Biology subjects

Chapman, S.

Publications and source records attributed to Chapman, S..

11 recordsLinked to original sources

bioRxiv2026

A hierarchical Bayesian model for dynamic phenotypes in multi-environment trials with genotype by environment interaction

New phenotyping tools produce longitudinal phenotypes that describe plant growth and development. Estimates for the parameters that describe the dynamics of such longitudinal phenotypes can be incorporated as predictors in models for static target traits like yield and biomass. Within a hierarchical Bayesian context, we present a differential equation (DE) model for log-logistic growth in longitudinal traits for sets of genotypes across multiple environments. The DE parameters coincide with a midpoint, a slope, and an asymptote at the end of growth and development, i.e., final biomass. Each DE parameter in our hierarchical model results from contributions of genotype, environment, and G x E. This formulation allows one to assess and quantify the genotype-specificity of the DE parameters. When none of the DE parameters in our model requires a G x E term, the dynamic parameters characterizing the particular trait will be useful features for inclusion in prediction models for yield or biomass. We fitted a DE model to APSIM-simulated longitudinal wheat biomass data and quantified the contribution of genotype, environment and G x E to the phenotypic variation in the DE parameters. Only the upper asymptote showed clear G x E. Estimates for genotypic and G x E parameters within the DE parameters were used to characterize genotypes. Estimates for environmental and G x E effects formed inputs for environmental characterization.

plant biology↗

bioRxiv2025

Rat and rabbit whole embryo culture as a new approach method for unlabeled therapeutic antisense oligonucleotide hazard identification with no requirement for microinjection or assisted transfection

The developmental hazard screening of oligonucleotide therapeutics (ONTs) presents challenges due to their frequent lack of pharmacology in nonclinical species and embryofetal exposure is presumed to be limited in vivo. This study demonstrates that direct culture in media containing mipomersen, a 2-O-methoxyethyl phosphorothioated antisense oligonucleotide (ASO), results in dose-responsive morphological changes in rat and rabbit whole embryo culture (WEC). Automated miRNAscope in situ hybridization was used to confirm dose-dependent embryonic exposure and visualize the distribution pattern of mipomersen in the embryo and extra-embryonic membranes, suggesting that ONTs may enter the umbilical vasculature and pass into embryo circulation. Neither microinjections nor assisted transfections were required to achieve embryonic exposure. These findings support the utility of WEC as a new approach method (NAM) for developmental hazard identification of ONTs. WEC could complement or partially replace in vivo studies, reducing animal use and required test material amounts, while enabling robust developmental hazard identification for ONTs. This work informs future safety assessment strategies and regulatory guidance for ONTs.

pharmacology and toxicology↗

bioRxiv2025

Isolation and proteomic analysis of intracellular vesicles from the potato late blight pathogen Phytophthora infestans

The oomycete Phytophthora infestans, a filamentous plant pathogen belonging to the kingdom Stramenopila, is the causal agent of potato late blight resulting in annual crop losses amounting to billions of dollars worldwide. Key to the success of this pathogen are the effector proteins it secretes during infection, whose functions include breaching the plant cell wall and suppression/evasion of plant immune responses. Currently, little is known regarding the intracellular trafficking of effectors enroute to secretion. In this study, we developed a robust density gradient ultracentrifugation method to isolate intracellular vesicles from P. infestans and to separate diverse vesicle populations based on buoyancy for the purpose of identifying vesicle-associated proteins by mass spectrometry. Gene Ontology Enrichment Analysis of proteins identified in buoyant fractions revealed enrichment for membrane-associated proteins and proteins involved in vesicle trafficking. Buoyant fractions were also enriched in RXLR class effectors, carbohydrate-active enzymes, and secretory proteins possessing N-terminal signal peptides, all representing potential vesicle cargo. In addition, previously identified P. infestans extracellular vesicle markers were also present. Unravelling how effector proteins are trafficked for secretion during infection is a critical step in developing robust strategies for combating potato late blight disease; the proteomics dataset and method presented here are valuable resources from which potential biomarkers for P. infestans vesicles can be identified for future studies towards this end.

cell biology↗

bioRxiv2025

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

Computer vision is increasingly used in farmers fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimetre ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although todays AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of wheat organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90%. However, the precision for stems with 54% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.

plant biology↗

bioRxiv2024

MitoMAMMAL: a genome scale model of mammalian mitochondria predicts cardiac and BAT metabolism

Mitochondria perform several essential functions in order to maintain cellular homeostasis and mitochondrial metabolism is inherently flexible to allow correct function in a wide range of tissues. Dysregulated mitochondrial metabolism can therefore affect different tissues in different ways which presents experimental challenges in understanding the pathology of mitochondrial diseases. System-level metabolic modelling is therefore useful in gaining in-depth insights into tissue-specific mitochondrial metabolism, yet despite the mouse being a common model organism used in research, there is currently no mouse specific mitochondrial metabolic model available. In this work, building upon the similarity between human and mouse mitochondrial metabolism, we have created mitoMammal, a genome-scale metabolic model that contains human and mouse specific gene-product reaction rules. MitoMammal is therefore able to model mouse and human mitochondrial metabolism. To demonstrate this feature, using an adapted E-Flux2 algorithm, we first integrated proteomic data extracted from mitochondria of isolated mouse cardiomyocytes and mouse brown adipocyte tissue. We then integrated transcriptomic data from in vitro differentiated human brown adipose cells and modelled the context specific metabolism using flux balance analysis. In all three simulations, mitoMammal made mostly accurate, and some novel predictions relating to energy metabolism in the context of cardiomyocytes and brown adipocytes. This demonstrates its usefulness in research relating to cardiac disease and diabetes in both mouse and human contexts.

systems biology↗

bioRxiv2024

Lack of an atypical PDR transporter generates an immunogenic Cryptococcus neoformans strain that drives a dysregulated and lethal immune response in murine lungs

Cryptococcus neoformans is an opportunistic fungal pathogen responsible for >150,000 deaths every year with a mortality rate as high as 81%. This high medical burden is due, in part, to an incomplete understanding of its pathogenesis. In a previous study, we identified a cryptococcal atypical ATP-binding cassette (ABC) pleiotropic drug resistance (PDR) transporter, PDR6, that affected antifungal resistance and host interactions. Here, we follow-up on the role of PDR6 in cryptococcal virulence. In vivo, mice infected with the pdr6{Delta} strain display altered symptomatology and disease progression. Specifically, we observed a significant increase in the innate immune cell populations in the pdr6{Delta}-infected mice when compared to their WT-infected littermates. Furthermore, quantification of pulmonary cytokines/chemokines revealed a robust increase of pro-inflammatory cytokines in mice infected with the pdr6{Delta} mutant strain. Despite the documented sensitivity of the pdr6{Delta} strain to azole antifungal drugs, the treatment of pdr6{Delta}-infected animals with antifungals did not affect survival, yet treatment with a corticosteroid significantly extended survival, highlighting the importance of a balanced/controlled host immune response. Results with mice that mount opposing immune responses supports out hypothesis that the pdr6{Delta} strain induces a hyper-inflammatory immune response, and that the mice succumb to immune-dependent tissue damage rather than the fungal burden. This altered immune response is driven, in part, by changes in the mutants surface. Taken together, this study provides insights regarding cryptococcal pathogenesis and highlights additional functions of PDR-type ABC transporters in pathogenic fungi. IMPORTANCEYeasts of the Cryptococcus genus, especially C. neoformans, can cause disease with unacceptably high mortality. This is due to delays in diagnostics, ineffective treatments, and an incomplete understanding of the interactions between this fungus and our immune system. In this study, we expand our knowledge of the biological function of the PDR6 gene, particularly its effect on modulating the hosts immune response. Normally, C. neoformanss infections are characterized by an anti-inflammatory response that is unable to control the yeast. In the absence of PDR6, the response to the infection is a dysregulated pro-inflammatory response that initially controls the fungi but eventually results in death of the host due to too much tissue damage. This is due, in part, to an altered fungal surface. Given the dual role of PDR6 in modulating antifungal sensitivity and immune responses, this work provides important insights that may lead to new or improved therapeutics.

microbiology↗

bioRxiv2023

A spectrum of altered non-rapid eye movement sleep in schizophrenia

BackgroundMultiple facets of sleep neurophysiology, including electroencephalography (EEG) metrics such as non-rapid eye movement (NREM) spindles and slow oscillations (SO), are altered in individuals with schizophrenia (SCZ). However, beyond group-level analyses which treat all patients as a unitary set, the extent to which NREM deficits vary among patients is unclear, as are their relationships to other sources of heterogeneity including clinical factors, illness duration and ageing, cognitive profiles and medication regimens. Using newly collected high density sleep EEG data on 103 individuals with SCZ and 68 controls, we first sought to replicate our previously reported (Kozhemiako et. al, 2022) group-level mean differences between patients and controls (original N=130). Then in the combined sample (N=301 including 175 patients), we characterized patient-to-patient variability in NREM neurophysiology. ResultsWe replicated all group-level mean differences and confirmed the high accuracy of our predictive model (Area Under the ROC Curve, AUC = 0.93 for diagnosis). Compared to controls, patients showed significantly increased between-individual variability across many (26%) sleep metrics, with patterns only partially recapitulating those for group-level mean differences. Although multiple clinical and cognitive factors were associated with NREM metrics including spindle density, collectively they did not account for much of the general increase in patient-to-patient variability. Medication regimen was a greater (albeit still partial) contributor to variability, although original group mean differences persisted after controlling for medications. Some sleep metrics including fast spindle density showed exaggerated age-related effects in SCZ, and patients exhibited older predicted biological ages based on an independent model of ageing and the sleep EEG. ConclusionWe demonstrated robust and replicable alterations in sleep neurophysiology in individuals with SCZ and highlighted distinct patterns of effects contrasting between-group means versus within-group variances. We further documented and controlled for a major effect of medication use, and pointed to greater age-related change in NREM sleep in patients. That increased NREM heterogeneity was not explained by standard clinical or cognitive patient assessments suggests the sleep EEG provides novel, nonredundant information to support the goals of personalized medicine. Collectively, our results point to a spectrum of NREM sleep deficits among SCZ patients that can be measured objectively and at scale, and that may offer a unique window on the etiological and genetic diversity that underlies SCZ risk, treatment response and prognosis.

neuroscience↗

bioRxiv2023

MitoMouse is a model reconstruction of murine mitochondrial metabolism

Mitochondria, apart from being the powerhouses of our cells, are key players in cellular metabolism and homeostasis. As a consequence, the core bioenergetics and metabolism of mitochondria are well studied considering the role that dysregulated mitochondrial metabolism plays in disease. Flux Balance Analysis, used in conjunction with metabolic model reconstructions is a powerful computational tool that predicts metabolism. The resulting quantitative descriptions of metabolic dysregulation not only allows current hypotheses to be tested in silico, but can also lead to novel model driven hypotheses that can be experimentally verified. Several metabolic reconstructions for human and mouse metabolism exist, but no model specific to mouse mitochondrial metabolism exists. Here, we have created a mouse-specific mitochondrial metabolic model, mitoMouse, which is based on the high-quality human MitoCore mode. MitoMouse contains 390 genes and 445 metabolites involved in 560 unique reactions, is able to model central carbon metabolism and has been extended to contain reduction of the CoQ complex of Oxidative Phosphorylation by the enzyme DHODH. MitoMOuse was validated to accurately model the important metabolic switch involving CoQ reduction resulting from increased malate import, as recently shown in mouse cardiac tissue. We expect this model to be of immense interest and relevance to researchers working on murine mitochondrial metabolism.

systems biology↗

bioRxiv2021

Integration of APSIM and PROSAIL models to develop more precise radiometric estimation of crop traits using deep learning

A major challenge for the estimation of crop traits (biophysical variables) from canopy reflectance is the creation of a high-quality training dataset. This can be addressed by using radiative transfer models (RTMs) to generate training dataset representing real-world data in situations with varying crop types and growth status as well as various observation configurations. However, this approach can lead to "ill-posed" problems related to assumptions in the sampling strategy and due to uncertainty in the model, resulting in unsatisfactory inversion results for retrieval of target variables. In order to address this problem, this research investigates a practical way to generate higher quality synthetic training data by integrating a crop growth model (CGM, in this case APSIM) with an RTM (in this case PROSAIL). This allows control of uncertainties of the RTM by imposing biological constraints on distribution and co-distribution of related variables. Subsequently, the method was theoretically validated on two types of synthetic dataset generated by PROSAIL or the coupling of APSIM and PROSAIL through comparing estimation precision for leaf area index (LAI), leaf chlorophyll content (Cab), leaf dry matter (Cm) and leaf water content (Cw). Additionally, the capabilities of current deep learning techniques using high spectral resolution hyperspectral data were investigated. The main findings include: (1) Feedforward neural network (FFNN) provided with appropriate configuration is a promising technique to retrieve crop traits from input features consisting of 1 nm-wide hyperspectral bands across 400-2500 nm range and observation configuration (solar and viewing angles), leading to a precise joint estimation for LAI (RMSE=0.061 m2 m-2), Cab (RMSE=1.42 {micro}g cm-2), Cm (RMSE=0.000176 g cm-2) and Cw (RMSE=0.000319 g cm-2); (2) For the aim of model simplification, a narrower range in 400-1100 nm without observation configuration in input of FFNN model provided less precise estimation for LAI (RMSE=0.087 m2 m-2), Cab (RMSE=1.92 {micro}g cm-2), Cm (RMSE=0.000299 g cm-2) and Cw (RMSE=0.001271 g cm-2); (3) The introduction of biological constraints in training datasets improved FFNN model performance in both average precision and stability, resulting in a much accurate estimation for LAI (RMSE=0.006 m2 m-2), Cab (RMSE=0.45 {micro}g cm-2), Cm (RMSE=0.000039 g cm-2) and Cw (RMSE=0.000072 g cm-2), and this improvement could be further increased by enriching sample diversity in training dataset.

ecology↗

bioRxiv2020

Modelling selection response in plant breeding programs using crop models as mechanistic gene-to-phenotype (CGM-G2P) multi-trait link functions

Plant breeding programs are designed and operated over multiple cycles to systematically change the genetic makeup of plants to achieve improved trait performance for a Target Population of Environments (TPE). Within each cycle, selection applied to the standing genetic variation within a structured reference population of genotypes (RPG) is the primary mechanism by which breeding programs make the desired genetic changes. Selection operates to change the frequencies of the alleles of the genes controlling trait variation within the RPG. The structure of the RPG and the TPE has important implications for the design of optimal breeding strategies. The breeders equation, together with the quantitative genetic theory behind the equation, informs many of the principles for design of breeding programs. The breeders equation can take many forms depending on the details of the breeding strategy. Through the genetic changes achieved by selection, the cultivated varieties of crops (cultivars) are improved for use in agriculture. From a breeding perspective, selection for specific trait combinations requires a quantitative link between the effects of the alleles of the genes impacted by selection and the trait phenotypes of plants and their breeding value. This gene-to-phenotype link function provides the G2P map for one to many traits. For complex traits controlled by many genes, the infinitesimal model for trait genetic variation is the dominant G2P model of quantitative genetics. Here we consider motivations and potential benefits of using the hierarchical structure of crop models as CGM-G2P trait link functions in combination with the infinitesimal model for the design and optimisation of selection in breeding programs.

genetics↗

bioRxiv2019

Conjugated linoleic acid as a novel insecticide targeting the agricultural pest Leptinotarsa decemlineata

The Colorado Potato Beetle, Leptinotarsa decemlineata, is a major agricultural pest of solanaceous crops in the United States. Historically, a multitude of insecticides have been used to control problematic populations. Due to increasing resistance to insecticides, novel compounds and methodologies are warranted for the control of beetle populations. Mixed-isomer conjugated linoleic acid has been studied in-depth for its beneficial properties to mammalian systems. At the same time, studies have demonstrated that conjugated linoleic acid can manipulate fatty acid composition in non-mammalian systems, resulting in embryo mortality. Consequently, experiments were conducted to assess the effects of foliar-applied conjugated linoleic acid on larval growth, embryogenesis, and feeding preference in Colorado potato beetle. Both maternal and deterrent effects of dietary conjugated linoleic acid were assessed. Conjugated linoleic acid demonstrated desirable insecticidal properties, including increased larval mortality, slowed larval development, antifeedant effects, and decreased egg viability after maternal ingestion.

ecology↗