Biology subjects

Gardner, L.

Publications and source records attributed to Gardner, L..

10 recordsLinked to original sources

bioRxiv2026

Network topology reveals distinct forms of developmental leverage in the Drosophila wing

Developmental gene regulatory networks reliably transform positional information into complex multicellular form, yet the organizational principles linking network architecture to developmental mechanism remain poorly understood. Here, we analyzed the Drosophila melanogaster wing developmental network to determine whether network topology reflects the distribution of developmental leverage during organogenesis. Integration of curated wing-development genes with high-confidence STRING interactions revealed five Hierarchical Layers of Developmental Control (HLDCs) associated with distinct topological and developmental roles. Organizer Centers, Signaling Scaffolds, and Pattern Implementers formed a forward-specification axis in which connectivity progressively contracted as positional information was transformed into increasingly localized developmental programs. Interface Coordinators departed from this hierarchy through disproportionate brokerage, whereas Local Modulators retained connectivity despite localized developmental scope. We propose that these complementary signatures reflect two regulatory architectures: 1) hierarchical information propagation that generates developmental identity and 2) distributed homeostatic regulation that coordinates and refines developmental outputs. Within Character Identity Modules (ChiMOs), this architecture links conserved patterning systems, Hox-defined contexts, and organ-specific kernels to reproducible morphology, providing a mechanistic hypothesis for developmental canalization and experimentally testable predictions.

developmental biology↗

bioRxiv2023

Cutaneous T cell lymphoma atlas reveals malignant Th2 cells supported by a B cell-rich microenvironment

Cutaneous T-cell lymphoma (CTCL) is a potentially fatal clonal malignancy of T cells primarily affecting the skin. The most common form of CTCL, mycosis fungoides (MF), can be difficult to diagnose resulting in treatment delay. The pathogenesis of CTCL is not fully understood due to limited data from patient studies. We performed single-cell RNA sequencing and spatial transcriptomics profiling of skin from patients with MF-type CTCL, and an integrated comparative analysis with human skin cell atlas datasets from healthy skin, atopic dermatitis and psoriasis. We reveal the co-optation of Th2-immune gene programmes by malignant CTCL cells and modelling of the tumour microenvironment to support their survival. We identify MHC-II+ fibroblast subsets reminiscent of lymph node T-zone reticular cells and monocyte-derived dendritic cells that can maintain Th2-like tumour cells. CTCL Th2-like tumour cells are spatially associated with B cells, forming aggregates reminiscent of tertiary lymphoid structures which are more prominent with progressive disease. Finally, we validated the enrichment of B cells in CTCL skin infiltrates and its association with disease progression across three independent patient cohorts. Our findings provide diagnostic aids, potential biomarkers for disease staging and therapeutic strategies for CTCL.

genomics↗

bioRxiv2023

Successful placentation in human pregnancy is regulated by reciprocal interactions between maternal uterine NK cells and fetal placental trophoblast

Fetal growth and development during human pregnancy depends on delivery of adequate maternal oxygen and nutrients to the fetus via the placenta. In humans, the balanced invasion of fetal placental trophoblast cells into the maternal uterine lining, where they interact with uterine natural killer cells (uNK), is thought to be critical for a successful pregnancy but exactly how this influences reproductive outcomes remains undefined. Here, we used our trophoblast organoid model and primary tissue samples to determine how uNK affect placentation. By locating potential interaction axes between primary trophoblast cells and uNK using single cell transcriptomics, and in vitro modelling of these interactions in trophoblast organoids, we identify a uNK-derived cytokine signal that promotes trophoblast differentiation by enhancing epithelial-mesenchymal transition and increasing trophoblast cells at the late stage of the invasive pathway. Moreover, it affects transcriptional programs involved in increasing blood flow, placental access to nutrients, and dampening inflammatory and adaptive immune responses, as well as gene signatures associated with disorders of pregnancy such as pre-eclampsia. Our findings shed new light on how optimal immunological interactions between maternal uNK cells and fetal trophoblast enhance reproductive success.

developmental biology↗

bioRxiv2022

Spatially resolved single-cell multiomics map of human trophoblast differentiation in early pregnancy

The relationship between the human placenta, the extraembryonic organ built by the fetus, and the decidua, the mucosal layer of the uterus, is essential to nurture and protect the fetus during pregnancy. Extravillous trophoblast cells (EVTs) anchor the placenta and infiltrate the decidua, transforming the maternal arteries into high conductance vessels. Defects in trophoblast invasion and arterial transformation established during early pregnancy underlie common pregnancy disorders such as pre-eclampsia. Despite its importance, how EVT invasion is regulated in humans is still unclear due the inaccessibility of the entire pregnant uterus and, until recently, a lack of reliable in vitro models. Here, we have generated a spatially-resolved multiomics single-cell atlas of the entire maternal-fetal interface including the myometrium, allowing us to resolve the full trajectory of trophoblast differentiation. We have used this cellular map to elucidate the main regulatory programmes mediating EVT invasion and show that they are preserved in trophoblast organoids. We define the transcriptomes of the final cell states of trophoblast invasion: placental bed giant cells (fused multinucleated EVTs) and endovascular EVTs (which form plugs inside the maternal arteries). We reconstruct the cell-cell communication events contributing to trophoblast invasion and GC formation, and define the dual role of interstitial EVTs and endovascular EVTs in mediating arterial transformation during early pregnancy. Together, our data provides a comprehensive analysis of postimplantation trophoblast differentiation in humans that can be used as a blueprint to design accurate multilineage placental in vitro models.

genomics↗

bioRxiv2021

A general deep learning model for bird detection in high resolution airborne imagery

Advances in artificial intelligence for computer vision hold great promise for increasing the scales at which ecological systems can be studied. The distribution and behavior of individuals is central to ecology, and computer vision using deep neural networks can learn to detect individual objects in imagery. However, developing supervised models for ecological monitoring is challenging because it needs large amounts of human-labeled training data, requires advanced technical expertise and computational infrastructure, and is prone to overfitting. This limits application across space and time. One solution is developing generalized models that can be applied across species and ecosystems. Using over 250,000 annotations from 13 projects from around the world, we develop a general bird detection model that achieves over 65% recall and 50% precision on novel aerial data without any local training despite differences in species, habitat, and imaging methodology. Fine-tuning this model with only 1000 local annotations increase these values to an average of 84% recall and 69% precision by building on the general features learned from other data sources. Retraining from the general model improves local predictions even when moderately large annotation sets are available and makes model training faster and more stable. Our results demonstrate that general models for detecting broad classes of organisms using airborne imagery are achievable. These models can reduce the effort, expertise, and computational resources necessary for automating the detection of individual organisms across large scales, helping to transform the scale of data collection in ecology and the questions that can be addressed.

ecology↗

bioRxiv2021

Mapping the temporal and spatial dynamics of the human endometrium in vivo and in vitro

The endometrium, the mucosal lining of the uterus, undergoes dynamic changes throughout the menstrual cycle in response to ovarian hormones. We have generated single-cell and spatial reference maps of the human uterus and 3D endometrial organoid cultures. We dissect the signalling pathways that determine cell fate of the epithelial lineages in the lumenal and glandular microenvironments. Our benchmark of the endometrial organoids highlights common pathways regulating the differentiation of secretory and ciliated lineage in vivo and in vitro. We show in vitro that downregulation of WNT or NOTCH pathways increases the differentiation efficiency along the secretory and ciliated lineages, respectively. These mechanistic insights provide a platform for future development of treatments for a range of common endometrial disorders including endometriosis and carcinoma.

genomics↗

bioRxiv2018

International travelers and genomics uncover a ‘hidden’ Zika outbreak

The ongoing Zika epidemic in the Americas has challenged public health surveillance, response, and control systems. Even as the epidemic appears to be near its end in the Americas, it is unclear whether substantial Zika virus transmission may still be ongoing. This issue is exacerbated by large discrepancies in local case reporting and significant delays in detecting outbreaks due to surveillance gaps. To uncover locations with lingering outbreaks in the Americas, we investigated travel-associated Zika cases diagnosed in the United States and Europe to identify signatures of transmission dynamics that were not captured by local reporting. We found that a large and unreported Zika outbreak occurred in Cuba during 2017, a year after peak transmission in neighboring countries, with cases still appearing in 2018. By sequencing Zika virus from infected travelers, we show that the 2017 outbreak in Cuba was sparked by long-lived lineages of Zika virus introduced from multiple places in the Americas a year prior. Our data suggest that while aggressive mosquito control in Cuba may initially have been effective at mitigating Zika virus transmission, in the absence of vaccines, herd immunity, or strong international coordination, such control measures may need to be maintained to be effective. Our study highlights how Zika virus may still be silently spreading in the Americas and provides a framework for more accurately understanding outbreak dynamics.

epidemiology↗

bioRxiv2018

Modeling the relative role of human mobility, land-use and climate factors on dengue outbreak emergence in Sri Lanka

BackgroundMore than 80,000 dengue cases including 215 deaths were reported nationally in less than seven months between 2016-2017, a fourfold increase in the number of reported cases compared to the average number over 2010-2016. The region of Negombo, located in the Western province, experienced the greatest number of dengue cases in the country and is the focus area of our study, where we aim to capture the spatial-temporal dynamics of dengue transmission.\n\nMethodsWe present a statistical modeling framework to evaluate the spatial-temporal dynamics of the 2016-2017 dengue outbreak in the Negombo region of Sri Lanka as a function of human mobility, land-use, and climate patterns. The analysis was conducted at a 1 km x 1 km spatial resolution and a weekly temporal resolution.\n\nResultsOur results indicate human mobility to be a stronger indicator for local outbreak clusters than land-use or climate variables. The minimum daily temperature was identified as the most influential climate variable on dengue cases in the region; while among the set of land-use patterns considered, urban areas were found to be most prone to dengue outbreak, followed by areas with stagnant water and then coastal areas. The results are shown to be robust across spatial resolutions.\n\nConclusionsOur study highlights the potential value of using travel data to target vector control within a region. In addition to illustrating the relative relationship between various potential risk factors for dengue outbreaks, the results of our study can be used to inform where and when new cases of dengue are likely to occur within a region, and thus help more effectively and innovatively, plan for disease surveillance and vector control.

epidemiology↗

bioRxiv2018

A dynamic neural network model for real-time prediction of the Zika epidemic in the Americas

BackgroundIn 2015 the Zika virus spread from Brazil throughout the Americas, posing an unprecedented challenge to the public health community. During the epidemic, international public health officials lacked reliable predictions of the outbreaks expected geographic scale and prevalence of cases, and were therefore unable to plan and allocate surveillance resources in a timely and effective manner.\n\nMethodsIn this work we present a dynamic neural network model to predict the geographic spread of outbreaks in real-time. The modeling framework is flexible in three main dimensions i) selection of the chosen risk indicator, i.e., case counts or incidence rate, ii) risk classification scheme, which defines the relative size of the high risk group, and iii) prediction forecast window (one up to 12 weeks). The proposed model can be applied dynamically throughout the course of an outbreak to identify the regions expected to be at greatest risk in the future.\n\nResultsThe model is applied to the recent Zika epidemic in the Americas at a weekly temporal resolution and country spatial resolution, using epidemiological data, passenger air travel volumes, vector habitat suitability, socioeconomic and population data for all affected countries and territories in the Americas. The model performance is quantitatively evaluated based on the predictive accuracy of the model. We show that the model can accurately predict the geographic expansion of Zika in the Americas with the overall average accuracy remaining above 85% even for prediction windows of up to 12 weeks.\n\nConclusionsSensitivity analysis illustrated the model performance to be robust across a range of features. Critically, the model performed consistently well at various stages throughout the course of the outbreak, indicating its potential value at the early stages of an epidemic. The predictive capability was superior for shorter forecast windows, and geographically isolated locations that are predominantly connected via air travel. The highly flexible nature of the proposed modeling framework enables policy makers to develop and plan vector control programs and case surveillance strategies which can be tailored to a range of objectives and resource constraints.

epidemiology↗

bioRxiv2018

A decision-support framework to optimize border control for global outbreak mitigation

The introduction and spread of emerging infectious diseases is increasing in both prevalence and scale. Whether naturally, accidentally or maliciously introduced, the substantial uncertainty surrounding the emergence of novel viruses, specifically where they may come from and how they will spread, demands robust and quantifiably validated outbreak control policies that can be implemented in real time. This work presents a novel mathematical modeling framework that integrates both outbreak dynamics and outbreak control into a decision support tool for mitigating infectious disease pandemics that spread through passenger air travel. An ensemble of border control strategies that exploit properties of the air traffic network structure and expected outbreak behavior are proposed. A stochastic metapopulation epidemic model is developed to evaluate and rank the control strategies based on their effectiveness in reducing the spread of outbreaks. Sensitivity analyses are conducted to illustrate the robustness of the proposed control strategies across a range of outbreak scenarios, and a case study is presented for the 2009 H1N1 influenza pandemic. This study highlights the importance of strategically allocating outbreak control resources, and the results can be used to identify the most robust border control policy that can be implemented in the early stages of an outbreak.

epidemiology↗