of PFF-induced TH and NeuN loss in the SN 6 months post-PFF injection. Collectively, these data indicate that developmental dieldrin exposure produces a male-specific increase in neuronal vulnerability to synucleinopathy. This sex-specific result is consistent with both previous work in the MPTP model, our previously reported sex-specific effects of this exposure paradigm on the male and female epigenome, and the higher prevalence and more severe course of PD in males. The novel two-hit environmental toxicant/PFF exposure paradigm established in this project can be used to explore the mechanisms by which other PD-related exposures alter neuronal vulnerability to synucleinopathy in sporadic PD. HighlightsO_LIDevelopmental dieldrin exposure increases -syn-PFF-induced motor deficits C_LIO_LIDevelopmental dieldrin exposure increases PFF-induced deficits in DA handling C_LIO_LIDevelopmental dieldrin exposure does not affect PFF-induced loss of nigral neurons C_LIO_LIThis is a novel paradigm modeling how environmental factors increase risk of PD C_LIO_LIFemale mice show PFF-induced pathology, but no PFF-induced motor deficits. C_LI

neuroscience↗

bioRxiv2019

Evolutionary Dynamics of microRNA target sites across vertebrate evolution

MicroRNAs (miRNAs) control the abundance of the majority of the vertebrate transcriptome. The recognition sequences, or target sites, for bilaterian miRNAs are found predominantly in the 3' untranslated regions (3'UTRs) of mRNAs, and are amongst the most highly conserved motifs within 3'UTRs. However, little is known regarding the evolutionary pressures that lead to loss and gain of such target sites. Here, we quantify the selective pressures that act upon miRNA target sites. Notably, selective pressure extends beyond deeply conserved binding sites to those that have undergone recent substitutions. Our approach reveals that even amongst ancient animal miRNAs, which exert the strongest selective pressures on 3'UTR sequences, there are striking differences in patterns of target site evolution between miRNAs. Considering only ancient animal miRNAs, we find three distinct miRNA groups, each exhibiting characteristic rates of target site gain and loss during mammalian evolution. The first group both loses and gains sites rarely. The second group shows selection only against site loss, with site gains occurring at a neutral rate, whereas the third loses and gains sites at neutral or above expected rates. Furthermore, mutations that alter strength of existing target sites are disfavored. Applying our approach to individual transcripts reveals variation in the distribution of selective pressure across the transcriptome and between miRNAs, ranging from strong selection acting on a small subset of targets of some miRNAs, to weak selection on many targets for other miRNAs. miR-20 and miR-30, and many other miRNAs, exhibit broad, deeply conserved targeting, while several other comparably ancient miRNAs show a lack of selective constraint, and a small number, including mir-146, exhibit striking evidence of rapidly evolving target sites. Our approach adds valuable perspective on the evolution of miRNAs and their targets, and can also be applied to characterize other 3'UTR regulatory motifs.\n\nAUTHOR SUMMARYGene regulation typically involves two components; the first is a regulator, and the second is a target that the regulator acts on. Examining the evolution of both components allows us to make inferences regarding how gene regulation originated and how it is changing over time. One gene regulatory system, which is widespread in mammals and other animals, relies upon regulators known as microRNAs (miRNAs), which recognize target sites within mRNAs, leading to post-transcriptional repression of the targeted mRNAs. This system has been studied extensively from the perspective of the evolution of the miRNA regulators. Moreover, a subset of the target sites themselves are known to be deeply conserved. Here, we have systematically examined the rate at which target sites for individual miRNAs are created and destroyed across mammalian evolution. We find that mutations that strengthen or weaken existing target sites are strongly disfavored. For ancient microRNAs, even recently evolved target sites are under strong selective constraint, and as miRNAs age, they tend to initially experience selection only against loss of existing target sites, and later accumulate strong selection against gain of novel target sites.

evolutionary biology↗

bioRxiv2017

Comprehensive Benchmarking and Ensemble Approaches for Metagenomic Classifiers

BackgroundOne of the main challenges in metagenomics is the identification of microorganisms in clinical and environmental samples. While an extensive and heterogeneous set of computational tools is available to classify microorganisms using whole genome shotgun sequencing data, comprehensive comparisons of these methods are limited. In this study, we use the largest (n=35) to date set of laboratory-generated and simulated controls across 846 species to evaluate the performance of eleven metagenomics classifiers. We also assess the effects of filtering and combining tools to reduce the number of false positives.\n\nResultsTools were characterized on the basis of their ability to (1) identify taxa at the genus, species, and strain levels, (2) quantify relative abundance measures of taxa, and (3) classify individual reads to the species level. Strikingly, the number of species identified by the eleven tools can differ by over three orders of magnitude on the same datasets. However, various strategies can ameliorate taxonomic misclassification, including abundance filtering, ensemble approaches, and tool intersection. Indeed, leveraging tools with different heuristics is beneficial for improved precision. Nevertheless, these strategies were often insufficient to completely eliminate false positives from environmental samples, which are especially important where they concern medically relevant species and where customized tools may be required.\n\nConclusionsThe results of this study provide positive controls, titrated standards, and a guide for selecting tools for metagenomic analyses by comparing ranges of precision and recall. We show that proper experimental design and analysis parameters, including depth of sequencing, choice of classifier or classifiers, database size, and filtering, can reduce false positives, provide greater resolution of species in complex metagenomic samples, and improve the interpretation of results.

genomics↗