Biology subjects

McIntyre, L. M.

Publications and source records attributed to McIntyre, L. M..

9 recordsLinked to original sources

bioRxiv2025

Multiparent Recombinant Inbred lines crossed to a tester provide novel insights into sources of cis and trans regulation of gene expression

We propose crossing multi-parent recombinant-inbred-lines (RILs) to a common tester and measuring allele specific gene expression in the offspring. Testing whether allelic imbalance between two RIL x Tester crosses is equal, is a test of cis or trans depending on the RIL alleles compared. The study design also enables to separate two sources of trans variation, genetic and environmental, detected via interactions with cis effects. Examining these components of regulatory variation, we demonstrate this approach in a long-read RNA-seq experiment in female abdominal tissue at two time points in Drosophila melanogaster. Among the 40% of all loci that show evidence of genetic variation in cis, trans effects due to the environment are detectable in 31% of loci and trans effects due to genetic background are detectable in 19% of loci with little overlap in sources of trans variation. The loci identified in this study are associated with loci previously reported to exhibit genetic variation in gene expression in a range of tissues and large population samples, suggesting that there is consistent variation for genetic regulation of gene expression. We show that eleven loci in a QTL for thermotolerance, previously shown to differ in expression based on temperature, have evidence for regulation of gene expression regardless of the environment, including Cpr67B, a cuticular protein suggesting a potential functional role for standing variation in gene expression. This study provides a blueprint for efficiently identifying regulatory variation in gene expression, as the tester design maximizes cis variation and enables the efficient assessment of all pairs of RIL alleles relative to the tester, a much smaller study compared to the pairwise direct assessment.

genetics↗

bioRxiv2025

Spatial transcriptomics from pancreas and local draining lymph node tissue reveals a lymphotoxin-β signature in human type 1 diabetes

This study explores the inflammatory response observed in pancreata and pancreatic lymph node (pLN) samples obtained throughout the natural history of type 1 diabetes (T1D) including non-diabetic individuals and non-diabetic autoantibody positive individuals with high susceptibility using spatial transcriptomics (ST). Integration of ST with public single-cell RNA sequencing data enabled interrogation of transcriptional alterations in T1D pathogenesis across both tissues and cellular scales. In the T1D pancreas, we observed global upregulation of multiple inflammation-associated transcripts, including regenerating islet-derived (REG) family genes, complement factor 3 (C3), SOD2, and OLFM4, and highlighted cellular candidates potentially contributing to these signatures. Within the T1D pLN, we observed spatially restricted upregulation of lymphotoxin-{beta} (LTB) alongside follicular dendritic cell (FDC)-associated transcripts including FDCSP, CLU, and FCER2. Collectively, these findings highlight distinct inflammation signatures in the pancreas and regional pLN which can help inform the development of future therapeutic interventions.

immunology↗

bioRxiv2023

SQANTI3: curation of long-read transcriptomes for accurate identification of known and novel isoforms

The emergence of long-read RNA sequencing (lrRNA-seq) has provided an unprecedented opportunity to analyze transcriptomes at isoform resolution. However, the technology is not free from biases, and transcript models inferred from these data require quality control and curation. In this study, we introduce SQANTI3, a tool specifically designed to perform quality analysis on transcriptomes constructed using lrRNA-seq data. SQANTI3 provides an extensive naming framework to describe transcript model diversity in comparison to the reference transcriptome. Additionally, the tool incorporates a wide range of metrics to characterize various structural properties of transcript models, such as transcription start and end sites, splice junctions, and other structural features. These metrics can be utilized to filter out potential artifacts. Moreover, SQANTI3 includes a Rescue module that prevents the loss of known genes and transcripts exhibiting evidence of expression but displaying low-quality features. Lastly, SQANTI3 incorporates IsoAnnotLite, which enables functional annotation at the isoform level and facilitates functional iso-transcriptomics analyses. We demonstrate the versatility of SQANTI3 in analyzing different data types, isoform reconstruction pipelines, and sequencing platforms, and how it provides novel biological insights into isoform biology. The SQANTI3 software is available at https://github.com/ConesaLab/SQANTI3.

bioinformatics↗

bioRxiv2022

High-resolution methylome analysis in the clonal Populus nigra cv. 'Italica' reveals environmentally sensitive hotspots and drought-responsive TE superfamilies

ABSTRACT/SUMMARYO_LIDNA methylation is environment-sensitive and can mediate plant stress responses. In long-lived trees, changing environments might cumulatively shape the methylome landscape over their lifetime. However, because high-resolution methylome studies usually focus on single environments, it remains unclear to what extent the methylation responses are generic or stress-specific, and how this relates to their long-term stability. C_LIO_LIHere, we studied the methylome plasticity of a single poplar genotype, Populus nigra cv. Italica. Adult poplar trees with diverse environmental histories were clonally propagated, and the ramets exposed to experimental cold, heat, drought, herbivory, rust infection and salicylic acid treatments. Then, we identified and compared stress-induced vs. naturally occurring DNA methylation changes using whole genome bisulfite sequencing data. C_LIO_LIMethylation changes mainly targeted transposable elements and when occurring in CG/CHG contexts, the same regions were often affected by multiple stresses, indicating a generic response. Drought triggered a unique CHH hypermethylation response in transposable elements, affecting entire superfamilies and often occurring near drought-responsive genes. Stress-induced methylation variation in CG/CHG contexts showed striking overlap with methylation differences observed between trees from distinct geographical locations. C_LIO_LIAltogether, our results indicate that generic methylome stress responses can persist as epialleles in nature while some environments trigger more transient but large and specific responses, with possible functional consequences. C_LI

genomics↗

bioRxiv2022

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics

Large-scale untargeted metabolomics studies suffer from individual variation, batch effects and instrument variability, making comparisons of common spectral features across studies difficult. One solution is to compare studies after compound identification. However, compound identification is expensive and time consuming. We successfully identify common spectral features across multiple studies, with a generalizable experimental design approach. First, we included an anchor strain, PD1074, during sample and data collection. Second, we collected data in blocks with multiple controls. These anchors enabled us to successfully integrate three studies of Caenorhabditis elegans for nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) data from five different assays. We found 34% and 14% of features to be significant in LC-MS and NMR, respectively. Between 20-50% of spectral features differ in a mutant and among a set of genetically diverse natural strains, suggesting this reduced set of spectral features are excellent targets for compound identification. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/485859v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@84abe1org.highwire.dtl.DTLVardef@3066fforg.highwire.dtl.DTLVardef@1ba166corg.highwire.dtl.DTLVardef@164cf0_HPS_FORMAT_FIGEXP M_FIG C_FIG Fourteen C. elegans strains are used in three individual studies. PD1074, the anchor control strain (orange), is grown alongside test strains (green, yellow, purple). Multiple biological replicates of PD1074 captures environmental variation in growth conditions. Non-polar and polar metabolic data across the three studies (i.e., natural strains, central metabolism mutants, and UGT mutants) were collected by nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Data acquisition controls in each block included biological reference material and pooled PD1074 samples. Biological replicates of PD1074 (n = 42 for LC-MS, n = 52 for NMR) were included in all batches. Meta-analysis provided comparable inferences to mixed effects models, and the estimated relative effects of each test strain to PD1074 and straightforward comparisons of test strains across experiments.

systems biology↗

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

Power Calculator for Detecting Allelic Imbalance Using Hierarchical Bayesian Model

Allelic imbalance (AI) is the differential expression of the two alleles in a diploid. AI can vary between tissues, treatments, and environments. Statistical methods for testing in this area exist, with impacts of explosive type I error in the presence of bias well understood. However, for study design, the more important and understudied problem is the type II error and power. As the biological questions for this type of study explode, and the costs of the technology plummet, what is more important: reads or replicates? How small of an interaction can be detected while keeping the type I error at bay? Here we present a simulation study that demonstrates that the proper model can control type I error below 5% for most scenarios. We find that a minimum of 2400, 480, and 240 allele specific reads divided equally among 12, 5, and 3 replicates is needed to detect a 10%, 20%, and 30%, respectively, deviation from allelic balance in a condition with power >80%. A minimum of 960 and 240 allele specific reads is needed to detect a 20% or 30% difference in AI between conditions with comparable power but these reads need to be divided amongst 8 replicates. Higher numbers of replicates increase power more than adding coverage without affecting type I error. We provide a Python package that enables simulation of AI scenarios and enables individuals to estimate type I error and power in detecting AI and differences in AI between conditions tailored to their own specific study needs.

bioinformatics↗

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↗

bioRxiv2017

Elevated ozone reduces photosynthetic carbon gain by accelerating leaf senescence of inbred and hybrid maize in a genotype-specific manner

Exposure to elevated tropospheric ozone concentration ([O3]) accelerates leaf senescence in many C3 crops. However, the effects of elevated [O3] on C4 crops including maize (Zea mays L.) are poorly understood in terms of physiological mechanism and genetic variation in sensitivity. Using Free Air gas Concentration Enrichment (FACE), we investigated the photosynthetic response of 18 diverse maize inbred and hybrid lines to season-long exposure to elevated [O3] (~100 nL L-1) in the field. Gas exchange was measured on the leaf subtending the ear throughout the grain filling period. On average over the lifetime of the leaf, elevated [O3] led to reductions in photosynthetic CO2 assimilation of both inbred (-22%) and hybrid (-33%) genotypes. There was significant variation among both inbred and hybrid lines in the sensitivity of photosynthesis to elevated [O3], with some lines showing no change in photosynthesis at elevated [O3]. Based on analysis of inbred line B73, the reduced CO2 assimilation at elevated [O3] was associated with accelerated senescence decreasing photosynthetic capacity, and not altered stomatal limitation. These findings across diverse maize genotypes could advance the development of more ozone tolerant maize, and provide experimental data for parameterization and validation of studies modeling how O3 impacts crop performance.

plant biology↗