Biology subjects

Murray, S. C.

Publications and source records attributed to Murray, S. C..

17 recordsLinked to original sources

bioRxiv2026

A breeder in the sky: scoring flowering with fewer flights

Flowering time is a trait of broad interest and importance to both plant breeders and plant biologists. Unlike many other traits, flowering time cannot be scored accurately from a single observation; its measurement requires repeated observations of the same experiments over time. Both classification- and object-detection-based approaches have demonstrated the potential to estimate flowering time from Unmanned aerial vehicle (UAV) imagery rather than by manual observation, but they still require data collection at many time points. Here, we train and deploy a regression-based framework for scoring flowering time. Using a dataset of more than 200,000 UAV images and associated flowering-time records collected across 27 environments, we demonstrate that this approach enables the prediction of flowering time from as few as one observation per field experiment and successfully generalizes to field experiments in environments not represented in the training dataset. These results indicate that regression-based approaches for predicting flowering time from sparse UAV observations have the potential to substantially reduce the data-collection burden in field experiments

plant biology↗

bioRxiv2026

An open field phenomics resource for multimodal maize yield prediction across divergent environments

Temporal drone phenotyping captures crop development, but irregular flight schedules complicate comparisons across environments. We release curated imagery from 356 flights across 19 Genomes to Fields environments containing 1,180 maize (Zea mays L.) hybrids. To evaluate its utility, we integrated functional principal components of vegetation index and weather trajectories with genomic information. Combinined genomic and phenomic kernels improved yield prediction, reaching correlations up to r = 0.501 for held-out hybrids in environments represented in training and 0.408 when environments were also withheld. Accumulated growing degree days offered no consistent predictive advantage over days after planting, and weather contributed modest, task-dependent gains. A transformer neural process learned directly from irregular observations, serving as a novel application of neural process models in agriculture. Mapping vegetation index functional principal components identified recurrent quantitative trait loci on chromosomes 3 and 7. This resource and its reproducible analyses guide the use of temporal spectral data for crop prediction and genetic discovery.

plant biology↗

bioRxiv2026

Splicing of ultraconserved poison exons controls mitotic fidelity and stem cell viability

SR proteins are essential splicing regulators whose expression is controlled in part through poison exons (PEs) -- ultraconserved non-coding exons that trigger nonsense-mediated decay -- yet the biological functions of these elements remain undefined. Here, we show that homozygous deletion of SRSF3-PE or TRA2{beta}-PE is selected against in mouse embryos and human induced pluripotent stem cells (iPSCs), and that conditional PE deletion causes apoptotic death in iPSCs but is tolerated in post-mitotic neurons, revealing a proliferative-state-specific requirement. Mechanistically, PE deletion elevates SR protein levels, triggers widespread splicing dysregulation, and disrupts the correct splicing of a mitotic gene network associated with spindle defects and mitotic errors. These findings establish ultraconserved poison exons as essential regulators of mitotic splicing fidelity and stem cell viability.

molecular biology↗

bioRxiv2026

A Data-Driven Image Extraction and Analysis Pipeline for Plant Phenotyping in Controlled Environments

Advances in automation, imaging, and artificial intelligence have enabled large-scale plant phenotyping, but image analysis remains a critical bottleneck for crop improvement and biological discovery. We developed an integrated multispectral phenotyping framework using imagery from the Texas A&M AgriLife Precision Automated Phenotyping Greenhouse and expanded Plant Growth and Phenotyping (PGP v2) data across maize, cotton, rice, and sorghum. The pipeline integrates pseudo-RGB generation, plant detection and segmentation, image stitching, vegetation-index analysis, texture analysis, morphological trait extraction, and temporal comparison of image-derived features to quantify changes in plant structure, spectral reflectance, and texture over time. Among the evaluated segmentation approaches, SAM v3 provided the highest and most consistent accuracy across diverse crop structures, although it required greater computational time than classical methods. SAM2Long maintained plant-instance associations across vertically stacked frames, while Scale-Invariant Feature Transform (SIFT)-based stitching reconstructed plant mosaics when individual plants extended beyond a single field of view. For each plant and imaging date, the pipeline generated an 863-dimensional feature vector spanning vegetation indices, spectral statistics, texture descriptors, and morphological traits. The framework was evaluated through two case studies: treatment-level temporal analysis of mutagenized sorghum lines and cold-stress phenotyping of maize using a separate imaging system. In both studies, the extracted features supported statistical and multivariate analyses of phenotypic variation and enabled separation of plants based on treatmentor stress-related responses. The combined dataset and workflow provide structured, automated, and well-documented phenotypic analysis across multiple crops, experimental settings, and imaging systems for controlledenvironment plant science and crop improvement. Plain Language SummaryTemporal imaging of plants in controlled environments helps scientists better understand growth and biological processes. However, analyzing large volumes of images has been limited by a lack of automated tools. Multispectral imagery captures additional information about plant pigments, structure, and stress beyond standard color images. We developed an automated analysis pipeline that identifies individual plants, tracks their growth over time, and measures traits such as height, area, shape, texture, and vegetation indices. Using artificial intelligence, the system efficiently processes thousands of images to provide consistent and repeatable measurements. By integrating engineering and plant biology, this work supports data-driven decisions for crop improvement and agricultural research.

plant biology↗

bioRxiv2025

Gene-by-Environment Interaction Significantly Drives Bacterial Endophyte Communities in Maize Stalks

1.BackgroundMany microbes are known to boost the performance of crop plants, and their use as crop treatments ("biologicals") is an active area of research and commercialization. Although many identified microbes can boost plant production under laboratory settings, most fail to translate to field settings, and even the ones that are commercialized are often unreliable. While many factors likely underlie these issues, one that has been underexplored is the idea that interactions between plants and microbes can vary across both different host genetics and different environments, and that specific combinations of host and environment can result in very different microbial outcomes. ResultsTo quantify how a plant microbiome can change across host genotype and environment and to determine the relative importance of these factors, we sampled maize stalks from 20 specific hybrid varieties grown at 15 locations in the United States. Using 16S rRNA gene sequencing, we find that the stalk microbiome is highly variable, with a handful of bacterial taxa conserved at broader taxonomic levels (Phylum, Class) and almost none at finer levels (Genus, Species). Local environment and gene-by-environment interactions both had consistently large and significant effects on the microbial community (measured by alpha-and beta-diversity summary statistics, individual taxa, and inferred functional capacity), whereas host genotype had little to no consistent effect across environments. We also identified specific soil factors (pH and potassium) as the most significant environmental drivers of the community, even though these communities were living inside the stalks. ConclusionsTaken together, our results indicate that while host genetics can significantly affect the stalk microbial community, these effects are almost entirely predicated on interactions with the environment. These results imply that reliable deployment of microbes for crop production will require significant investment in trials across many locations and genetic backgrounds, either to estimate the GxE effects directly or to screen for microbes whose effects are less dependent on these two factors.

microbiology↗

bioRxiv2025

Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery

Genomic and phenomic analyses suggest additional heritable phenomic features can improve modeling of important end traits like senescence or yield. Field phenotyping generally uses trait values averaged across individual experimental units (plants or numerous plants within plots), ignoring the full distributional pattern of collected measures. Images of plants or plots, as captured by drones (unoccupied aerial vehicles / UAVs / drones), can be viewed as individual distribution functions that capture biological information. This study introduces and validates distributional data analysis in two crops and experiment types - cotton (Gossypium hirsutum L.) single plant vegetation index (VI) analysis and maize (Zea mays L.) plot-level yield predictions. In both crops, the concept of within-day variance decomposition was demonstrated. In cotton, genotypes exerted significant influences on temporal quantile functions of VIs. Maize yield prediction using distributional data with elastic-net regression indicated improvements in yield prediction between 12.7%-21.6% with quantiles outside the conventionally used median responsible for added predictive power. A novel data visualization method for per-pixel heritability allowed distributional features to be explainable and interpretable. These results have implications for future plant phenomic studies, indicating that distributional data analysis applied across temporal imagery captures novel, heritable, and interpretable biological signal that is lost when working with conventional measures of central tendency such as mean or median summary values of experimental units. SignificanceRepeated aerial imaging of agricultural experiments produces image data sets that capture plant development in high spatial and temporal resolutions. Frequently, images are summarized by measures of central tendency, such as mean or median values. Here, functional data distributional methods were applied to cotton (Gossypium hirsutum L.) and maize (Zea mays L.) image data, capturing more information than standard approaches. Cotton genotypes significantly impacted distributional spectral data while in maize, distributional data enabled more accurate predictions of grain yield versus models trained with median data alone. Distributional data were more explainable by genetics, with novel data visualization techniques able to shine light on specific parts of plant imagery with high and low genetic variance.

plant biology↗

bioRxiv2024

Temporal Image Sandwiches Enable Link between Functional Data Analysis and Deep Learning for Single-Plant Cotton Senescence

Senescence is a highly ordered degenerative biological process that affects yield and quality in annuals and perennials. Images from 14 unoccupied aerial system (UAS, UAV, drone) flights captured the senescence window across two experiments while functional principal component analysis (FPCA) effectively reduced the dimensionality of temporal visual senescence ratings (VSRs) and two vegetation indices: RCC and TNDGR. Convolutional neural networks (CNNs) trained on temporally concatenated, or "sandwiched," UAS images of individual cotton plants (Gossypium hirsutum L.), allowed single-plant analysis (SPA). The first functional principal component scores (FPC1) served as the regression target across six CNN models (M1-M6). Model performance was strongest for FPC1 scores from VSR (R2 = 0.857 and 0.886 for M1 and M4), strong for TNDGR (R2 = 0.743 and 0.745 for M3 and M6), and strong-to- moderate for RCC (R2 = 0.619 and 0.435 for M2 and M5), with deep learning attention of each model confirmed by activation of plant pixels within saliency maps. Single-plant UAS image analysis across time enabled translatable implementations of high-throughput phenotyping by linking deep learning with functional data analysis (FDA). This has applications for fundamental plant biology, monitoring orchards or other spaced plantings, plant breeding, and genetic research.

plant biology↗

bioRxiv2024

DEEP LEARNING-BASED HIGH-THROUGHPUT PHENOTYPING OF MAIZE (Zea mays L.) TASSELING FROM UAS IMAGERY ACROSS ENVIRONMENTS

AO_SCPLOWBSTRACTC_SCPLOWFlowering time is a critical phenological trait in maize (Zea mays L.) breeding programs. Traditional measurements for assessing flowering time involve semi-subjective and labor-intensive manual observation, limiting the scale and efficiency of genetics and breeding improvement. Leveraging unoccupied aerial system (UAS, also known as UAVs or drones) technology coupled with convolutional neural networks (CNNs) presents a promising approach for high-throughput phenotyping of tasseling in maize. Most CNN image analysis is overly complicated for simple tasks relevant to plant scientists. Here a methodology for extracting tasseling from RGB imagery using a CNN-based approach was applied to 220 hybrids and 30 test lines grown in eight diverse environments (Wisconsin and Texas, U.S.A.) then validated through an unrelated set of hybrids. Overall accuracies of .946, .911, .985, and .988 were obtained for classifying maize images with or without tassels from College Station, TX in 2020; College Station, TX in 2021; Arlington, WI in 2021; and Madison, WI in 2021 respectively. By employing deep learning techniques, larger volumes of phenotypic data can be processed enabling high-throughput phenotyping in breeding programs. Although large datasets are required to train CNN models, the proposed methodology prioritizes simplicity in computational architecture while maintaining effectiveness in identifying flowered maize across diverse genotypes and environments.

plant biology↗

bioRxiv2023

Near Infrared Reflectance Spectroscopy Phenomic and Genomic Prediction of Maize Agronomic and Composition Traits Across Environments

For nearly two decades, genomic selection has supported efforts to increase genetic gains in plant and animal improvement programs. However, novel phenomic strategies helping to predict complex traits in maize have proven beneficial when integrated into across- and within-environment genomic prediction models. One phenomic data modality is near infrared spectroscopy (NIRS), which records reflectance values of biological samples (e.g., maize kernels) based on chemical composition. Predictions of seven maize agronomic traits and three kernel composition traits across two years (2011-2012) and two management conditions (water stressed and well-watered) were conducted using combinations of NIRS and genomic data within four different cross-validation prediction scenarios. In aggregate, models incorporating NIRS data alongside genomic data improved predictive ability over models using only genomic data in 5 of 28 trait/cross-validation scenarios for across-environment prediction and 15 of 28 trait/environment scenarios for within-environment prediction, while the model with NIRS data alone had the highest prediction ability in only 1 of 28 scenarios for within-environment prediction. Potential causes of the surprisingly lower phenomic than genomic prediction power in this study are discussed, including sample size, sample homogenization, and low GxE. A genome-wide association study (GWAS) implicated known (i.e., MADS69, ZCN8, sh1, wx1, du1) and unknown candidate genes linked to plant height and flowering-related agronomic traits as well as compositional traits such as kernel protein and starch content. This study demonstrated that including NIRS with genomic markers is a viable method to predict multiple complex traits with improved predictive ability and elucidate underlying biological causes. Key messageGenomic and NIRS data from a maize diversity panel were used for prediction of agronomic and kernel composition traits while uncovering candidate genes for kernel protein and starch content.

plant biology↗

bioRxiv2021

Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions

A major challenge of genetic improvement and selection is to accurately predict individuals with the highest fitness in a population without direct measurement. Over the last decade genomic predictions (GP) based on genome-wide markers have become reliable and routine. Now phenotyping technologies, including unoccupied aerial systems (UAS also known as drones), can characterize individuals with a data depth comparable to genomics when used throughout growth. This study, for the first time, demonstrated that the prediction power of temporal UAS phenomic data can achieve or exceed that of genomic data. UAS data containing red-green-blue (RGB) bands over fifteen growth time points and multispectral (RGB, red-edge and near infrared) bands over twelve time points were compared across 280 unique maize hybrids. Through cross validation of untested genotypes in tested environments (CV2), temporal phenomic prediction (TPP) outperformed GP (0.80 vs 0.71); TPP and GP performed similarly in three other cross validation scenarios. Genome wide association mapping using area under temporal curves of vegetation indices (VIs) revealed 24.5 percent of a total of 241 discovered loci (59 loci) had associations with multiple VIs, explaining up to 51 percent of grain yield variation, less than GP and TPP predicted. This suggests TPP, like GP, integrates small effect loci well improving plant fitness predictions. More importantly, temporal phenomic prediction appeared to work successfully on unrelated individuals unlike genomic prediction.

plant biology↗

bioRxiv2021

Mapping Human Transient Transcriptomes Using Single Nucleotide Resolution 4sU Sequencing (SNU-Seq)

Genomes are pervasively transcribed, leading to stable and unstable transcripts that influence 3-dimensional genome organisation and gene regulation. High sensitivity and nucleotide resolution are required to resolve mammalian nascent transcriptomes. Here, we exploit the sensitivity of 4-thio-uridine (4sU) metabolic pulse-labelling to develop two nucleotide-resolution methods: Single-Nucleotide resolution 4sU sequencing (SNU-Seq) and size-fractionated 4sU-Seq (sf4sU-Seq). sf4sU-Seq involves gel isolation of abundant 4sU-labelled promoter proximal nascent transcripts, enabling nucleotide resolution mapping of transcription start sites and promoter proximal pauses (PPPs) on the same transcript. SNU-Seq exploits 3 end RNA-Seq, using bacterial poly(A) polymerase (bPAP) to polyadenylate the 3 ends of nascent transcripts and create oligo(dT)-primed libraries. The artificial poly(A) tail marks the precise position of polymerase on a transcription unit. SNU-Seq read levels are similar at pre-mRNAs and enhancers genome-wide and read spikes in pre-mRNA outputs map pauses, PPPs and polyadenylation sites. SNU-Seq enables discovery of thousands of unannotated regions of divergent transcription and helps define hundreds of the more than 10,000 regions of primed non-transcribed acetylated open chromatin that induce divergent nascent transcripts within 0.5h of IFN-{gamma} treatment in Hep3B cells. Thus, combining chromatin analysis with SNU-Seq reveals the transcriptional responsiveness of an epigenetically primed human genome. HIGHLIGHTSO_LISNU-Seq maps nascent transcripts with bp resolution, high sensitivity and low cost C_LIO_LIsf4sU-Seq resolves TSS and PPP at the same gene, complementing SNU-Seq C_LIO_LI1000s of divergently transcribed enhancers resolved by SNU-Seq C_LIO_LIRapid IFN{gamma} dependent transcriptional induction from primed Hep3B epigenome C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=165 SRC="FIGDIR/small/452379v2_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@da6239org.highwire.dtl.DTLVardef@16692c6org.highwire.dtl.DTLVardef@12e30f7org.highwire.dtl.DTLVardef@190c98f_HPS_FORMAT_FIGEXP M_FIG C_FIG

molecular biology↗

bioRxiv2020

Validation of Functional Polymorphisms Affecting Maize Plant Height by Unoccupied Aerial Systems (UAS) allows Novel Temporal Phenotypes

Plant height (PHT) in maize (Zea mays L.) has been scrutinized genetically and phenotypically due to relationship with other agronomically valuable traits (e.g. yield). Heritable variation of PHT is determined by many discovered quantitative trait loci (QTLs); however, phenotypic effects of such loci often lack validation across environments and genetic backgrounds, especially in the hybrid state grown by farmers rather than the inbred state preferred by geneticists. A previous genome wide association study using a hybrid diversity panel identified two novel quantitative trait variants (QTVs) controlling both PHT and grain yield. Here, heterogeneous inbred families demonstrated that these two loci, characterized by two single nucleotide polymorphisms (SNPs), cause phenotypic variation in inbred lines, but that size of these effects were variable across four different genetic backgrounds, ranging from 1 to 10 cm. Weekly unoccupied aerial system flights demonstrated both SNPs had larger effects, varying from 10 to 25 cm, in early growth while SNPs effects decreased towards the end of the season. These results show that allelic effect sizes of economically valuable loci are both dynamic in temporal growth and dynamic across genetic backgrounds resulting in informative phenotypic variability overlooked following traditional phenotyping methods. Public genotyping data shows recent favorably selection in elite temperate germplasm with little change across tropical backgrounds. As these loci remain rare in tropical germplasm, with effects most visible early in growth, they are useful for breeding and selection to expand the genetic basis of maize.

plant biology↗

bioRxiv2020

R/UAStools::plotshpcreate: Create Multi-Polygon Shapefiles for Extraction of Research Plot Scale Agriculture Remote Sensing Data.

Agricultural researchers are embracing remote sensing tools to phenotype and monitor agriculture crops. Specifically, large quantities of data are now being collected on small plot research studies using Unoccupied Aerial Systems (UAS, aka drones), ground systems, or other technologies but, data processing and analysis lags behind. One major contributor to current data processing bottlenecks has been the lack of publicly available software tools tailored towards remote sensing of small plots, and usability for researchers inexperienced in remote sensing. To address these needs we created plot shapefile maker (R/UAS::plotshpcreate), an open source R function which rapidly creates ESRI polygon shapefiles to the desired dimensions of individual agriculture research plots areas of interest and associates plot specific information. Plotshpcreate was developed to utilize inputs containing experimental design, field orientation, and plot dimensions for easily creating a multi-polygon shapefile of an entire small plot experiment. Output shapefiles are based on the user inputs geolocation of the front left corner of the research field ensuring accurate overlay of polygons often without manual user adjustment. The output shapefile is useful in GIS software to extract plot level data tracing back to the unique IDs of the experimental plots. Plotshpcreate is available on GitHub (https://github.com/andersst91/UAStools).

bioinformatics↗

bioRxiv2019

Unoccupied aerial system enabled functional modeling of maize (Zea mays L.) height reveals dynamic expression of loci associated to temporal growth.

Unoccupied aerial systems (UAS) were used to phenotype growth trajectories of inbred maize populations under field conditions. Three recombinant inbred line populations were surveyed on a weekly basis collecting RGB images across two irrigation regimens (irrigated and non-irrigated/rain fed). Plant height, estimated by the 95th percentile (P95) height from UAS generated 3D point clouds, exceeded 70% correlation to manual ground truth measurements and 51% of experimental variance was explained by genetics. The Weibull sigmoidal function accurately modeled plant growth (R2: >99%; RMSE: < 4 cm) from P95 genetic means. The mean asymptote was strongly correlated (r2=0.66-0.77) with terminal plant height. Maximum absolute growth rates (mm d-1) were weakly correlated to height and flowering time. The average inflection point ranged from 57 to 60 days after sowing (DAS) and was correlated with flowering time (r2=0.45-0.68). Functional growth parameters (asymptote, inflection point, growth rate) alone identified 34 genetic loci, each explaining 3 to 15% of total genetic variation. Plant height was estimated at one-day intervals to 85 DAS, identifying 58 unique temporal quantitative trait loci (QTL) locations. Genomic hotspots on chromosome 1 and 3 indicated chromosomal regions associated with functional growth trajectories influencing flowering time, growth rate, and terminal growth. Temporal QTL demonstrated unique dynamic expression patterns not observable previously, no QTL were significantly expressed throughout the entire growing season. UAS technologies improved phenotypic selection accuracy and permitted monitoring traits on a temporal scale previously infeasible using manual measurements, furthering understanding of crop development and biological trajectories. Author summaryUnoccupied aerial systems (UAS) now can provide high throughput phenotyping to functionally model plant growth and explore genetic loci underlying temporal expression of dynamic phenotypes, specifically plant height. Efficient integration of temporal phenotyping via UAS, will improve the scientific understanding of dynamic, quantitative traits and developmental trajectories of important agronomic crops, leading to new understanding of plant biology. Here we present, for the first time, the dynamic nature of quantitative trait loci (QTL) over time under field conditions. To our knowledge, this is first empirical study to expand beyond selective developmental time points, evaluating functional and temporal QTL expression in maize (Zea mays L.) throughout a growing season within a field-based environment.

plant biology↗

bioRxiv2019

H3K4me3 is neither instructive for, nor informed by, transcription.

H3K4me3 is a near-universal histone modification found predominantly at the 5 region of genes, with a well-documented association with gene activity. H3K4me3 has been ascribed roles as both an instructor of gene expression and also a downstream consequence of expression, yet neither has been convincingly proven on a genome-wide scale. Here we test these relationships using a combination of bioinformatics, modelling and experimental data from budding yeast in which the levels of H3K4me3 have been massively ablated. We find that loss of H3K4me3 has no effect on the levels of nascent transcription or transcript in the population. Moreover, we observe no change in the rates of transcription initiation, elongation, mRNA export or turnover, or in protein levels, or cell-to-cell variation of mRNA. Loss of H3K4me3 also has no effect on the large changes in gene expression patterns that follow galactose induction. Conversely, loss of RNA polymerase from the nucleus has no effect on the pattern of H3K4me3 deposition and little effect on its levels, despite much larger changes to other chromatin features. Furthermore, large genome-wide changes in transcription, both in response to environmental stress and during metabolic cycling, are not accompanied by corresponding changes in H3K4me3. Thus, despite the correlation between H3K4me3 and gene activity, neither appear to be necessary to maintain levels of the other, nor to influence their changes in response to environmental stimuli. When we compare gene classes with very different levels of H3K4me3 but highly similar transcription levels we find that H3K4me3-marked genes are those whose expression is unresponsive to environmental changes, and that their histones are less acetylated and dynamically turned-over. Constitutive genes are generally well-expressed, which may alone explain the correlation between H3K4me3 and gene expression, while the biological role of H3K4me3 may have more to do with this distinction in gene class.

genomics↗

bioRxiv2019

Maize Kernel Development Stage the Primary Factor in Differential Gene Expression in Response to Two Methods of Field Inoculation with Aspergillus flavus

BackgroundAflatoxins, produced by the fungus Aspergillus flavus, often contaminate preharvest maize (Zea mays L.) grain under heat and drought stresses, posing serious health hazards to humans and livestock, and resulting in significant costs to identify and dispose of contaminated grain. This study was designed to investigate the changes in differential gene expression during seed morphogenesis and maturation in the \"aflatoxin resistant\" Argentinian inbred line Tx772 when challenged by the introduction of A. flavus through two different methods of ear inoculation; non-wounding (silk channel, used to select Tx772), wounding (side needle) and a non-inoculated control.\n\nMethod and FindingsGrain maturity had the largest effect on overall RNA-Seq differential gene expression (DGE) as measured by edgeR of the Bioconductor platform. However, within each stage of development, ranging from blister to dent, similar up-regulation in expression of many maize genes following inoculation with either method was observed; a total of 16 genes previously associated with resistance to pathogens were identified among the transcripts differentially expressed (DE) at p [≤] .05, FDR [≤] .10, and fold change [≥] 2.0 over all stages. The side needle technique produced a larger effect of infection as evidenced by 6,324 fungal reads versus 518 in silk channel and a higher level of aflatoxin. Correlations between approximately 7,000 fungal reads and the number of maize DE genes for each of the eight treatment groups was 0.56 (p = .152) and was 0.65 (p < .001) with levels of aflatoxin ranging from 0 to 137 ng g-1.\n\nConclusionThese correlations provided an internal measure of effectiveness of inoculation methods that were associated with the mostly up-regulation of defense-related genes in response to the presence of Aspergillus flavus in a unique maize genetic background.

plant biology↗

bioRxiv2017

Antisense transcription-dependent chromatin signature modulates sense transcription and transcript dynamics

Antisense transcription is widespread in genomes. Despite large differences in gene size and architecture, we find that yeast and human genes share a unique, antisense transcription-associated chromatin signature. We asked whether this signature is related to a biological function for antisense transcription. Using quantitative RNA-FISH, we observed changes in sense transcript distributions in nuclei and cytoplasm as antisense transcript levels were altered. To determine the mechanistic differences underlying these distributions, we developed a mathematical framework describing transcription from initiation to transcript degradation. At GAL1, high levels of antisense transcription alters sense transcription dynamics, reducing rates of transcript production and processing, while increasing transcript stability, which is also a genome-wide association. Establishing the antisense transcription-associated chromatin signature through disruption of the Set3C histone deacetylase activity is sufficient to similarly change these rates even in the absence of antisense transcription. Thus, antisense transcription alters sense transcription dynamics in a chromatin-dependent manner.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=198 HEIGHT=200 SRC=\"FIGDIR/small/187237_fig8.gif\" ALT=\"Figure 8\">\nView larger version (52K):\norg.highwire.dtl.DTLVardef@4f872corg.highwire.dtl.DTLVardef@1339662org.highwire.dtl.DTLVardef@1d62cc2org.highwire.dtl.DTLVardef@148f24_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract\n\nC_FIG In this work, Brown et al. provide a mechanistic understanding of the effect of antisense transcription on the production and fate of sense transcripts. Antisense transcription buffers genes against the action of the Set3 lysine deacetylase, thus altering rates of transcript production, processing and stability. O_LIConserved antisense transcription-dependent chromatin architecture near promoters\nC_LIO_LIAntisense transcription alters sense transcription dynamics and transcript stability\nC_LIO_LIAntisense transcription functions in a chromatin-dependent manner\nC_LIO_LIIncreased acetylation by set3{Delta} mimics high antisense transcriptional dynamics\nC_LI

systems biology↗