Biology subjects

Rosen, A.

Publications and source records attributed to Rosen, A..

11 recordsLinked to original sources

bioRxiv2025

An Axiom SNP genotyping array for potato: development, evaluation and applications

Potato is a versatile food crop and a major component of human nutrition worldwide. Genomic-assisted breeding methods have the potential to increase the gain of selection. We report the development and validation of a high-density Axiom-based SNP array for potato (Solanum tuberosum L.). Whole-genome 10X Genomics based sequencing of 108 diverse clones representing landraces, improved cultivars, and wild relatives identified around 23.8 million sequence variants, from which 929,127 variants, alongside with 18,718 markers from a previously developed Illumina Infinium 21K array (GGP3), were tiled on the array. The array demonstrated high reproducibility, with replicate samples showing an average concordance of 99.88% in genotype calls for PotatoTools specific variants, compared to 99.93% for the Illumina Infinium 21K array variants. A panel of 1,247 diploid and tetraploid clones was genotyped with the developed array. Genotype calling, considering allele dosage, was realized using fitpoly and yielded 852,793 calls. The array informativeness was optimized by applying Euclidean distance, heterozygous strength offset metrics, call rate, minor allele frequency filtering, and diploid-based allele correction, yielding a final set of 206,616 robust and informative markers. The filtered marker set enables precise characterization of genetic variation across diverse germplasm, thereby supporting robust analyses of population structure, genome-wide association studies (GWAS), and genomic prediction in potato. Population structure analysis genotyped clones revealed clear subpopulation differentiation consistent with known ploidy groups. In addition, our discriminant analysis of principal components revealed a weak but structured diversity among clones of different market segments. GWAS analysis of 998 potato clones identified sequence variants significantly associated with polyphenol oxidase (PPO) activity, confirming the platforms efficacy for trait mapping. For the same trait, genomic prediction accuracies of 0.72-0.86 have been observed. The developed potato SNP array provides a robust platform for high-throughput genotyping, supporting genetic diversity studies, association mapping, and genomic-assisted breeding in this important crop.

plant biology↗

bioRxiv2025

LiDAR and hyperspectral-based structured population models show future forest fire frequency may compromise forest resilience

AbstractForest disturbances are accelerating biodiversity loss and altering tree productivity worldwide. Post-disturbance recovery time is critical for identifying vulnerable areas and targeting conservation but varies with environmental conditions. Monitoring recovery at scale requires tracking tree dynamics, yet traditional ground-based approaches are resource-intensive. We present a pipeline to parameterise integral projection models (IPMs) using LiDAR and hyperspectral data to assess post-fire recovery across large, forested areas. Focusing on the fire-adapted Picea mariana, we model passage times to reproductive heights and life expectancy under different fire regimes as indicators of recovery. To do this, we combined hyperspectral-derived species maps and LiDAR-based crown heights to track individual tree survival and growth at the Caribou-Poker Creek Research Watershed (BONA) from 2017-2023. We incorporated fire history, aspect, slope, elevation, and surrounding canopy height into our models and found partial support for their expected effects on survival and growth. Once accounting for topography and competition, we estimated passage times to reproductive maturity (11-22 years). Life expectancy in the absence of fire is shortest on North-facing slopes with recent fire (579 years). Sensitivity analyses highlight fire history and aspect as key modulators of population resilience, with elevation exerting strong influence on life expectancy across all conditions. Our results demonstrate that remotely sensed IPMs can effectively quantify forest recovery at scale, revealing that in some contexts, stands of P. mariana may not recover between fire disturbances. We discuss the implications of these findings for resilience-based forest management and highlight both the challenges and opportunities of using LiDAR and hyperspectral data to build demographic models for forecasting forest dynamics.

ecology↗

bioRxiv2025

Individual heterogeneity drives a plant macroparasites life history

The population dynamics and life histories of macroparasites are fundamental to examine their impacts on hosts and ecosystems. Still, macroparasite population models often ignore parasite individual heterogeneity and are rarely applied to parasitic plants, where demographic strategies are less well understood. Using a 10-year dataset on European mistletoe (Viscum album), we examine how this macroparasites traits influence its performance. V. album survival and reproduction depend on its size and, to a lesser extent, height on the host tree, with a strong growth-reproduction trade-off. We parameterise these relationships in an integral projection model to compare the life history strategy of V. album with two other macroparasites and 490 free-living plants. Contrary to our hypothesis, V. album and other macroparasites do not follow an extreme strategy, suggesting that parasitic plants may not escape the life history constraints experienced by non-parasitic plants. Our results highlight how incorporating parasite heterogeneity can improve macroparasite models.

ecology↗

bioRxiv2025

Modelling forest dynamics using integral projection models (IPMs) and repeat LiDAR

O_LIEstimating the life histories and population dynamics of trees is important for predicting how forests respond to climate change and disturbances. To do so, ecologist often use stage-structured population models, which explicitly account for individual heterogeneity. However, the data needed to parameterise these models is typically constrained by time- and labour-intensive field methods. C_LIO_LIDespite growing access to remote sensing imagery that could potentially satiate these data-hungry population models, these data remain underutilised in population ecology. Here, we demonstrate a pipeline that integrates repeat-LiDAR data with a type of structured demographic model (Integral Projection Model, IPM) to examine forest-wide dynamics in response to environmental drivers. Using Australias Great Western Woodlands as a case study, we model the survival and growth of [~]40,000 eucalypt trees over a decade to estimate their life expectancy and characterise multiple stages of growth (height growth and crown expansion). C_LIO_LIOur results indicate distinct responses of small trees (where vital rates are predicted by height) and large trees (which invested predominantly in expanding crown area) to proxies for competition and soil moisture (local canopy density and topographic wetness index, respectively). Parameterising structured population models using LiDAR data therefore offers a step-change in perspective towards more ecologically meaningful forest dynamics. C_LIO_LITo broaden the application of this pipeline, we highlight three priorities: (1) Application to more complex systems such as mixed species stands and dense, multilayered canopies); (2) Incorporating complete life histories, including reproduction and early life stages; and (3) Using in long-term or comparative studies through improved availability of high-quality, comparable, repeat-LiDAR surveys. By combining remote sensing data with detailed insights from field- based studies, this pipeline represents a scalable framework for guiding forest management and conservation decisions. C_LI

ecology↗

bioRxiv2024

Higher functional resilience of temperate forests at intermediate latitudes of a large latitudinal gradient in South America

Accurately mapping and assessing plant functional composition across space and time is pivotal for understanding environmental change impacts on the biodiversity and functioning of forests. Here, we test the capabilities of a combination of in-situ and remote sensing approaches to deliver accurate estimates of the functional composition of temperate forest ecosystems considering leaf and stem morphological, nutrient, hydraulic, and photosynthetic traits. We identify hydrological stress, soil, and topography as key drivers of plant functional traits. Further, hydrological stress and soil are key determinants of functional dispersion and redundancy in temperate forests distributed across a large latitudinal (30{degrees}S to 53{degrees}S) gradient in Chile. Functional dispersion peaks across Mediterranean forests, woodlands, and scrub, occupying between 30{degrees}S to 35{degrees}S. Conversely, functional redundancy peaks between 42{degrees}S and 53{degrees}S, corresponding to Magellanic subpolar forests. Although functional dispersion and redundancy peak at different latitudes corresponding to distinct forest types; they are both high at latitudes between 35{degrees}S and 42{degrees}S, coinciding with Valdivian temperate rainforests. Our results highlight areas in temperate forests in South America where both tree functional dispersion and redundancy are high, and hence could potentially be more resilient to environmental changes.

ecology↗

bioRxiv2023

Secretory Leukocyte Protease Inhibitor Protects Against Severe Urinary Tract Infection in Mice

Millions suffer from urinary tract infections (UTIs) worldwide every year with women accounting for the majority of cases. Uropathogenic Escherichia coli (UPEC) causes most of these primary infections and leads to 25% becoming recurrent or chronic. To repel invading pathogens, the urinary tract mounts a vigorous innate immune response that includes the secretion of antimicrobial peptides (AMPs), rapid recruitment of phagocytes and exfoliation of superficial umbrella cells. Here, we investigate secretory leukocyte protease inhibitor (SLPI), an AMP with antiprotease, antimicrobial and immunomodulatory functions, known to play protective roles at other mucosal sites, but not well characterized in UTIs. Using a mouse model of UPEC-caused UTI, we show that urine SLPI increases in infected mice and that SLPI is localized to bladder epithelial cells. UPEC infected SLPI-deficient (Slpi-/-) mice suffer from higher urine bacterial burdens, prolonged bladder inflammation, and elevated urine neutrophil elastase (NE) levels compared to wild-type (Slpi+/+) controls. Combined with bulk bladder RNA sequencing, our data indicate that Slpi-/- mice have a dysregulated immune and tissue repair response following UTI. We also measure SLPI in urine samples from a small group of female subjects 18-49 years old and find that SLPI tends to be higher in the presence of a uropathogen, except in patients with history of recent or recurrent UTI (rUTI), suggesting a dysregulation of SLPI expression in these women. Taken together, our findings show SLPI protects against acute UTI in mice and provides preliminary evidence that SLPI is likewise regulated in response to uropathogen exposure in women.

immunology↗

bioRxiv2021

The DNA sensors AIM2 and IFI16 are NET-binding SLE autoantigens

Nucleic acid binding proteins are frequently targeted as autoantigens in systemic lupus erythematosus (SLE) and other interferon (IFN)-linked rheumatic diseases. The AIM-like receptors (ALRs) are IFN-inducible innate sensors that form supramolecular assemblies along double-stranded DNA of various origins. Here, we identify the ALR Absent in melanoma 2 (AIM2) as a novel autoantigen in SLE, with similar properties to the established ALR autoantigen interferon-inducible protein 16 (IFI16). Our SLE cohort revealed a frequent co-occurrence of anti-AIM2, anti-IFI16 and anti-DNA antibodies, and higher clinical measures of disease activity in patients positive for antibodies against these ALRs. We examined neutrophil extracellular traps (NETs) as DNA scaffolds on which these antigens might interact in a pro-immune context, finding that both ALRs bind NETs in vitro and in SLE renal tissues. We demonstrate that ALR binding causes NETs to resist degradation by DNase I, suggesting a mechanism whereby extracellular ALR-NET interactions may promote sustained IFN signaling. Our work suggests that extracellular ALRs bind NETs, leading to DNase resistant nucleoprotein fibers that are targeted as autoantigens in SLE.

immunology↗

bioRxiv2018

A Bayesian Approach to Restricted Latent Class Models for Scientifically-Structured Clustering of Multivariate Binary Outcomes

SO_SCPLOWUMMARYC_SCPLOWThis paper presents a model-based method for clustering multivariate binary observations that incorporates constraints consistent with the scientific context. The approach is motivated by the precision medicine problem of identifying autoimmune disease patient subsets or classes who may require different treatments. We start with a family of restricted latent class models or RLCMs (e.g., Xu and Shang, 2018). However, in the motivating example and many others like it, the unknown number of classes and the definition of classes using binary states are among the targets of inference. We use a Bayesian approach to RCLMs in order to use informative prior assumptions on the number and definitions of latent classes to be consistent with scientific knowledge so that the posterior distribution tends to concentrate on smaller numbers of clusters and sparser binary patterns. The paper derives a posterior sampling algorithm based on Markov chain Monte Carlo with split-merge updates to efficiently explore the space of clustering allocations. Through simulations under the assumed model and realistic deviations from it, we demonstrate greater interpretability of results and superior finite-sample clustering performance for our method compared to common alternatives. The methods are illustrated with an analysis of protein data to detect clusters representing autoantibody classes among scleroderma patients.

bioinformatics↗

bioRxiv2017

Diminished Cortical Thickness is Associated with Impulsive Choice in Adolescence

Adolescence is characterized by both maturation of brain structure and increased risk of negative outcomes from behaviors associated with impulsive decision-making. One important index of impulsive choice is delay discounting (DD), which measures the tendency to prefer smaller rewards available soon over larger rewards delivered after a delay. However, it remains largely unknown how individual differences in structural brain development may be associated with impulsive choice during adolescence. Leveraging a unique large sample of 427 human youths (208 males and 219 females) imaged as part of the Philadelphia Neurodevelopmental Cohort, we examined associations between delay discounting and cortical thickness within structural covariance networks. These structural networks were derived using non-negative matrix factorization, an advanced multivariate technique for dimensionality reduction, and analyzed using generalized additive models with penalized splines to capture both linear and nonlinear developmental effects. We found that impulsive choice, as measured by greater discounting, was most strongly associated with diminished cortical thickness in structural brain networks that encompassed the ventromedial prefrontal cortex, orbitofrontal cortex, temporal pole, and temporoparietal junction. Furthermore, structural brain networks predicted DD above and beyond cognitive performance. Taken together, these results suggest that reduced cortical thickness in regions known to be involved in value-based decision-making is a marker of impulsive choice during the critical period of adolescence.\n\nSIGNIFICANCERisky behaviors during adolescence, such as initiation of substance use or reckless driving, are a major source of morbidity and mortality. In this study, we present evidence from a large sample of youths that diminished cortical thickness in specific structural brain networks is associated with impulsive choice. Notably, the strongest association between impulsive choice and brain structure was seen in regions implicated in value-based decision-making; namely, the ventromedial prefrontal and orbitofrontal cortices. Moving forward, such neuroanatomical markers of impulsivity may aid in the development of personalized interventions targeted to reduce risk of negative outcomes resulting from impulsivity during adolescence.

neuroscience↗

bioRxiv2017

Estimating Autoantibody Signatures To Detect Autoimmune Disease Patient Subsets

Autoimmune diseases are characterized by highly specific immune responses against molecules in self-tissues. Different autoimmune diseases are characterized by distinct immune responses, making autoantibodies useful for diagnosis and prediction. In many diseases, the targets of autoantibodies are incompletely defined. Although the technologies for autoantibody discovery have advanced dramatically over the past decade, each of these techniques generates hundreds of possibilities, which are onerous and expensive to validate. We set out to establish a method to greatly simplify autoantibody discovery, using a pre-filtering step to define subgroups with similar specificities based on migration of radiolabeled, immunoprecipitated proteins on sodium dodecyl sulfate (SDS) gels and autoradiography [Gel Electrophoresis and band detection on Autoradiograms (GEA)]. Human recognition of patterns is not optimal when the patterns are complex or scattered across many samples. Multiple sources of errors - including irrelevant intensity differences and warping of gels - have challenged automation of pattern discovery from autoradiograms.\n\nIn this paper, we address these limitations using a Bayesian hierarchical model with shrinkage priors for pattern alignment and spatial dewarping. The Bayesian model combines information from multiple gel sets and corrects spatial warping for coherent estimation of autoantibody signatures defined by presence or absence of a grid of landmark proteins. We show the pre-processing creates more clearly separated clusters and improves the accuracy of autoantibody subset detection via hierarchical clustering. Finally, we demonstrate the utility of the proposed methods with GEA data from scleroderma patients.

cancer biology↗

bioRxiv2017

Data-driven Assessment of Structural Image Quality

Data quality is increasingly recognized as one of the most important confounding factors in brain imaging research. It is particularly important for studies of brain development, where age is systematically related to in-scanner motion and data quality. Prior work has demonstrated that in-scanner head motion biases estimates of structural neuroimaging measures. However, objective measures of data quality are not available for most structural brain images. Here we sought to identify quantitative measures of data quality for T1-weighted volumes, describe how such measures of quality relate to cortical thickness, and delineate how this in turn may bias inference regarding associations with age in youth. Three highly-trained raters provided manual ratings of 1,840 raw T1-weighted volumes. These images included a training set of 1,065 images from Philadelphia Neurodevelopmental Cohort (PNC), a test set of 533 images from the PNC, as well as an external test set of 242 adults acquired on a different scanner. Manual ratings were compared to automated quality measures provided by the Preprocessed Connectomes Projects Quality Assurance Protocol (QAP), as well as FreeSurfers Euler number, which summarizes the topological complexity of the reconstructed cortical surface. Results revealed that the Euler number was consistently correlated with manual ratings across samples. Furthermore, the Euler number could be used to identify images scored \"unusable\" by human raters with a high degree of accuracy (AUC: 0.98-0.99), and outperformed proxy measures from functional timeseries acquired in the same scanning session. The Euler number also was significantly related to cortical thickness in a regionally heterogeneous pattern that was consistent across datasets and replicated prior results. Finally, data quality both inflated and obscured associations with age during adolescence. Taken together, these results indicate that reliable measures of data quality can be automatically derived from T1-weighted volumes, and that failing to control for data quality can systematically bias the results of studies of brain maturation.

bioinformatics↗