Biology subjects

Doherty, C. J.

Publications and source records attributed to Doherty, C. J..

7 recordsLinked to original sources

bioRxiv2026

Biophytometallurgy: biomining metals from plant resources

The physicochemical controls governing metal acquisition, release, and redistribution across biological interfaces remain poorly understood. Biophytometallurgy--the microbially assisted release and recovery of plant-associated metals--was used to probe the directionality of the mechanisms controlling nickel and rare earth element (REE) release from Phytolacca during solid-liquid extraction. Bulk characterization did not support a dominant crystalline REE-phosphate-like host in hydroponically enriched shoots. Dissolution and rebinding experiments instead revealed chemically accessible nickel and REE pools, the latter of which had behaviors consistent with apparent equilibrium-like partitioning under mildly acidic conditions. During sulfur biooxidation, Acidithiobacillus ferrooxidans promoted REE release while providing a competing cell-associated REE sink. Consequently, aqueous REE concentrations reflected net redistribution among the separable plant, solution, and microbial phases instead of dissolution alone. These results establish a framework for studying metal partitioning across complex and coupled biological systems and support a route for aqueous REE recovery from plants without thermochemical conversion to ash.

bioengineering↗

bioRxiv2025

Nondestructive Detection and Quantification of Dysprosium in Plant Tissues

BackgroundThe growing demand for rare-earth elements (REEs), particularly dysprosium (Dy), in part driven by clean energy technologies, underscores the need for sustainable extraction methods. Recovery of Dy, particularly from geographically distributed waste sources is challenging. This gap positions phytomining--a technique using plants to accumulate metals-- as a promising alternative. However, plant species differ in their ability to accumulate metals in high concentrations, necessitating efficient screening methods. In this study, we developed a high-throughput fluorescence-based assay to detect and quantify Dy uptake in plant tissues. ResultsOur Dy detection method exploits Dys unique spectroscopic properties for sensitive and efficient analysis, enabling detection of concentrations as low as 0.3 {micro}M. By incorporating sodium tungstate (Na WO) as a fluorescence enhancer, we achieved robust emissions at 480 and 580 nm, facilitating Dy quantification in complex plant matrices. Additionally, time-resolved fluorescence techniques reduced background autofluorescence from plant tissues, enhancing signal specificity. Validation against Inductively Coupled Plasma Mass Spectrometry (ICP-MS) demonstrated strong correlation. Greenhouse trials confirmed the methods utility for screening Dy accumulation in living plants and highlight the potential for rapid standoff detection. ConclusionsThis fluorescence-based approach offers a scalable, efficient tool for identifying Dy-accumulating plants, advancing phytomining as a sustainable strategy for REE recovery.

plant biology↗

bioRxiv2023

A normalization method that controls for total RNA abundance affects the identification of differentially expressed genes, revealing bias toward morning-expressed responses

RNA-Sequencing is widely used to investigate changes in gene expression at the transcription level in plants. Most plant RNA-Seq analysis pipelines base the normalization approaches on the assumption that total transcript levels do not vary between samples. However, this assumption has not been demonstrated. In fact, many common experimental treatments and genetic alterations affect transcription efficiency or RNA stability, resulting in unequal transcript abundance. The addition of synthetic RNA controls is a simple correction that controls for variation in total mRNA levels. However, adding spike-ins appropriately is challenging with complex plant tissue, and carefully considering how they are added is essential to their successful use. We demonstrate that adding external RNA spike-ins as a normalization control produces differences in RNA-Seq analysis compared to traditional normalization methods, even between two times of day in untreated plants. We illustrate the use of RNA spike-ins with 3 RNA-Seq and present a normalization pipeline that accounts for differences in total transcriptional levels. We evaluate the effect of normalization methods on identifying differentially expressed genes in the context of identifying the effect of the time of day on gene expression and response to chilling stress in sorghum.

plant biology↗

bioRxiv2022

Assessing the Nucleotide-Level Impact of Spaceflight Stress using RNA-Sequencing Data

Understanding the effects of space radiation and microgravity on DNA integrity is critical to assess the impact of long-term spaceflight. However, studying spaceflights effect on terrestrial life is difficult. NASA created GeneLab, a public Omics database for spaceflight-related data, to help combat these limitations. While GeneLab has very few DNA-based data sets, transcriptome information is abundant. This study used RNA-Seq data from GeneLab to examine DNA sequence variants linked to spaceflight stress exposure. More mutations were observed in spaceflight samples than in the ground control samples. This increase in variants was not reduced in samples grown under artificial gravity in space, suggesting that microgravity did not significantly affect the amount of DNA damage in this experiment. There was also an increase in transversion mutations, consistent with known forms of radiation-induced damage. This work demonstrates that RNA-Seq data is a useful resource for evaluating DNA damage from spaceflight and provides a baseline for the types of mutations that could be detected.

bioinformatics↗

bioRxiv2022

Arabidopsis cell suspension culture that lacks circadian rhythms can be recovered by constitutive ELF3 expression

Callus and cell suspension culture techniques are valuable tools in plant biotechnology and are widely used in fundamental and applied research. For studies in callus and cell suspension cultures to be relevant, it is essential to know if the underlying biochemistry is similar to intact plants. This study examined the expression of core circadian genes in Arabidopsis callus from the cell suspension named AT2 and found that the circadian rhythms were impaired. The circadian waveforms are similar to intact plants in the light/dark cycles, but the circadian expression in the AT2 callus stopped in the free-running, constant light conditions. Temperature cycles could drive the rhythmic expression in constant conditions, but there were novel peaks at the point of temperature transitions unique to each clock gene. We found that callus freshly induced from seedlings had normal oscillations, like intact plants, suggesting that the loss of the circadian oscillation in the AT2 callus was specific to this callus. We determined that neither the media composition nor the source of the AT2 callus caused this disruption. We observed that ELF3 expression was not differentially expressed between dawn and dusk in both entrained, light-dark cycles and constant light conditions. Overexpression of ELF3 in the AT2 callus partially recovers the circadian oscillation in the AT2 callus. This work shows that while callus and cell suspension cultures can be valuable tools for investigating plant responses, careful evaluation of their phenotype is important. Moreover, the altered circadian rhythms under constant light and temperature cycles in the AT2 callus could be useful backgrounds to understand the connections driving circadian oscillators and light and temperature sensing at the cellular level.

plant biology↗

bioRxiv2020

Detection of Gray Mold in Plants Using a Multispectral Imaging System

Gray mold disease caused by the fungus Botrytis cinerea damages many crop hosts worldwide and is responsible for heavy economic losses. Early diagnosis and detection of the disease would allow for more effective crop management practices to prevent outbreaks in field or greenhouse settings. Furthermore, having a simple, non-invasive way to quantify the extent of gray mold disease is important for plant pathologists interested in quantifying infection rates. In this paper, we design and build a multispectral imaging system for discriminating between leaf regions, infected with gray mold, and those that remain unharmed on a lettuce (Lactuca spp.) host. First, we describe a method to select two optimal (high contrast) spectral bands from continuous hyperspectral imagery (450-800 nm). We then built a system based on these two spectral bands, located at 540 and 670 nm. The resultant system uses two cameras, with a narrow band-pass spectral filter mounted on each, to measure the multispectral reflectance of a lettuce leaf. The two resulting images are combined using a normalized difference calculation that produces a single image with high contrast between the leaves infected and healthy regions. A classifier was then created based on the thresholding of single pixel values. We demonstrate that this simple classification produces a true positive rate of 95.25% with a false positive rate of 9.316%.

bioengineering↗

bioRxiv2016

Incorporating the Rate of Transcriptional Change Improves Construction of Transcription Factor Regulatory Networks

Transcriptional regulatory networks (TRNs) can be developed by computational approaches that infer regulator-target gene interactions from transcriptional assays. Successful algorithms that generate predictive, accurate TRNs enable the identification of regulator-target relationships in conditions where experimentally determining regulatory interactions is a challenge. Improving the ability of TRNs to successfully predict known regulator-target relationships in model species will enhance confidence in applying these approaches to determine regulator-target interactions in non-model species where experimental validation is challenging. Many transcriptional profiling experiments are performed across multiple time points; therefore we sought to improve regulator-target predictions by adjusting how time is incorporated into the network. We created ExRANGES, which incorporates Expression in a Rate-Normalized GEne Specific manner that adjusts how expression data is provided to the network algorithm. We tested this on a two different network construction approaches and found that ExRANGES prioritizes targets differently than traditional expression and improves the ability of these networks to accurately predict known regulator targets. ExRANGES improved the ability to correctly identify targets of transcription factors in large data sets in four different model systems: mouse, human, Arabidopsis, and yeast. Finally, we examined the performance of ExRANGES on a small data set from field-grown Oryza sativa and found that it also improved the ability to identify known targets even with a limited data set.\n\nAuthor SummaryTo understand how organisms can turn a collection of genes into a physiological response, we need to understand how certain genes are turned on and off. In model organisms, the ability to identify direct targets of transcription factors via ChIP-Seq in a high-throughput manner has advanced our understanding of transcriptional regulatory networks and how organisms regulate gene expression. However, for non-model organisms, it remains a challenge to identify TF-target relationships through experimental approaches such as ChIP-Seq. Without this information, the ability to understand regulatory control is limited. Computational approaches to identify regulator-target relationships in silico from easily attainable transcriptional data offer a solution. Several approaches exist for identifying gene regulatory networks, including many that take advantage of time series data. Most of these approaches weigh the relationship between regulators and putative targets at all time points equally. However, many regulators may control a single target in response to different inputs. In our approach, we focus on the association between regulators and targets primarily at times when there is a significant change in expression. ExRANGES essentially weights the expression value of each time point by the slope change after that time point so that relationships between regulators and targets are emphasized at the time points when the transcript levels are changing. This change in input into network identification algorithms improves the ability to predict regulator-target interactions and could be applied to many different algorithms We hope this improvement will increase the ability to identify regulators of interest in non-model species.

bioinformatics↗