Biology subjects

Scott, A. M.

Publications and source records attributed to Scott, A. M..

16 recordsLinked to original sources

bioRxiv2026

Activity Patterns Structure Food Web Interactions Through Time

Many mobile animals move to locate and consume resources, making energy gain and growth dependent on activity. Yet the role of activity in shaping predator-prey interactions in food webs has not been broadly considered. Here, we synthesize empirical examples to examine how three activity traits (mean, variance, and timing) vary among taxa (fish, mammals, birds) and between predators and prey across temporal scales. We then use predator-prey models to explore how these diverse activity patterns influence stability. Motivated by emerging activity patterns, our theory shows that fluctuating activity rates can drive predator-prey interaction strengths with major consequences for stability. Future research is needed on activity trait patterning, links between activity and attack rates, and the consequences of activity for predator-prey interactions to whole food webs. This is especially critical as human-driven changes to abiotic cues increasingly alter animal activity rates and may rewire food webs.

animal behavior and cognition↗

bioRxiv2025

Transient dynamics and counterintuitive competitive performance in periodic environments

Despite the rapid pace of global change altering temporal environmental patterning, we lack a general understanding of how periodic environments structure ecological communities. In fluctuating environments, nonlinear dynamics associated with temporal trade-offs between competing species can create the potential for both niche differentiation (coexistence) and seemingly unexpected outcomes (exclusion) that deviate from deterministic coexistence theory. Yet, the mechanisms behind these outcomes are not fully understood. Here, we show that periodic fluctuations between times of high and low growth (e.g., seasons), and adaptive temporal trade-offs within and between species, can drive counterintuitive over- and under-performance of competing species. Most notable is the counterintuitive outcome of seasonally-mediated competitive exclusion that would not occur in either season alone, but is rather the direct result of environmental variability itself. We find that seasonal trade-offs in species growth rates, seasonal differences in competition strength, and functional similarity between competing species have the potential to drive nonlinear responses in coexistence to changing seasonality under global change. These biological conditions collectively influence our models transient dynamics, further explaining the mechanisms behind counterintuitive outcomes and highlighting the importance of non-equilibrium theory for global change ecology. Importantly, the seasonal patterns and species trade-offs that magnify these results are biologically realistic, therefore providing important insight into the implications for the maintenance of biodiversity under global change.

ecology↗

bioRxiv2024

Inferring the composition of the blood plasma proteome by a human proteome distribution atlas

The plasma proteome is maintained by the influx and efflux of proteins from surrounding organs and cells. To quantify the extent different organs and cells contribute to the plasma proteome composition, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in human plasma in health and disease. First, we constructed an extensive human proteome atlas from 18 vascularized organs and the most abundant cell types in blood. Second, the atlas was interfaced with previous RNA/protein atlases to objectively define proteome wide protein-organ associations to enable both the inference of origin and the reproducible quantification of organ-specific proteins in plasma. We demonstrate that the resource can determine disease-specific quantitative changes of organ-enriched protein panels in three separate patient cohorts with infection, pancreatitis, and myocardial injury. The strategy can be extended to other diseases to advance our understanding of the processes contributing to plasma proteome dynamics.

systems biology↗

bioRxiv2023

Community-based peptidomic clustering reveals pathogen-specific proteolytic signatures in wounds

Recent advances in mass spectrometry-based peptidomics has catalyzed the identification and quantification of thousands endogenous peptides across diverse biological systems. However, the large theoretical peptidomic landscape and high proportion of missing values poses several challenges for downstream analyses and limits the comparability of clinical samples. Here, we present a generalizable computational workflow that clusters peptides with overlapping sequences to reduce the dimensionality of peptidomic data, improve the definition of protease cut-sites, enhance inter-sample comparability, and enable the implementation of reliable and large-scale data analysis methods akin to those employed in other omics fields. We showcase the algorithm by performing large-scale quantitative analysis of wound fluid peptidomes of highly defined porcine wounds and human clinical non-healing wounds. The analysis revealed signature phenotype-specific peptide regions reflecting pathogen-specific proteolytic activity at the earliest stages of colonization, resulting in novel class of potential peptide cluster-based biomarkers.

bioinformatics↗

bioRxiv2023

Recovery from social isolation requires dopamine in males, but not the autism-related gene nlg3 in either sex

Social isolation causes profound changes in social behaviour in a variety of species including humans, monkeys, mice, bees, and vinegar flies. However, the genetic and molecular mechanisms modulating behavioural responses to both social isolation and social recovery remain to be elucidated. In this study, we quantified the behavioural response of vinegar flies to social isolation through the use of two distinct protocols, one involving flies social space preference and the other assessing flies sociability, defined as their spontaneous tendencies to form groups. We found that social isolation increased social space and reduced sociability. These effects of social isolation, however, were reversible and could be reduced after 3 days of group housing. Flies with a loss of function of neuroligin3 (ortholog of autism-related neuroligin genes) with known increased social space in a socially enriched environment, were still able to recover from social isolation. Using a UAS-TH-RNAi driven in all neurons, we show that dopamine is important for a response to social isolation and recovery in males but not in females. Furthermore, only in males, dopamine levels are reduced after isolation and are not recovered after group housing. Finally, in socially enriched flies with a loss of function of neuroligin3, dopamine levels are reduced in males, but not in females. We propose a model to explain how dopamine and neuroligin3 are involved in the behavioural response to social isolation and its recovery in a dynamic and sex-specific manner.

neuroscience↗

bioRxiv2023

LSD1/KDM1A is essential for neural stem cell differentiation in mice

The proper regulation of neural stem cell differentiation is required for the proper specification of the central nervous system. Here we investigated the function of the H3K4me1/2 demethylase LSD1/KDM1A during neural stem differentiation in mice. Conditional deletion of LSD1 in nestin - positive neural stem cells results in 100% perinatal lethality after birth with severe motor coordination deficits, retarded growth and defects in brain morphology. Despite these severe defects, motor neuron progenitors and the initial motor neuron population are specified normally and motor neurons with normal morphology can be cultured from these mice in vitro. However, motor neurons cultured from mice lacking LSD1 in neural stem cells continue to inappropriately maintain critical neural stem cell proteins. Taken together these results suggest that, as in other mouse stem cell populations, LSD1 is required to deactivate the stem cell program to enable normal neural stem cell differentiation. However, unlike in other mouse stem cell populations, the inappropriate maintenance of the stem cell program during neural stem cell differentiation may compromise neuronal function rather than neuronal specification.

genetics↗

bioRxiv2023

Detection of EGFR and its Activity State in Plasma CD63-EVs from Glioblastoma Patients: Rapid Profiling using an Anion Exchange Membrane Sensor

We present a novel quantitative immunoassay for CD63 EVs (extracellular vesicles) and a constituent surface cargo, EGFR and its activity state, that provides a sensitive, selective, fluorophore-free and rapid alternative to current EV-based diagnostic methods. Our sensing design utilizes a charge-gating strategy, with a hydrophilic anion exchange membrane and a charged silica nanoparticle reporter. With sensitivity and robustness enhancement by the ion-depletion action of the membrane, this hydrophilic design with charged reporters minimizes interference from dispersed proteins and fluorophore degradation, thus enabling direct plasma analysis. With a limit of detection of 30 EVs/L and a high relative sensitivity of 0.01% for targeted proteomic subfractions, our assay enables accurate quantification of the EV marker, CD63, with colocalized EGFR by an operator/sample insensitive universal normalized calibration. Glioblastoma necessitates improved non-invasive diagnostic approaches for early detection and monitoring. Notably, we target both total and "active" EGFR on EVs; with a monoclonal antibody mAb806 that recognizes a normally hidden epitope on overexpressed or mutant variant III EGFR. This approach offers direct glioblastoma detection from untreated human patient samples. Analysis of glioblastoma clinical samples yielded an area-under-the-curve (AUC) value of 0.99 and low p-value of 0.000033, significantly surpassing the performance of existing assays and markers.

cancer biology↗

bioRxiv2023

Explainable machine learning for the identification of proteome states via the data processing kitchen sink

The application of machine learning algorithms to facilitate the understanding of changes in proteome states has emerged as a promising methodology in proteomics research. Unfortunately, these methods can prove difficult to interpret, as it may not be immediately obvious how models reach their predictions. We present the data processing kitchen sink (DPKS) which provides reproducible access to classic statistical methods and advanced explainable machine learning algorithms to build highly accurate and fully interpretable predictive models. In DPKS, explainable machine learning methods are used to calculate the importance of each protein towards the prediction of a model for a particular proteome state. The calculated importance of each protein can enable the identification of proteins that drive phenotypic change in a data-driven manner while classic techniques rely on arbitrary cutoffs that may exclude important features from consideration. DPKS is a free and open source Python package available at https://github.com/InfectionMedicineProteomics/DPKS.

bioinformatics↗

bioRxiv2023

Coexistence in Periodic Environments

Climate change and other anthropogenic impacts are rapidly altering natural environmental periodicities on a variety of time scales. Despite this, a general theoretical foundation describing the role of periodic environmental variation in structuring species interactions and ecological communities is still underdeveloped. Alarmingly, this leaves us unprepared to understand and predict implications for the maintenance of biodiversity under global change. Here, we extend a two-species Lotka-Volterra competition model that incorporates periodic forcing between seasons of high and low production to investigate the effects of changing environmental patterns on species coexistence. Towards this, we define coexistence criteria for periodic environments by approximating isocline solutions akin to classical coexistence outcomes. This analytical approach illustrates that periodic environments (i.e., seasonality) in and of themselves can mediate different competitive outcomes, and these patterns are general across varying time scales. Importantly, species coexistence may be incredibly sensitive to changes in these abiotic periods, suggesting that climate change has the potential to drastically impact the maintenance of biodiversity in the future.

ecology↗

bioRxiv2023

Interpreting biologically informed neural networks for enhanced biomarker discovery and pathway analysis

The advent of novel methods in mass spectrometry-based proteomics allows for the identification of biomarkers and biological pathways which are crucial for the understanding of complex diseases. However, contemporary analytical methods often omit essential information, such as protein abundance and protein co-regulation, and therefore miss crucial relationships in the data. Here, we introduce a generalized workflow that incorporates proteins, their abundances, and associated pathways into a deep learning-based methodology to improve biomarker identification and pathway analysis through the creation and interpretation of biologically informed neural networks (BINNs). We successfully employ BINNs to differentiate between two subphenotypes of septic acute kidney injury (AKI) and COVID-19 from the plasma proteome and utilize feature attribution-methods to introspect the networks to identify which proteins and pathways are important for distinguishing between subphenotypes. Compared to existing methods, BINNs achieved the highest predictive accuracy and revealed that metabolic processes were key to differentiating between septic AKI subphenotypes, while the immune system was more important to the classification of COVID-19 subphenotypes. The methodology behind creating, interpreting, and visualizing BINNs were implemented in a free and open source Python-package: https://github.com/InfectionMedicineProteomics/BINN.

bioinformatics↗

bioRxiv2022

Generalized peakgroup scoring boosts identification rates and accuracy in mass spectrometry based discovery proteomics

The statistical validation of peptide and protein identifications in mass spectrometry proteomics is a critical step in the analytical workflow. This is particularly important in discovery experiments to ensure only confident identifications are accumulated for downstream analysis and biomarker consideration. However, the inherent nature of discovery proteomics experiments leads to scenarios where the search space will inflate substantially due to the increased number of potential proteins that are being queried in each sample. In these cases, issues will begin to arise when the machine learning algorithms that are trained on an experiment specific basis cannot accurately distinguish between correct and incorrect identifications and will struggle to accurately control the false discovery rate. Here, we propose an alternative validation algorithm trained on a curated external data set of 2.8 million extracted peakgroups that leverages advanced machine learning techniques to create a generalizable peakgroup scoring (GPS) method for data independent acquisition (DIA) mass spectrometry. By breaking the reliance on the experimental data at hand and instead training on a curated external dataset, GPS can confidently control the false discovery rate while increasing the number of identifications and providing more accurate quantification in different search space scenarios. To first test the performance of GPS in a standard experimental environment and to provide a benchmark against other methods, a novel spike-in data set with known varying concentrations was analyzed. When compared to existing methods GPS increased the nunmber of identifications by 5-18% and was able to provide more accurate quantification by increasing the number of ratio validated identifications by 24-74%. To evaluate GPS in a larger search space, a novel data set of 141 blood plasma samples from patients developing acute kidney injury after sepsis was searched with a human tissue spectral library (10000+ proteins). Using GPS, we were able to provide a 207-377% increase in the number of candidate differentially abundant proteins compared to the existing methods while maintaining competitive numbers of global identifications. Finally, using an optimized human tissue library and workflow we were able to identify 1205 proteins from the 141 plasma samples and increase the number of candidate differentially abundant proteins by 70.87%. With the addition of machine learning aided differential expression, we were able to identify potential new biomarkers for stratifying subphenotypes of acute kidney injury in sepsis. These findings suggest that by using a generalized model such as GPS in tandem with a massive scale spectral library it is possible to expand the boundaries of discovery experiments in DIA proteomics. GPS is open source and freely available on github at (https://github.com/InfectionMedicineProteomics/gscore).

bioinformatics↗

bioRxiv2022

Confinement of unliganded EGFR by tetraspanin nanodomains gates EGFR ligand binding and signaling

The epidermal growth factor receptor (EGFR) is a central regulator of cell physiology. EGFR is activated by ligand binding, triggering receptor dimerization, activation of kinase activity, and intracellular signaling. EGFR is transiently confined within various plasma membrane nanodomains, yet how this may contribute to regulation of EGFR ligand binding is poorly understood. To resolve how EGFR nanoscale compartmentalization gates ligand binding, we developed single-particle tracking methods to track the mobility of ligand-bound and total EGFR, in combination with modeling of EGFR ligand binding. In comparison to unliganded EGFR, ligand-bound EGFR was more confined and distinctly regulated by clathrin and tetraspanin nanodomains. Ligand binding to unliganded EGFR occurred preferentially in tetraspanin nanodomains, and disruption of tetraspanin nanodomains impaired EGFR ligand binding and altered the conformation of the receptors ectodomain. We thus reveal a novel mechanism by which EGFR confinement within tetraspanin nanodomains regulates receptor signaling at the level of ligand binding.

cell biology↗

bioRxiv2021

CoREST has a conserved role in facilitating SPR-5/LSD1 maternal reprogramming of histone methylation

Maternal reprogramming of histone methylation is critical for reestablishing totipotency in the zygote, but how histone modifying enzymes are regulated during maternal reprogramming is not well characterized. To address this gap, we asked whether maternal reprogramming by the H3K4me1/2 demethylase SPR-5/LSD1/KDM1A, is regulated by the co-repressor protein, SPR-1/CoREST in C. elegans and mice. In C. elegans, SPR-5 functions as part of a reprogramming switch together with the H3K9 methyltransferase MET-2. By examining germline development, fertility and gene expression in double mutants between spr-1 and met-2, we find that spr-1 mutants are partially compromised for spr-5; met-2 reprogramming. In mice, we generated a separation of function Lsd1 M448V point mutation that compromises CoREST binding, but only slightly affects LSD1 demethylase activity. When maternal LSD1 in the oocyte is derived exclusively from this allele, the progeny phenocopy the increased perinatal lethality that we previously observed when LSD1 was reduced maternally. Together, these data are consistent with CoREST having a conserved function in facilitating maternal LSD1 epigenetic reprogramming.

developmental biology↗

bioRxiv2020

Cell-extrinsic effects in T cell acute lymphoblastic pre-leukemia stem cells mediated by EphA3

Our recent study of a novel model of T-ALL pre-leukemic stem cells, the NUP98-HOXD13 (NHD13) mouse, showed that the abnormal self-renewal of these stem cells was dependent on Lyl1 yet, when Lyl1 was deleted, the T-ALL still developed. In the present study, we observe that the thymocytes in these mice also overexpress EphA3, and we characterise the thymocytes in NHD13-EphA3-/- mice. NHD13-EphA3-/- thymocytes retain their abnormal self-renewal activity demonstrated by their capacity to engraft following primary and secondary transplants. Strikingly, NHD13-EphA3-/- thymocytes fail to engraft upon the third serial transplant, whereas the NHD13 thymocytes engraft indefinitely. Seeking to explain this, we find that NHD13 DN2 thymocytes are capable of halting the normal differentiation process of incoming WT progenitor cells, and remarkably, this capacity is severely impaired in the absence of EphA3. Therefore EphA3 is not critical for engraftment, but is essential for enabling the halt in differentiation of neighbouring WT cells, which in turn allows the incumbent progenitors to remain longer in the thymus due to an absence of normal cell competition, a property that in itself has been demonstrated to be oncogenic. We suggest that pre-leukemic self-renewal in this model is a complex interplay of cell intrinsic and extrinsic factors, and that multiple redundant pathways to leukemogenesis are active in this model.

cancer biology↗

bioRxiv2019

DECR1 is an androgen-repressed survival factor that regulates PUFA oxidation to protect prostate tumor cells from ferroptosis

Fatty acid {beta}-oxidation (FAO) is the main bioenergetic pathway in prostate cancer (PCa) and a promising novel therapeutic vulnerability. Here we demonstrate therapeutic efficacy of targeting FAO in clinical prostate tumors cultured ex vivo, and identify DECR1, which encodes the rate-limiting enzyme for oxidation of polyunsaturated fatty acids (PUFAs), as robustly overexpressed in PCa tissues and associated with shorter relapse-free survival. DECR1 is a negatively-regulated androgen receptor (AR) target gene and, therefore, may promote PCa cell survival and resistance to AR targeting therapeutics. DECR1 knockdown in PCa cells selectively inhibited {beta}-oxidation of PUFAs, inhibited proliferation and migration of PCa cells, including treatment resistant lines, and suppressed tumor cell proliferation in vivo. Mechanistically, targeting of DECR1 caused cellular accumulation of linoleic acid, enhanced mitochondrial oxidative stress and lipid peroxidation, and ferroptosis. These findings implicate PUFA oxidation via DECR1 as a previously unexplored facet of FAO that promotes survival of PCa cells.

cancer biology↗

bioRxiv2018

Enabling the hypothesis-driven prioritization of ligand candidates in big databases: Screenlamp and its application to GPCR inhibitor discovery for invasive species control

While the advantage of screening vast databases of molecules to cover greater molecular diversity is often mentioned, in reality, only a few studies have been published demonstrating inhibitor discovery by screening more than a million compounds for features that mimic a known three-dimensional ligand. Two factors contribute: the general difficulty of discovering potent inhibitors, and the lack of free, user-friendly software to incorporate project-specific knowledge and user hypotheses into 3D ligand-based screening. The Screenlamp modular toolkit presented here was developed with these needs in mind. We show Screenlamps ability to screen more than 12 million commercially available molecules and identify potent in vivo inhibitors of a G protein-coupled bile acid receptor within the first year of a discovery project. This pheromone receptor governs sea lamprey reproductive behavior, and to our knowledge, this project is the first to establish the efficacy of computational screening in discovering lead compounds for aquatic invasive species control. Significant enhancement in activity came from selecting compounds based on one of the hypotheses: that matching two distal oxygen groups in the three-dimensional structure of the pheromone is crucial for activity. Six of the 15 most active compounds met these criteria. A second hypothesis - that presence of an alkyl sulfate side chain results in high activity - identified another 6 compounds in the top 10, demonstrating the significant benefits of hypothesis-driven screening.

bioinformatics↗