Biology subjects

Taylor, S. D.

Publications and source records attributed to Taylor, S. D..

12 recordsLinked to original sources

bioRxiv2026

Systems serology of responses against tumor antigens in ovarian cancer reveal disrupted Fc-mediated immunity

High-grade serous ovarian cancer (HGSOC) represents 75% of ovarian cancer cases and 80% of deaths, with most patients relapsing despite initial treatment response. The limited effectiveness of immunotherapies in HGSOC indicates urgent need for novel therapeutic approaches. HGSOC patients produce tumor-binding autoantibodies (TBAs) with high tumor selectivity. Since effective antibody-mediated tumor cell killing requires Fc domain interactions with immune cells, we hypothesized that, although TBAs recognize tumor cells, they might still poorly elicit cell killing responses. Using a systems serology approach, we profiled TBA subclass and biophysical interactions with Fc receptors in HGSOC, comparing them to antiviral antibody responses. TBAs were consistently identified within ascites and serum and were heterogeneous in subclass composition. However, TBAs consistently lacked the capacity to bind Fc{gamma}RIIIa despite abundant interaction with Fc{gamma}RIIa and poorly elicited antibody-dependent cellular cytotoxicity, suggesting their Fc features prevent cell killing responses. Restoring Fc{gamma}RIIIa interaction may be a promising therapeutic approach in HGSOC. HighlightsO_LITBAs in ovarian carcinoma patients consistently lack interaction with Fc{gamma}RIIIa C_LIO_LIAscites- and serum-derived TBAs have heterogeneous subclass composition C_LIO_LISystems analysis shows complex serologic differences between TBAs and antiviral responses C_LIO_LIPatient-expressed TBAs demonstrate little antibody-dependent cellular cytotoxicity C_LI

cancer biology↗

bioRxiv2023

Dissecting signaling regulators driving AXL-mediated bypass resistance and associated phenotypes by phosphosite perturbations

Receptor tyrosine kinase (RTK)-targeted therapies are often effective but invariably limited by drug resistance. A major mechanism of acquired resistance involves "bypass" switching to alternative pathways driven by non-targeted RTKs that restore proliferation. One such RTK is AXL whose overexpression, frequently observed in bypass resistant tumors, drives both cell survival and associated malignant phenotypes such as epithelial-to-mesenchymal (EMT) transition and migration. However, the signaling molecules and pathways eliciting these responses have remained elusive. To explore these coordinated effects, we generated a panel of mutant lung adenocarcinoma PC9 cell lines in which each AXL intracellular tyrosine residue was mutated to phenylalanine. By integrating measurements of phosphorylation signaling and other phenotypic changes associated with resistance through multivariate modeling, we mapped signaling perturbations to specific resistant phenotypes. Our results suggest that AXL signaling can be summarized into two clusters associated with progressive disease and poor clinical outcomes in lung cancer patients. These clusters displayed favorable Abl1 and SFK motifs and their phosphorylation was consistently decreased by dasatinib. High-throughput kinase specificity profiling showed that AXL likely activates the SFK cluster through FAK1 which is known to complex with Src. Moreover, the SFK cluster overlapped with a previously established focal adhesion kinase (FAK1) signature conferring EMT-mediated erlotinib resistance in lung cancer cells. Finally, we show that downstream of this kinase signaling, AXL and YAP form a positive feedback loop that sustains drug tolerant persister cells. Altogether, this work demonstrates an approach for dissecting signaling regulators by which AXL drives erlotinib resistance-associated phenotypic changes. One-sentence summaryA systems biology approach elucidates the signaling pathways driving AXL-mediated erlotinib resistance in lung cancer.

cancer biology↗

bioRxiv2022

HOXD13 is a direct EWS-FLI1 target and moderates fusion-dependent transcriptional states

Oncogenic fusion proteins display exquisite tissue specificity, revealing that malignant transformation requires cooperation with cell-autonomous factors. Recent studies have also demonstrated that tumorigenicity of Ewing sarcoma requires precise regulation of the transcriptional activity of the EWS-FLI1 oncogenic driver. Here we show that the developmentally and anatomically restricted transcription factor HOXD13 is a direct target of EWS-FLI1. Transcriptomic and CUT&RUN studies revealed that HOXD13 binds active, fusion-bound enhancers, resulting in altered expression of EWS-FLI1-induced targets. More strikingly, HOXD13 was found to bind and activate cis-regulatory regions of genes that are normally repressed by EWS-FLI1. Single-cell sequencing demonstrated marked intra-tumoral heterogeneity of HOXD13 transcriptional activity and revealed that antagonism between HOXD13-mediated gene activation and EWS-FLI1-dependent gene repression confers a spectrum of transcriptional cell states along a mesenchymal axis. Thus, HOXD13 serves as an internal rheostat for EWS-FLI1 activity, providing a paradigm for tissue-specific transcription factors as critical partners in fusion-driven cancers.

cancer biology↗

bioRxiv2021

Deep learning models for identifying crop and field attributes from near surface cameras

Near surface cameras, such as those in the PhenoCam network, are a common source of ground truth data in modelling and remote sensing studies. Despite having locations across numerous agricultural sites, few studies have used near surface cameras to track the unique phenology of croplands. Due to management activities, crops do not have a natural vegetation cycle which many phenological extraction methods are based on. For example, a field may experience abrupt changes due to harvesting and tillage throughout the year. A single camera can also record several different plants due to crop rotations, fallow fields, and cover crops. Current methods to estimate phenology metrics from image time series compress all image information into a relative greenness metric, which discards a large amount of contextual information. This can include the type of crop present, whether snow or water is present on the field, the crop phenology, or whether a field lacking green plants consists of bare soil, fully senesced plants, or plant residue. Here we developed a modelling workflow to create a daily time series of crop type and phenology, while also accounting for other factors such as obstructed images and snow covered fields. We used a mainstream deep learning image classification model, VGG16. Deep learning classification models do not have a temporal component, so to account for temporal correlation among images our workflow incorporates a hidden markov model in the post-processing. The initial image classification model had out of sample F1 scores of 0.83-0.85, which improved to 0.86-0.91 after all post-processing steps. The resulting time series show the progression of crops from emergence to harvest, and can serve as a daily, local scale dataset of field states and phenological stages for agricultural research.

plant biology↗

bioRxiv2021

Constraints and Opportunities for Detecting Land Surface Phenology in Drylands

Land surface phenology (LSP) enables global scale tracking of ecosystem processes, but its utility is limited in drylands due to low vegetation cover and resulting low annual amplitudes of vegetation indices (VIs). Due to the importance of drylands for biodiversity, food security, and the carbon cycle it is necessary to understand limitations in measuring dryland dynamics. Here, using simulated data and multi-temporal unmanned aerial vehicle (UAV) imagery of a desert shrubland, we explore the feasibility of detecting LSP with respect to fractional vegetation cover, plant functional types, VI uncertainty, and two different detection algorithms. Using simulated data we found that plants with distinct VI signals, such as deciduous shrubs, can require up to 60% fractional cover to consistently detect LSP. Evergreen plants, with lower seasonal VI amplitude, require considerably higher cover and can have undetectable phenology even with 100% vegetation cover. Our evaluation of two algorithms showed that neither performed the best in all cases. Even with adequate cover, biases in phenological metrics can still exceed 20 days, and can never be 100% accurate due to VI uncertainty from shadows, sensor view angle, and atmospheric interference. We showed how high-resolution UAV imagery enables LSP studies in drylands, and highlighted important scale effects driven by within canopy VI variation. With high-resolution imagery the open canopies of drylands are beneficial as they allow for straightforward identification of individual plants, enabling the tracking of phenology at the individual level. Drylands thus have the potential to become an exemplary environment for future LSP research.

ecology↗

bioRxiv2021

Structured decomposition improves systems serology prediction and interpretation

Systems serology measurements provide a comprehensive view of humoral immunity by profiling both the antigen-binding and Fc properties of antibodies. Identifying patterns in these measurements will help to guide vaccine and therapeutic antibody development, and improve our understanding of disorders. Furthermore, consistent patterns across diseases may reflect conserved regulatory mechanisms; recognizing these may help to combine modalities such as vaccines, antibody-based interventions, and other immunotherapies to maximize protection. A common feature of systems serology studies is structured biophysical profiling across disease-relevant antigen targets, properties of antibodies interaction with the immune system, and serological samples. These are typically produced alongside additional measurements that are not antigen-specific. Here, we report a new form of tensor factorization, total tensor-matrix factorization (TMTF), which can greatly reduce these data into consistently observed patterns by recognizing the structure of these data. We use a previous study of HIV-infected subjects as an example. TMTF outperforms standard methods like principal components analysis in the extent of reduction possible. Data reduction, in turn, improves the prediction of immune functional responses, classification of subjects based on their HIV control status, and interpretation of these resulting models. Interpretability is improved specifically by applying further data reduction, separation of the Fc from antigen-binding effects, and recognizing consistent patterns across individual measurements. Therefore, we propose that TMTF will be an effective general strategy for exploring and using systems serology. Summary pointsO_LIStructured decomposition provides substantial data reduction without loss of information. C_LIO_LIPredictions based on decomposed factors are accurate and robust to missing measurements. C_LIO_LIDecomposition structure improves the interpretability of modeling results. C_LIO_LIDecomposed factors represent meaningful patterns in the HIV humoral response. C_LI

immunology↗

bioRxiv2020

Influence of climate forecasts, data assimilation, and uncertainty propagation on the performance of near-term phenology forecasts

Evaluation of ecological forecasts is a vital step in the continuous improvement of near-term ecological forecasts. Here we performed a thorough evaluation of a near-term phenological forecast system which has been operating for several years. We evaluated point forecast accuracy and the reliability of the prediction intervals. We also tested the contribution of upstream climate forecasts on phenology forecast proficiency. We found that 9 month climate forecasts contributed little skill overall, though some species did benefit from them. The assimilation of observed winter and spring temperature provided the largest improvement of forecast skill throughout the spring. We also found that phenology forecast prediction intervals were most robust when uncertainty was propagated from climate, phenological model, and model parameters as opposed to using climate uncertainty alone. Our analysis points the way toward several potential improvements to the forecasting system, which can be re-evaluated at a future date in a continuous cycle of forecast refinement.

ecology↗

bioRxiv2019

Opportunistically collected photographs can be used to estimate large-scale phenological trends

PremiseResearch on large-scale patterns of phenology have utilized multiple sources of data to analyze the timing of events such as flowering, fruiting, and leaf out. In-situ observations from standardized surveys are ideal, but remain spatially sparse. Herbarium records and phenology-focused citizen science programs provide a source of historic data and spatial replication, but the sample sizes for any one season are still relatively low. A novel and rapidly growing source of broad-scale phenology data are photographs from the iNaturalist platform, but methods utilizing these data must generalize to a range of different species with varying season lengths and occurring across heterogenous areas. They must also be robust to different sample sizes and potential biases toward well travelled areas such as roads and towns.\n\nMethods/ResultsWe developed a spatially explicit model, the Weibull Grid, to estimate flowering onset across large-scales, and utilized a simulation framework to test the approach using different phenology and sampling scenarios. We found that the model is ideal when the underlying phenology is non-linear across space. We then use the Weibull Grid model to estimate flowering onset of two species using iNaturalist photographs, and compare those estimates with independent observations of greenup from the Phenocam network. The Weibull Grid model estimate consistently aligned with Phenocam greenup across four years and broad latitudes.\n\nConclusioniNaturalist observations can considerably increase the amount of phenology observations and also provide needed spatial coverage. We showed here they can accurately describe large-scale trends as long as phenological and sampling processes are considered.

ecology↗

bioRxiv2019

Rethinking global carbon storage potential of trees. A comment on Bastin et. al 2019

Key MessageBastin et al. 2019 used flawed assumptions in calculating the carbon storage of restored forests worldwide, resulting in a gross overestimate. ContextBastin et al. 2019 use two flawed assumptions: 1) that the area suitable for restoration does not contain any carbon currently, and 2) that soil organic carbon (SOC) from increased canopy cover will accumulate quickly enough to mitigate anthropogenic carbon emissions. AimsWe re-evaluated the potential carbon storage worldwide using empirical relationships of tree cover and carbon. Methods and ResultsWe use global datasets of tree cover, soil organic carbon, and above ground biomass to estimate the empirical relationships of tree cover and carbon stock storage. A more realistic range of global carbon storage potential is between 71.7 and 75.7 GtC globally, with a large uncertainty associated with SOC. This is less than half of the original 205 GtC estimate. ConclusionThe potential global carbon storage of restored forests is much less than that estimated by Bastin et al. 2019. While we agree on the value of assessing global reforestation potential, we suggest caution in considering it the most effective strategy to mitigate anthropogenic emissions.

ecology↗

bioRxiv2019

Automated data-intensive forecasting of plant phenology throughout the United States

Phenology - the timing of cyclical and seasonal natural phenomena such as flowering and leaf out - is an integral part of ecological systems with impacts on human activities like environmental management, tourism, and agriculture. As a result, there are numerous potential applications for actionable predictions of when phenological events will occur. However, despite the availability of phenological data with large spatial, temporal, and taxonomic extents, and numerous phenology models, there has been no automated species-level forecasts of plant phenology. This is due in part to the challenges of building a system that integrates large volumes of climate observations and forecasts, uses that data to fit models and make predictions for large numbers of species, and consistently disseminates the results of these forecasts in interpretable ways. Here we describe a new near-term phenology forecasting system that makes predictions for the timing of budburst, flowers, ripe fruit, and fall colors for 78 species across the United States up to 6 months in advance and is updated every four days. We use the lessons learned in developing this system to provide guidance developing large-scale near-term ecological forecast systems more generally, to help advance the use of automated forecasting in ecology.

ecology↗

bioRxiv2018

Comparison of large-scale citizen science data and long-term study data for phenology modeling

Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and inter-annual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science National Phenology Network (NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built process based phenology models for species common to each dataset. From these models we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two datasets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for NPN data had the lowest error when using models built from the NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. Accordingly, the choice of dataset depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale NPN data, which has high spatial representation and a large regional species pool. LTER datasets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models which integrate the strengths of both datasets.

ecology↗

bioRxiv2018

Developing an automated iterative near-term forecasting system for an ecological study

O_LIMost forecasts for the future state of ecological systems are conducted once and never updated or assessed. As a result, many available ecological forecasts are not based on the most up-to-date data, and the scientific progress of ecological forecasting models is slowed by a lack of feedback on how well the forecasts perform.\nC_LIO_LIIterative near-term ecological forecasting involves repeated daily to annual scale forecasts of an ecological system as new data becomes available and regular assessment of the resulting forecasts. We demonstrate how automated iterative near-term forecasting systems for ecology can be constructed by building one to conduct monthly forecasts of rodent abundances at the Portal Project, a long-term study with over 40 years of monthly data. This system automates most aspects of the six stages of converting raw data into new forecasts: data collection, data sharing, data manipulation, modeling and forecasting, archiving, and presentation of the forecasts.\nC_LIO_LIThe forecasting system uses R code for working with data, fitting models, making forecasts, and archiving and presenting these forecasts. The resulting pipeline is automated using continuous integration (a software development tool) to run the entire pipeline once a week. The cyberinfrastructure is designed for long-term maintainability and to allow the easy addition of new models. Constructing this forecasting system required a team with expertise ranging from field site experience to software development.\nC_LIO_LIAutomated near-term iterative forecasting systems will allow the science of ecological forecasting to advance more rapidly and provide the most up-to-date forecasts possible for conservation and management. These forecasting systems will also accelerate basic science by allowing new models of natural systems to be quickly implemented and compared to existing models. Using existing technology, and teams with diverse skill sets, it is possible for ecologists to build automated forecasting systems and use them to advance our understanding of natural systems.\nC_LI

ecology↗