Biology subjects

Pauli, D.

Publications and source records attributed to Pauli, D..

10 recordsLinked to original sources

bioRxiv2025

Identification of a volatile chemotype associated with resilience to water stress in domesticated varieties of Cotton

Volatile organic compounds (VOCs) mediate plant responses to environmental stresses, yet their chemotypic variation and drought responsiveness remain poorly characterized in domesticated crops. Here, we identify a previously undocumented binary chemotype structure in cultivated upland cotton (Gossypium hirsutum), defined by the mutually exclusive production of either bisabolene or guaiene sesquiterpenes. Across 24 genotypes grown under field conditions, genotypes consistently produced one of these compounds, but never both, revealing a cryptic chemotypic polymorphism with physiological consequences. While drought stress reduced leaf water content across all plants, plants in the guaiene chemotype exhibited disproportionately greater losses. Water limitation also reduced total monoterpene and sesquiterpene concentrations, with the bisabolene chemotype showing stronger declines despite higher constitutive levels. Chemotypes further diverged in their biosynthesis of key damage-induced green leaf volatiles (GLVs). While the guaiene chemotype reduced GLV biosynthetic capacity under prolonged drought, the bisabolene chemotype showed transient increases in GLVs and pathway-specific enzyme activity under short-term stress, suggesting differential physiological resilience. Notably, all genotypes in this study fell within the low {gamma}-terpinene category previously described in wild cotton, indicating that this bisabolene/guaiene variation represents a novel axis of chemical diversity distinct from known wild chemotypes. These findings position chemotypic variation as a promising, non-destructive biomarker for drought response in cotton breeding programs. By linking chemotype to constitutive VOCs and water status, our work provides new tools for identifying stress-resilient cultivars and advances understanding of phytochemical strategies for crop management in a changing climate.

plant biology↗

bioRxiv2025

HAMRLNC: A Comprehensive Pipeline for High-throughput Analysis of Modified Ribonucleotides and Long Non-Coding Ribonucleic Acids

As sequencing technologies advance and costs decline, there has been a surge in the application of RNA sequencing (RNA-seq) in understanding biological processes. In addition to the typical uses of RNA-seq for transcriptomics, gene annotation, novel gene discovery, and network analysis, these data can enable a deeper understanding of cellular processes through the identification of RNA modifications (epitranscriptome) and long non-coding RNAs (lncRNAs). To expedite discovery, we developed a portable, centralized computational pipeline for High-throughput Annotation of Modified Ribonucleotides and Long Non-Coding ribonucleic acids (HAMRLNC). HAMRLNC differs from existing methods by incorporating three workflows for quantifying transcript abundance, inferring RNA modifications, and lncRNA annotation using the same RNA- seq pre-processing and mapping steps. This facilitates reproducibility across multiple analyses and allows researchers to perform post-hoc analyses of archived sequencing data. In addition, we include novel analysis features to enable downstream visualization of annotated modified RNAs. HAMRLNC generates over a dozen well-defined and labeled figures as output, including gene ontology heatmaps, modification enrichment landscape, and modification clustering statistics. Availability and ImplementationHAMRLNC is an open-source software, and the source code is available at https://github.com/bdgregory/HAMRLNC. The pipeline can be installed and used through a docker container (https://hub.docker.com/r/chosenobih/HAMRLNC/tags). HAMRLNC is also available as an app in the CyVerse Discovery Environment https://de.cyverse.org/. Supplementary informationSupplementary data are available at BioRxiv online.

bioinformatics↗

bioRxiv2025

Investigating the role of stomatal dynamics on agronomic traits using a slac1-2 Zea mays mutant

The production of staple food crops is becoming increasingly difficult due to the amount of freshwater needed to realize food security for a growing global population. Climate projections show hotter and drier growing seasons in traditionally productive agricultural regions, which will increase the demand for limited water resources. Thus, strategies to improve water use efficiency will only become more important for sustainable agriculture. At the leaf level, stomatal conductance has a large influence on transpirational water loss and therefore water use efficiency. The anion channel SLAC1 has been shown in several plant species to function as the primary mechanism for stomatal closure, which allows stomatal aperture to change dynamically in response to environmental stimuli. Given that slac1 is a single gene that can significantly alter stomatal conductance, it has the potential to serve as a control point for improving water use efficiency. Here we fully characterize a Zea mays slac1-2 mutant in multiple field environments. Interestingly, homozygous slac1-2 hybrids did not show improved net CO2 assimilation or increased grain yield despite having greater stomatal conductance compared to a wild-type hybrid. Net CO2 assimilation and grain yield were either lower or similar in slac1-2 compared to wild-type across environments. Furthermore, the slac1-2 hybrid did not have increased nitrogen uptake. These results suggest that the C4 carbon concentrating mechanism removes any stomatal conductance limitations to CO2 assimilation, even in highly productive wild-type Z. mays hybrids. Because the slac1-2 hybrids eliminate stomatal conductance as a major variable for modulating water loss, future studies will be able to investigate alternate regulators of plant water potential to identify novel mechanisms for increasing water use efficiency.

physiology↗

bioRxiv2025

A field-focused systems approach reveals mRNA covalent modifications linked to sorghum growth and development under drought stress

RNA Covalent Modifications (RCMs) are post-transcriptional chemical alterations that influence RNA stability and translation efficiency, thus play critical roles in eukaryotic growth and development. However, their role in regulating plant performance under abiotic stress remain largely unexplored. Here, we integrated multi-omics data in six Sorghum bicolor accessions under water-limiting conditions in the field to explore the relationship between RCMs and drought response. Within a stress and photosynthesis-associated gene co-expression module, we identified SbDUS2, a member of family of enzymes, conserved across eukaryotes, which catalyzes the reduction of uracil to dihydrouridine (DHU) on RNA molecules. DHU-modified transcripts in this module were enriched for photosynthetic functions and showed strong correlation with photosynthetic traits. To elucidate the function of this RCM, we characterized loss of function dus2 mutants in the genetic model, Arabidopsis thaliana. Under control conditions, these DHU-deficient mutants exhibited impaired germination and delayed development. Furthermore, when exposed to heat or water-limiting conditions, these mutants showed significantly reduced net CO2 assimilation and survival. Using multiple transcriptome-wide RNA stability assays, we demonstrated that transcripts associated with lower DHU level in a dus2 background generally exhibited increased stability compared to Col-0 controls. Particularly, lack of DUS2 led to the hyperstability of photosynthesis-related transcripts, impeding their turnover and likely preventing proper photosynthetic acclimation during stress. We propose a model based on these data where DHU acts as a critical post-transcriptional regulator marking mRNAs for rapid turnover under stress, highlighting an overlooked regulatory layer contributing to plant resilience.

plant biology↗

bioRxiv2024

Quantifying Leaf Symptoms of Sorghum Charcoal Rot in Images of Field-Grown Plants 9 Using Deep Neural Networks

Charcoal rot of sorghum (CRS) is a significant disease affecting sorghum crops, with limited genetic resistance available. The causative agent, Macrophomina phaseolina (Tassi) Goid, is a highly destructive fungal pathogen that targets over 500 plant species globally, including essential staple crops. Utilizing field image data for precise detection and quantification of CRS could greatly assist in the prompt identification and management of affected fields and thereby reduce yield losses. The objective of this work was to implement various machine learning algorithms to evaluate their ability to accurately detect and quantify CRS in red-green-blue (RGB) images of sorghum plants exhibiting symptoms of infection. EfficientNet-B3 and a fully convolutional network (FCN) emerged as the top-performing models for image classification and segmentation tasks, respectively. Among the classification models evaluated, EfficientNet-B3 demonstrated superior performance, achieving an accuracy of 86.97%, a recall rate of 0.71, and an F1 score of 0.73. Of the segmentation models tested, FCN proved to be the most effective, exhibiting a validation accuracy of 97.76%, a recall rate of 0.68, and an F1 score of 0.66. As the size of the image patches increased, both models validation scores increased linearly, and their processing time decreased exponentially. The models, in addition to being immediately useful for breeders and growers of sorghum, advance the domain of automated plant phenotyping and may serve as a base for drone-based or other automated field phenotyping efforts. Additionally, the models presented herein can be accessed through a web-based application where users can easily analyze their own images. Core ideasO_LIAutomated phenotyping tools are required for the efficient detection and quantification of charcoal rot of sorghum. C_LIO_LIClassification and segmentation models can distinguish between concurrent plant stresses with similar symptoms. C_LIO_LILarger image patch sizes generally improve model performance and reduce processing time. C_LI

plant biology↗

bioRxiv2022

Comparative multi-omic analyses reveal gene regulatory networks controlling physiological and metabolic responses to drought in upland cotton

Drought stress substantially impacts crop physiology resulting in alteration of growth and productivity. Understanding the genetic and molecular crosstalk between stress responses and agronomically important traits such as fiber yield is particularly complicated in the allopolyploid species, upland cotton (Gossypium hirsutum), due to reduced sequence variability between A and D subgenomes. To better understand how drought stress impacts yield, the transcriptomes of 22 genetically and phenotypically diverse upland cotton accessions grown under well-watered and water-limited conditions in the Arizona low desert were sequenced. Gene co-expression analyses were performed, uncovering a group of stress response genes, in particular transcription factors GhDREB2A-A and GhHSFA6B-D, associated with improved yield under water-limited conditions in an ABA-independent manner. DNA affinity purification sequencing (DAP-seq), as well as public cistrome data from Arabidopsis, were used to identify targets of these two TFs. Among these targets were two lint-yield associated genes previously identified through genome-wide association studies (GWAS) -based approaches, GhABP-D and GhIPS1-A. Biochemical and phylogenetic approaches were used to determine that GhIPS1-A is positively regulated by GhHSFA6B-D, and that this regulatory mechanism is specific to Gossypium spp. containing the A (old-world) genome. Finally, a SNP was identified within the GhHSFA6B-D binding site in GhIPS1-A that is positively associated with yield under water limiting conditions. These data lay out a regulatory connection between abiotic stress and fiber yield in cotton that appears conserved in other systems such as Arabidopsis. This regulatory mechanism highlights how sub-genome dynamics contribute to phenotypic stress-response plasticity in cotton.

plant biology↗

bioRxiv2021

Repression of the Hox gene abd-A by ELAV-mediated Transcriptional Interference

Intergenic transcription is a common feature of eukaryotic genomes and performs important and diverse cellular functions. Here, we investigate the iab-8 ncRNA from the Drosophila Bithorax Complex and show that this RNA is able to repress the transcription of genes located at its 3 end by a sequence-independent, transcriptional interference mechanism. Although this RNA is expressed in the early epidermis and CNS, we find that its repressive activity is limited to the CNS, where in wild-type embryos, it acts on the Hox gene, abd-A located immediately downstream of it. The CNS specificity is achieved through a 3 extension of the transcript, mediated by the neuronal-specific, RNA-binding protein, ELAV. Loss of ELAV activity eliminates the 3 extension and results in the ectopic activation of abd-A. Thus, a tissue-specific change in the length of a ncRNA is used to generate a precise pattern of gene expression in a higher eukaryote.

genetics↗

bioRxiv2020

Selective linkage of mitochondrial enzymes to intracellular calcium stores differs between human induced pluripotent stem cells, neural stem cells and neurons

The coupling of the endoplasmic reticulum (ER) with mitochondria modulates neuronal calcium signaling. Whether this link changes with neuronal development is unknown. The current study first determined whether ER calcium stores are similar during development of human neurons, and then tested if the ER/mitochondrial coupling varied with development. The release of ER calcium to the cytosol by the IP3 agonist bradykinin was determined in human induced-pluripotent stem cells (iPSC), neural stem cells (NSC) and neurons. The concentration dependence for the release of ER calcium was similar at different stages of development. Metabolism changes dramatically with development. Glycolysis is the main energy source in iPSC and NSC whereas mitochondrial metabolism is more prominent in neurons. To test whether the coupling of mitochondria and ER changed with development, bombesin or bradykinin releasable calcium stores (BRCS) were monitored after inhibiting either of two key mitochondrial enzyme complexes: the alpha-ketoglutarate dehydrogenase complex (KGDHC) or the pyruvate dehydrogenase complex (PDHC). Inhibition of KGDHC did not alter BRCS in either iPSC or NSC. Inhibition of PDHC in neurons diminished BRCS whereas decreased KGDHC activity exaggerated BRCS. The latter finding may help understand the pathology of Alzheimers disease (AD). BRCS is exaggerated in cells from AD patients and KGDHC is reduced in brains of patients with AD. In summary, a prominent ER/mitochondrial link in neurons is associated with selective mitochondrial enzymes. The ER/mitochondrial link changes with human neuronal development and plausibly links ER calcium changes to AD.

neuroscience↗

bioRxiv2020

A minimally disruptive method for measuring water potential in-planta using hydrogel nanoreporters

Leaf water potential is a critical indicator of plant water status, integrating soil moisture status, plant physiology, and environmental conditions. There are few tools for measuring plant water status (water potential) in situ, presenting a critical barrier for the development of appropriate phenotyping (measurement) methods for crop development and modeling efforts aimed at understanding water transport in plants. Here, we present the development of an in situ, minimally-disruptive hydrogel nanoreporter (AquaDust) for measuring leaf water potential. The gel matrix responds to changes in water potential in its local environment by swelling; the distance between covalently linked dyes changes with the reconfiguration of the polymer, leading to changes in the emission spectrum via Fluorescence Resonance Energy Transfer (FRET). Upon infiltration into leaves, the nanoparticles localize within the apoplastic space in the mesophyll; they do not enter the cytoplast or the xylem. We characterize the physical basis for AquaDusts response and demonstrate its function in intact maize (Zea mays L.) leaves as a reporter of leaf water potential. We use AquaDust to measure gradients of water potential along intact, actively transpiring leaves as a function of water status; the localized nature of the reporters allows us to define a hydraulic model that distinguishes resistances inside and outside the xylem. We also present field measurements with AquaDust through a full diurnal cycle to confirm the robustness of the technique and of our model. We conclude that AquaDust offers potential opportunities for high-throughput, field measurements and spatially resolved studies of water relations within plant tissues.

plant biology↗

bioRxiv2017

Multivariate analysis of the cotton seed ionome reveals integrated genetic signatures of abiotic stress response

To mitigate the effects of heat and drought stress, a better understanding of the genetic control of physiological responses to these environmental conditions is needed. To this end, we evaluated an upland cotton (Gossypium hirsutum L.) mapping population under water-limited and well-watered conditions in a hot, arid environment. The elemental concentrations (ionome) of seed samples from the population were profiled in addition to those of soil samples taken from throughout the field site to better model environmental variation. The elements profiled in seeds exhibited moderate to high heritabilities, as well as strong phenotypic and genotypic correlations between elements that were not altered by the imposed irrigation regimes. Quantitative trait loci (QTL) mapping results from a Bayesian classification method identified multiple genomic regions where QTL for individual elements colocalized, suggesting that genetic control of the ionome is highly interrelated. To more fully explore this genetic architecture, multivariate QTL mapping was implemented among groups of biochemically related elements. This analysis revealed both additional and pleiotropic QTL responsible for coordinated control of phenotypic variation for elemental accumulation. Machine learning algorithms that utilized only ionomic data predicted the irrigation regime under which genotypes were evaluated with very high accuracy. Taken together, these results demonstrate the extent to which the seed ionome is genetically interrelated and predictive of plant physiological responses to adverse environmental conditions.\n\nOne sentence summaryThe cotton seed ionome has a shared genetic basis that provides insight into the physiological status of the plant.

genetics↗