Biology subjects

Stone, E. A.

Publications and source records attributed to Stone, E. A..

6 recordsLinked to original sources

bioRxiv2026

Combining 3D-multispectral and hyperspectral imaging to identify environmental stress treatments imposed during plant growth

Non-invasive, high-throughput phenotyping tools are needed that can identify environmental effects on plant structure and function to diagnose factors responsible for reduced growth in commercial and non-commercial settings. In this study, we explored whether the integration of 3D-multispectral (3D) and 2D-hyperspectral imaging (HSI), aided by machine learning (ML), could be used to identify environmental stress treatments imposed during plant growth. Controlled environment-grown Nicotiana Benthamiana plants were subjected to a range of abiotic treatments - including different growth irradiances, heat treatment and drought stress - with the treatments resulting in differences in shoot height, biomass, leaf area and spectral reflectance. ML models were trained to identify these treatments using morphological and spectral traits measured at 27, 29, 31, and 34 days after sowing (DAS). A 3D-multispectral scanner was used to obtain information on plant height, biomass, and leaf area. A visible and near-infrared (VNIR) HSI camera provided detailed spectral information for deriving spectral indices including the Normalised Difference Vegetation Index (NDVI), Photochemical Reflectance Index (PRI) and Normalized Difference Red Edge (NDRE). Manual measurements provided baseline comparative data. The 3D-multispectral scanner reliably estimated above-ground traits, with high correlations between manual and scanner-derived measurements. The ML models accurately differentiated among environmental stress treatments, with the fused 3D+HSI model achieving the best overall predictive performance across all evaluated metrics compared with models based on either imaging modality alone. Results demonstrated the effectiveness of combining 3D-multispectral and 2D-HSI data with ML analyses for non-destructive, high-throughput phenotyping. The integration of these techniques enabled non-destructive, high-throughput identification of environmental stress treatments imposed during plant growth.

plant biology↗

bioRxiv2026

Transposable element disruption of a second thyroglobulin-like gene confers Vip3Aa resistance in Helicoverpa armigera

BackgroundThe cotton bollworm Helicoverpa armigera is a major global pest controlled by genetically engineered crops expressing Bacillus thuringiensis (Bt) toxins, including Vip3Aa. While Vip3Aa is widely deployed, the genetic basis of resistance remains poorly understood. Previous work identified disruption of a thyroglobulin-like gene (HaVipR1) as one mechanism of resistance, suggesting additional loci may be involved. ResultsUsing linkage analysis, transcriptomics, long-read sequencing, and CRISPR-Cas9 gene editing, we identify a second thyroglobulin-like gene, HaVipR2, as a novel mediator of Vip3Aa resistance. Resistance in a field-derived H. armigera line was shown to be monogenic, recessive, and autosomal, mapping to chromosome 29. Long-read sequencing revealed a [~]16 kb transposable element insertion disrupting HaVipR2, which was undetectable using standard short-read approaches. CRISPR-Cas9 knockout of HaVipR2 conferred >900-fold resistance, confirming its causal role. Comparative analyses show that HaVipR1 and HaVipR2 share conserved domain architecture, indicating that thyroglobulin-domain proteins represent a recurrent target of resistance evolution. ConclusionsOur findings establish thyroglobulin-domain proteins as a new class of Bt resistance genes in Lepidoptera and demonstrate that transposable element insertions can drive adaptive resistance while evading detection by conventional methods. These results highlight the importance of long-read sequencing and accurate genome annotation for resistance monitoring and provide new insights into the molecular basis and evolution of Vip3Aa resistance.

genomics↗

bioRxiv2025

Path2Omics: Enhanced transcriptomic and methylation prediction accuracy from tumor histopathology

Precision oncology is becoming increasingly integral to clinical practice, demonstrating notable improvements in treatment outcomes. While molecular data provide comprehensive insights, obtaining such data remains costly and time-consuming. To address this challenge, we developed Path2Omics, a deep learning model that predicts gene expression and methylation from histopathology for 23 cancer types. Path2Omics was trained on 20,497 slides (9,456 formalin-fixed and paraffin-embedded (FFPE) and 11,041 fresh frozen (FF)) from 8,007 patients across 23 The Cancer Genome Atlas cohorts. When tested on FFPE slides, the most readily available format in clinical pathology practice, the integrated model outperformed its individual FF and FFPE components, robustly predicting nearly 5,000 genes on average, approximately five times more than our recently published DeepPT model. Externally evaluated on seven independent cohorts, Path2Omics robustly predicted the expression of approximately 4,400 genes, yielding a 30% increase over the FFPE model alone. Finally, we demonstrate that the inferred gene expression is nearly as effective as the actual values in predicting patient survival and treatment response. These results lay the basis for using Path2Omics to advance precision oncology from histopathology slides in a speedy and cost-effective manner. Statement of significancePath2Omics is a deep learning model that accurately predicts gene expression and methylation from histopathology slides across 23 cancer types. Unlike existing approaches that rely solely on FFPE slides for training, Path2Omics leverages both FFPE and FF slides by constructing two separate models and integrating them. Downstream analyses show that the inferred values from Path2Omics are nearly as effective as actual values in predicting patient survival and treatment response.

bioinformatics↗

bioRxiv2024

Dynamic metal coordination controls chemoselectivity in radical halogenases

The activation of inert C(sp3)-H bonds by non-heme Fe enzymes plays a key role in metabolism, epigenetics, and signaling, while providing a powerful biocatalytic platform for the chemical synthesis of molecules with increased sp3 complexity. In this context, FeII/-ketoglutarate-dependent radical halogenases represent a broadly interesting system, as they are uniquely capable of carrying out transfer of a diverse array of bound anions following C-H activation. Here, we provide the first experimental evidence that bifurcation of H-atom abstraction and radical rebound is driven both by the ability of a dynamic metal coordination sphere to reorganize as well as by a second-sphere hydrogen-bond network where only two residues (Asn224 and Ile151) are necessary and sufficient. The identification of this minimal motif provides a paradigm for understanding the evolution of catalytic plasticity in these enzymes and yields new insight into the design principles by which to expand their reaction scope.

biochemistry↗

bioRxiv2022

Synthetic lethality-based prediction of cancer treatment response from histopathology images

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Importantly, ENLIGHT-DeepPT successfully predicts true responders in five independent patients cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, trained and tested on the same cohort in cross validation. Its future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries. Statement of SignificanceENLIGHT-DeepPT is the first approach shown to successfully predict response to multiple targeted and immune cancer therapies from H&E slides. In distinction from all previous H&E slides prediction approaches, it does not require supervised training on a specific cohort for each drug/indication treatment but is trained to predict expression on the TCGA cohort and then can predict response to an array of treatments without any further training. ENLIGHT-DeepPT can provide rapid treatment recommendations to oncologists and help advance precision oncology in underserved regions and low-income countries.

cancer biology↗

bioRxiv2017

Private haplotype barcoding facilitates inexpensive high-resolution genotyping of multiparent crosses

Inexpensive, high-throughput sequencing has led to the generation of large numbers of sequenced genomes representing diverse lineages in both model and non-model organisms. Such resources are well suited for the creation of new multiparent populations to identify quantitative trait loci that contribute to variation in phenotypes of interest. However, despite significant drops in per-base sequencing costs, the costs of sample handling and library preparation remain high, particularly when many samples are sequenced. We describe a novel method for pooled genotyping of offspring from multiple genetic crosses, such as those that that make up multiparent populations. Our approach, which we call \"private haplotype barcoding\" (PHB), utilizes private haplotypes to deconvolve patterns of inheritance in individual offspring from mixed pools composed of multiple offspring. We demonstrate the efficacy of this approach by applying the PHB method to whole genome sequencing of 96 segregants from 12 yeast crosses, achieving over a 90% reduction in sample preparation costs relative to non-pooled sequencing. In addition, we implement a hidden Markov model to calculate genotype probabilities for a generic PHB run and a specialized hidden Markov model for the yeast crosses that improves genotyping accuracy by making use of tetrad information. Private haplotype barcoding holds particular promise for facilitating inexpensive genotyping of large pools of offspring in diverse non-model systems.

genetics↗