Biology subjects

Knox, N.

Publications and source records attributed to Knox, N..

6 recordsLinked to original sources

bioRxiv2026

Perinatal Exposure to Organophosphate Flame Retardants Induces Sex- and Hormone-Dependent Alterations in Anxiety, Memory, Neurotransmitter Content, and Hippocampal Gene Expression

Developmental exposure to organophosphate flame retardants (OPFRs) is a public health concern due to their endocrine-disrupting potential. We examined perinatal exposure to tris(1,3-dichloro-2-propyl) phosphate, triphenyl phosphate, and tricresyl phosphate in mice. Adult male and female offspring were assessed for memory and anxiety-like behavior. Dopamine and norepinephrine were quantified in the hippocampus and prefrontal cortex (PFC), and bulk RNA sequencing was conducted for the hippocampus. OPFR-treated females in high ovarian hormone states spent less time in the open field test (OFT) center, the Y-maze unknown arm, and with the displaced object in spatial object recognition (SOR) indicating increased anxiety-like behavior and impaired spatial memory. These females also illustrated improved memory on the short-term Barnes maze, and a trending improvement in the novel object recognition test. Females in low ovarian hormone states, demonstrated a trend in center OFT exploration. OPFR-treated males displayed disruption in memory in the SOR and the short- and long-term Barnes maze. Perinatal OPFR reduced hippocampal dopamine in males and altered prefrontal dopamine in females in a hormone-dependent manner. OPFR-treated females in high ovarian hormones states demonstrated a trending decrease in PFC norepinephrine. Perinatal OPFR treatment caused differential gene expression in 121 individual genes and alteration to functional modules related to RNA processing, cellular metabolism, and extracellular organization. Hormone status also affected gene OPFR-induced altered expression, with similarity between males and high ovarian hormone state females. Our findings suggest that perinatal OPFR exposure causes widespread, sex specific, and hormone dependent disruptions in behavior, neurochemistry, and gene expression in adulthood. HighlightsO_LIAnxiety-like behavior in OPFR-treated females varied with ovarian hormone status C_LIO_LIHigh ovarian hormone OPFR females showed task-dependent changes in memory C_LIO_LIMales displayed impaired spatial memory following perinatal OPFR treatment C_LIO_LIPerinatal OPFR modifies hippocampal and prefrontal dopamine and norepinephrine C_LIO_LIOPFR treatment altered individual gene and functional gene module expression C_LI

pharmacology and toxicology↗

bioRxiv2024

The Interaction Of Diet-Induced Obesity And Chronic Stress In A Mouse Model Of Menopause

Menopause is characterized by the cessation of ovarian hormone production. During postmenopause, cisgender women face increased risks of obesity, cognitive decline, and mood disorder. Mood disorders are associated with exposure to chronic stress. We investigated the combined effects of a high-fat diet (HFD) and chronic stress exposure in a mouse model of menopause using 4-vinylcyclohexene diepoxide (VCD), a selective ovotoxicant that gradually depletes ovarian follicles and hormones. Starting at 6 months, 82 female WT C57BL/6J mice received saline or VCD (130 mg/kg i.p.) 5 days per week for 3 weeks. One month after injection, mice were fed either low-fat diet (LFD) or HFD for 8 weeks followed by 6 weeks of chronic variable mild stress (CVMS). Post-CVMS, mice were either processed for gene expression of the anterodorsal BNST or behavior tests to assess cognitive and anxiety-related behaviors. Plasma samples were collected to analyze metabolic hormones and corticosterone levels. VCD-treated HFD-fed mice had higher fat and body mass, and elevated fasting glucose levels compared to controls and more pronounced avoidance behaviors and cognitive impairments. LFD-fed, VCD-treated mice exhibited less exploration of novel objects and open spaces compared to OIL and HFD counterparts. VCD elevated corticosterone levels on LFD and increased BNST Pacap gene expression on HFD. These findings highlight cognitive repercussions of estrogen deficiency and suggest a potential protective effect of a HFD against some of the adverse outcomes associated with menopause. Our study emphasizes the importance of considering dietary and hormonal interactions in the development of therapeutic strategies.

neuroscience↗

bioRxiv2023

Proportionality-based association metrics in count compositional data

MotivationCompositional data comprise vectors that describe the constituent parts of a whole. Data arising from various -omics platforms such as 16S and RNA-sequencing are compositional in nature. However, correlations between features on raw counts have no meaningful interpretation. Metrics of proportionality were formulated to address this problem. However, there is an inherent bias that arises when calculating these metrics empirically on count-based measures due to variability in read depths. ResultsWe quantify the bias introduced by empirically calculating proportionality-based association metrics in count data. Additionally, we propose a means of estimating these metrics within a logit-normal multinomial model in pursuit of more accurate estimates. The model-based estimates are shown to outperform empirical estimates in simulated data, and are additionally applied to a mouse embryonic stem-cell single-cell sequencing dataset as well as a pediatric-onset multiple sclerosis metagenomic dataset. Availability and ImplementationAn R package is available at https://CRAN.R-project.org/package=countprop. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2020

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel

BioHansel performs high-resolution genotyping of bacterial isolates by identifying phylogenetically informative single nucleotide polymorphisms (SNPs), also known as canonical SNPs, in whole genome sequencing (WGS) data. The application uses a fast k-mer matching algorithm to map pathogen WGS data to canonical SNPs contained in hierarchically structured schemas and assigns genotypes based on the detected SNP profile. Using modest computing resources, BioHansel efficiently types isolates from raw sequence reads or assembled contigs in a matter of seconds, making it attractive for use by public health, food safety, environmental, and agricultural authorities that wish to apply WGS methodologies for their surveillance, diagnostics, and research programs. BioHansel currently provides canonical SNP genotyping schemas for four prevalent Salmonella serovars--Typhi, Typhimurium, Enteritidis and Heidelberg--as well as a schema for Mycobacterium tuberculosis. Users can also supply their own schemas for genotyping other organisms. BioHansels quality assurance system assesses the validity of the genotyping results and can identify low quality data, contaminated datasets, and misidentified organisms. BioHansel is targeted to support surveillance, source attribution, risk assessment, diagnostics, and rapid screening for public health purposes, such as product recalls. BioHansel is an open source application with packages available for PyPI, Conda, and the Galaxy workflow manager. In summary, BioHansel performs efficient, rapid, accurate, and high-resolution classification of bacterial genomes from sequence reads or assembled contigs on standard computing hardware. BioHansel is suitable for use as a general research tool as well as in fully operationalized WGS workflows at the front lines of infectious disease surveillance, diagnostics, and outbreak investigation and response. Impact statementPublic health, food safety, environmental, and agricultural authorities are currently engaged in a global effort to incorporate whole genome sequencing technologies into their infectious disease research, surveillance, and outbreak investigation programs. Its widespread adoption, however, has been impeded by two major obstacles: the need for high performance computing to generate results and the expert knowledge required to interpret and communicate those results. BioHansel addresses these limitations by rapidly genotyping pathogens from whole genome sequence data in an accurate, simple, familiar, and easily sharable manner using standard computing resources. BioHansel provides a compact and readily interpretable genotype based on canonical SNP genotyping schemas. BioHansels genotyping nomenclature encodes the pathogens position in its population structure, which simplifies and facilitates its comparison with actively circulating strains and historical strains. The genotyping information provided by BioHansel can identify points of intervention to prevent the spread of pathogenic bacteria, screen for the presence of priority pathogens, and perform source attribution and risk assessment. Thus, BioHansel serves as a readily accessible and powerful WGS method, implementable on a laptop, for genotyping pathogens to detect, monitor, and control the emergence and spread of infectious disease through surveillance, screening, diagnostics, and outbreak investigation and response activities. Data summaryBioHansel is a Python 3 application available as PyPI, Conda Galaxy Tool Shed packages. It is an open source application distributed under the Apache License, Version 2.0. Source code is available at https://github.com/phac-nml/biohansel. The BioHansel user guide is available at https://bio-hansel.readthedocs.io/en/readthedocs/. Supplementary Materials are available at https://github.com/phac-nml/biohansel-manuscript-supplementary-data. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

bioinformatics↗

bioRxiv2017

Plasmid Profiler: Comparative Analysis Of Plasmid Content In WGS Data

SummaryComparative analysis of bacterial plasmids from whole genome sequence (WGS) data generated from short read sequencing is challenging. This is due to the difficulty in identifying contigs harbouring plasmid sequence data, and further difficulty in assembling such contigs into a full plasmid. As such, few software programs and bioinformatics pipelines exist to perform comprehensive comparative analyses of plasmids within and amongst sequenced isolates. To address this gap, we have developed Plasmid Profiler, a pipeline to perform comparative plasmid content analysis without the need for de novo assembly. The pipeline is designed to rapidly identify plasmid sequences by mapping reads to a plasmid reference sequence database. Predicted plasmid sequences are then annotated with their incompatibility group, if known. The pipeline allows users to query plasmids for genes or regions of interest and visualize results as an interactive heat map.\n\nAvailability and ImplementationPlasmid Profiler is freely available software released under the Apache 2.0 open source software license. A stand-alone version of the entire Plasmid Profiler pipeline is available as a Docker container at https://hub.docker.com/r/phacnml/plasmidprofiler_0_1_6/.\n\nThe conda recipe for the Plasmid R package is available at: https://anaconda.org/bioconda/r-plasmidprofiler\n\nThe custom Plasmid Profiler R package is also available as a CRAN package at https://cran.r-project.org/web/packages/Plasmidprofiler/index.html\n\nGalaxy tools associated with the pipeline are available as a Galaxy tool suite at https://toolshed.g2.bx.psu.edu/repository?repository_id=55e082200d16a504\n\nThe source code is available at: https://github.com/phac-nml/plasmidprofiler\n\nThe Galaxy implementation is available at: https://github.com/phac-nml/plasmidprofiler-galaxy\n\nContactEmail: gary.vandomselaar@canada.ca\n\nAddress: National Microbiology Laboratory, Public Health Agency of Canada, 1015 Arlington Street, Winnipeg, Manitoba, Canada\n\nSupplementary informationDocumentation: http://plasmid-profiler.readthedocs.io/en/latest/

bioinformatics↗

bioRxiv2016

SNVPhyl: A Single Nucleotide Variant Phylogenomics pipeline for microbial genomic epidemiology

MotivationThe recent widespread application of whole-genome sequencing (WGS) for microbial disease investigations has spurred the development of new bioinformatics tools, including a notable proliferation of phylogenomics pipelines designed for infectious disease surveillance and outbreak investigation. Transitioning the use of WGS data out of the research lab and into the front lines of surveillance and outbreak response requires user-friendly, reproducible, and scalable pipelines that have been well validated.\n\nResultsSNVPhyl (Single Nucleotide Variant Phylogenomics) is a bioinformatics pipeline for identifying high-quality SNVs and constructing a whole genome phylogeny from a collection of WGS reads and a reference genome. Individual pipeline components are integrated into the Galaxy bioinformatics framework, enabling data analysis in a user-friendly, reproducible, and scalable environment. We show that SNVPhyl can detect SNVs with high sensitivity and specificity and identify and remove regions of high SNV density (indicative of recombination). SNVPhyl is able to correctly distinguish outbreak from non-outbreak isolates across a range of variant-calling settings, sequencing-coverage thresholds, or in the presence of contamination.\n\nAvailabilitySNVPhyl is available as a Galaxy workflow, Docker and virtual machine images, and a Unix-based command-line application. SNVPhyl is released under the Apache 2.0 license and available at http://snvphyl.readthedocs.io/ or at https://github.com/phac-nml/snvphyl-galaxy.

bioinformatics↗