Biology subjects

Morse, A. M.

Publications and source records attributed to Morse, A. M..

3 recordsLinked to original sources

bioRxiv2021

GAIT-GM integrative cross-omics analyses reveal cholinergic defects in a C. elegans model of Parkinson's disease

BackgroundParkinsons disease (PD) is a disabling neurodegenerative disorder in which multiple cell types, including dopaminergic and cholinergic neurons, are affected. The mechanisms of neurodegeneration in PD are unknown, limiting the development of therapies directed at disease-relevant molecular targets. C. elegans is a genetically tractable model system that can be used to disentangle disease mechanisms in complex diseases such as PD. Such mechanisms can be studied combining high-throughput molecular profiling technologies such as transcriptomics and metabolomics. However, the integrative analysis of multi-omics data in order to unravel disease mechanisms is a challenging task without advanced bioinformatics training. Galaxy, a widely-used resource for enabling bioinformatics analysis by the broad scientific community, has poor representation of multi-omics integration pipelines. ResultsWe present the integrative analysis of gene expression and metabolite levels of a C. elegans PD model using GAIT-GM, a new Galaxy tool for multi-omics data analysis. Using GAIT-GM, we discovered an association between branched-chain amino acid metabolism and cholinergic neurons in the C. elegans PD model. An independent follow-up experiment uncovered cholinergic neurodegeneration in the C. elegans model that is consistent with cholinergic cell loss observed in PD. ConclusionGAIT-GM is an easy to use Galaxy-based tool for generating novel testable hypotheses of disease mechanisms involving gene-metabolite relationships.

neuroscience↗

bioRxiv2021

Ozone sensitivity of diverse maize genotypes is associated with differences in gene regulation, not gene content

The maize pangenome has demonstrate large amounts of presence/absence variation and it has been hypothesized that presence/absence variation contributes to stress response. To uncover whether the observed genetic variation in physiological response to elevated ozone (a secondary air pollutant that causes significant crop yield losses) concentration is due to variation in genic content, and/or variation in gene expression, we examine the impact of sustained elevated ozone concentration on the leaf tissue from 5 diverse maize inbred genotypes (B73, Mo17, Hp301, C123, NC338). Analysis of long reads from the transcriptomes of the 10 conditions found expressed genes in the leaf are part of the shared genome, with 94.5% of expressed genes from syntenic loci. Quantitative analysis of short reads from 120 plants (twelve from each condition) found limited transcriptional response to sustained ozone stress in the ozone resistant B73 genotype (151 genes), while more than 3,300 genes were significantly differentially expressed in the more sensitive NC338 genotype. The genes underpinning the divergence of B73 from the other 4 genotypes implicates ethylene signaling consistent with some findings in Arabidopsis. For the 82 of the 83 genes differentially expressed among all 5 genotypes and the 788 of 789 genes differentially expressed in 4 genotypes (excluding B73) in sensitivity to ozone is associated with oxidative stress tolerance being associated with a weaker response to a reactive oxygen species (ROS) signal and suggests that genetic variation in downstream processes is key to ozone tolerance.

genomics↗

bioRxiv2021

Long-Term Metabolomics Reference Material

The use of quality control samples in metabolomics ensures data quality, reproducibility and comparability between studies, analytical platforms and laboratories. Long-term, stable and sustainable reference materials (RMs) are a critical component of the QA/QC system, however, the limited selection of currently available matrix matched RMs reduce their applicability for widespread use. To produce a RM in any context, for any matrix that is robust to changes over the course of time we developed IBAT (Iterative Batch Averaging meThod). To illustrate this method, we generated 11 independently grown E. coli batches and made a RM over the course of 10 IBAT iterations. We measured the variance of these materials by NMR and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as food source to produce a C. elegans RM for a metabolomics experiment. The metabolite extraction of this material alongside 41 independently grown individual C. elegans samples of the same genotype, allowed to estimate the proportion of sample variation in pre-analytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

biochemistry↗