Biology subjects

Weston, M.

Publications and source records attributed to Weston, M..

15 recordsLinked to original sources

bioRxiv2026

Biology Undergraduates Use of AI-Enabled Feedback to Revise Scientific Writing: Recipience and Barriers

Background: Instructors and education researchers are increasingly developing feedback with the assistance of generative AI, which we refer to as AI-enabled feedback, yet we do not know whether established understandings of feedback uptake and effectiveness apply similarly in AI-mediated contexts. This makes the recipience of AI-enabled feedback a central concern to understanding how to best support students in developing their scientific writing skills. This study examined how two teams of undergraduate biology students used AI-enabled feedback to revise their scientific writing, and the extent to which their revisions addressed that feedback. The teams revised their research proposal drafts after receiving AI-enabled feedback developed by their instructor with the aid of a custom AI tool. Results: Our findings show that AI-enabled feedback prompted actionable revision across both teams. Each team addressed approximately half of the feedback comments, indicating comparable levels of uptake despite their differing approaches to revision. We observed that both teams were inconsistent in providing the additional information and reasoning requested in the feedback, and they struggled to address comments requiring self-evaluation. Taken together, we see that students often addressed the more easily actionable parts of AI-enabled feedback while omitting more demanding revisions. Conclusions: These findings provide behavioral evidence of how students used AI-enabled feedback to revise their scientific writing. The observed patterns suggest that the content of AI-enabled feedback supported meaningful revision, while limitations in students agency, strategies, and volition likely shaped their implementation of comments. Although the degree of uptake observed in this study is encouraging, it is unlikely that a single application of generative AI within the feedback process will resolve all the challenges associated with feedback uptake.

scientific communication and education↗

bioRxiv2025

Recurrent enhancer-promoter interactions across samples

Enhancer-promoter interactions (EPIs) are fundamental to gene regulation, and understanding their recurrence across diverse biological samples is key to deciphering chromatin architecture. In this study, we systematically analyzed the recurrence of EPIs across 49 Hi-C and 95 HiChIP datasets. We found that the majority of EPIs identified in a given sample were also present in other samples, regardless of the assay type (Hi-C or HiChIP) or the enhancer annotations used. Interestingly, EPIs that appeared unique to individual samples were typically surrounded by fewer neighboring EPIs, suggesting they may not represent truly sample-specific interactions. Our findings indicate that most human EPIs have already been captured and that cells primarily reuse subsets of these shared EPIs across different cell types and conditions. This study provides new insights into the pervasive and reusable nature of EPIs in the human genome, with important implications for chromatin conformation studies.

bioinformatics↗

bioRxiv2025

Bidirectional Crosstalk Between Sleep and Pulmonary Arterial Hypertension

BackgroundPulmonary Arterial Hypertension (PAH) is a devastating cardiopulmonary disease characterized by pulmonary vascular remodeling due to vascular cells dysfunction. Among other clinical signs, emerging data suggest poor sleep quality in patients with PAH; however, how poor sleep impacts hemodynamic burden, symptom severity, and pulmonary vascular remodeling during PAH progression remains unknown. MethodsWe used two models of sleep disturbances (sleep fragmentation and chronic jet lag) and different mouse models of PAH to determine the effects of poor sleep on PAH. We carried out timepoint quantitative RT-PCR analyses to define the clock gene oscillations in human pulmonary artery smooth muscle cells (PASMCs) isolated from non-PAH and PAH patients. Bulk RNA-sequencing analysis, immunostaining, and proliferation assays were used to explore the mechanisms by which poor sleep impacts PAH. Electroencephalogram and Electromyogram recordings (EEG/EMG) were used to determine whether PAH affects sleep in mice. ResultsPoor sleep exacerbated right ventricular dysfunction, pulmonary vascular remodeling, and PAH. RNA-seq and immunostaining analyses showed that poor sleep induces lung inflammation. Inflammation affected the pulmonary vascular molecular clock to drive PASMC hyperproliferation and increased migration. SMC-specific deletion of Bmal1 protected mice from RV dysfunction, pulmonary vascular remodeling, and PAH. EEG/EMG measurements demonstrated that PAH causes poor sleep quality in mice. Lastly, we showed that improving sleep via melatonin delivery and blunting inflammation with clodronate inhibited pulmonary vascular remodeling and PAH. ConclusionsOur study demonstrates that PAH causes poor sleep which in turn induces inflammation, increases PASMC proliferation, and exacerbates PAH. This suggests that the relationship between PAH and poor sleep is a self-amplifying cycle, and that a combination of hypnotic and anti-inflammatory drugs may give PAH patients better clinical outcomes. Clinical PerspectiveO_ST_ABSWhat is New?C_ST_ABSO_LIPAH causes poor sleep quality which in turn induces lung inflammation, PASMC proliferation, and exacerbated PAH. C_LIO_LIBmal1 rhythmicity is disrupted, and its expression is increased in lungs of patients with PAH. C_LIO_LISMC-specific deletion of Bmal1 protects from PAH. C_LIO_LIImproving sleep quality and blunting inflammation attenuates PAH. C_LI What Are the Clinical Implications?O_LIBmal1 represents a promising disease-modifying agent with potential for clinical translation in the treatment of PAH. C_LIO_LIA combination of hypnotic and anti-inflammatory drugs has therapeutic potential to treat PAH. C_LI

pathology↗

bioRxiv2025

Novel features of miRNA and isomiR-mRNA interactions

Studying the interactions between microRNAs/isomiRs and mRNAs is critical due to their fundamental roles in gene regulation and their involvement in various diseases. Although many isomiRs have been identified, the analysis of their interactions with mRNAs remains in its early stages. In this study, we compiled available human chimeric read data, each comprising a microRNA or isomiR segment paired with an mRNA fragment and identified 1,747 isomiRs and over 5 million microRNA/isomiR-mRNA interactions. We observed that microRNAs with higher adenine and thymine content, and lower cytosine content, tend to have more isomiRs and more mRNA targets. Notably, an average of 18.9% of mRNA targets were bound exclusively by isomiRs, not by their microRNA counterparts. Furthermore, isomiRs sharing the same seed sequences as their reference microRNAs may bind to different targets from their microRNAs, highlighting functional divergence. Interestingly, 20.0% of microRNAs and 8.2% of isomiRs appear to bind mRNAs independently of their seed regions. Among those that do utilize seed regions, 94.5% of microRNAs and 95.7% of isomiRs also engage non-seed regions, suggesting a broader and more complex binding behavior. Our findings offer new insights into microRNA/isomiR-mRNA interactions.

bioinformatics↗

bioRxiv2024

Effects of live yeasts and their metabolic products on bumble bee microcolony development

Bumble bees can benefit from fungi, though the mechanisms underlying these benefits remain unknown and could include nutrition, resource supplementation, or pathogen protection. We tested how adding living yeasts or their metabolic products to Bombus impatiens diets in a factorial experiment affects microcolony performance, including survival, reproduction, and pathogen presence. We additionally assessed effects of yeast treatments on diet (nectar and pollen) chemical composition using untargeted metabolomics. Yeasts impacted microcolony reproduction and survival, but effects depended on source colony. Colonies containing the putative pathogen Aspergillus showed reduced reproduction, but yeast treatments reduced Aspergillus prevalence. Yeast treatments altered chemical composition of nectar and pollen, but most distinguishing compounds were unidentified. Our results suggest limited direct effects of yeasts via nutrition, resource supplementation, or modification of diets, instead suggesting that yeasts may benefit bees through interactions with the pathogens including Aspergillus. Overall, the effects of yeast supplementation are context-dependent, and more research is necessary to better understand the factors important in determining their impacts on bee hosts.

ecology↗

bioRxiv2022

Urban birds' flight responses were unaffected by the COVID-19 shutdowns

The coronavirus disease 2019 (COVID-19) pandemic dramatically altered human activities, and, during shutdowns. Here, we evaluated whether urban birds from five countries changed their tolerance towards humans during the COVID-19 shutdowns. We collected 6369 flight initiation distance estimates for 147 bird species and found that human numbers in parks (at a given hour, day, week or year - before and during the COVID-19 shutdowns) had a little effect on birds tolerance of approaching humans. Apart from the actual human numbers in the area (hourly scale), the effect of human activity at other temporal scales centered around zero. The results were similar across countries, for most species or when we restricted our analyses only to species sampled both before and during the COVID-19 shutdowns. As expected, the level of daily human presence in parks (measured by Google Mobility Reports) correlated negatively with the stringency of governmental restrictions (a weekly proxy for human presence) and was overall lower during COVID-19 shutdowns than during the post-shutdown year (2022). Our results highlight the resilience of birds to changes in human numbers on multiple temporal scales, the complexities of linking animal fear responses to human behavior, and the challenge of quantifying both simultaneously in situ.

animal behavior and cognition↗

bioRxiv2022

Building an Open Representation for Biological Protocols

Laboratory protocols are critical to biological research and development, yet difficult to communicate and reproduce across projects, investigators, and organizations. While many attempts have been made to address this challenge, there is currently no available protocol representation that is unambiguous enough for precise interpretation and automation, yet simultaneously abstract enough to enable reuse and adaptation. The Protocol Activity Markup Language (PAML) is a free and open protocol representation aiming to address this gap, building on a foundation of UML, Autoprotocol, and SBOL RDF. PAML provides a representation both for protocols and for records of their execution and the resulting data, as well as a framework for exporting from PAML for execution by either humans or laboratory automation. PAML is currently implemented in the form of an RDF knowledge representation, specification document, and Python library, can be exported for execution as either a manual "paper protocol" or Autoprotocol, and is being further developed as an open community effort.

synthetic biology↗

bioRxiv2022

Computational Prediction of Synthetic Circuit Function Across Growth Conditions

A challenge in the design and construction of synthetic genetic circuits is that they will operate within biological systems that have noisy and changing parameter regimes that are largely unmeasurable. The outcome is that these circuits do not operate within design specifications or have a narrow operational envelope in which they can function. This behavior is often observed as a lack of reproducibility in function from day to day or lab to lab. Moreover, this narrow range of operating conditions does not promote reproducible circuit function in deployments where environmental conditions for the chassis are changing, as environmental changes can affect the parameter space in which the circuit is operating. Here we describe a computational method for assessing the robustness of circuit function across broad parameter regions. Previously designed circuits are assessed by this computational method and then circuit performance is measured across multiple growth conditions in budding yeast. The computational predictions are correlated with experimental findings, suggesting that the approach has predictive value for assessing the robustness of a circuit design.

synthetic biology↗

bioRxiv2022

Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop

Computational tools addressing various components of design-build-test-learn loops (DBTL) for the construction of synthetic genetic networks exist, but do not generally cover the entire DBTL loop. This manuscript introduces an end-to-end sequence of tools that together form a DBTL loop called DART (Design Assemble Round Trip). DART provides rational selection and refinement of genetic parts to construct and test a circuit. Computational support for experimental process, metadata management, standardized data collection, and reproducible data analysis is provided via the previously published Round Trip (RT) test-learn loop. The primary focus of this work is on the Design Assemble (DA) part of the tool chain, which improves on previous techniques by screening up to thousands of network topologies for robust performance using a novel robustness score derived from dynamical behavior based on circuit topology only. In addition, novel experimental support software is introduced for the assembly of genetic circuits. A complete design-through-analysis sequence is presented using several OR and NOR circuit designs, with and without structural redundancy, that are implemented in budding yeast. The execution of DART tested the predictions of the design tools, specifically with regard to robust and reproducible performance under different experimental conditions. The data analysis depended on a novel application of machine learning techniques to segment bimodal flow cytometry distributions. Evidence is presented that, in some cases, a more complex build may impart more robustness and reproducibility across experimental conditions.

synthetic biology↗

bioRxiv2022

Highly-Automated, High-Throughput Replication of Yeast-based Logic Circuit Design Assessments

We describe an experimental campaign that replicated the performance assessment of logic gates engineered into cells of S. cerevisiae by Gander, et al. Our experimental campaign used a novel high throughput experimentation framework developed under DARPAs Synergistic Discovery and Design (SD2) program: a remote robotic lab at Strateos executed a parameterized experimental protocol. Using this protocol and robotic execution, we generated two orders of magnitude more flow cytometry data than the original experiments. We discuss our results, which largely, but not completely, agree with the original report, and make some remarks about lessons learned.

synthetic biology↗

bioRxiv2021

Excess interictal activity marks seizure prone cortical areas and mice in a genetic epilepsy model

Genetic epilepsies are often caused by variants in widely expressed genes, potentially impacting numerous brain regions and functions. For instance, gain-of-function (GOF) variants in the widely expressed Na+-activated K+ channel gene KCNT1 alter basic neurophysiological and synaptic properties of cortical neurons, leading to developmental epileptic encephalopathy. Yet, aside from causing seizures, little is known about how such variants reshape interictal brain activity, and how this relates to epileptic activity and other disease symptoms. To address this knowledge gap, we monitored neural activity across the dorsal cortex in a mouse model of human KCNT1-related epilepsy using in vivo, awake widefield Ca2+ imaging. We observed 52 spontaneous seizures and 1700 interictal epileptiform discharges (IEDs) in homozygous mutant (Kcnt1m/m) mice, allowing us to map their appearance and spread at high spatial resolution. Outside of seizures and IEDs, we detected ~46,000 events, representing interictal cortical activity, in both Kcnt1m/m and wild-type (WT) mice, and we classified them according to their spatial profiles. Spontaneous seizures and IEDs emerged within a consistent set of susceptible cortical areas, and seizures propagated both contiguously and non-contiguously within these areas in a manner influenced, but not fully determined, by underlying synaptic connectivity. Seizure emergence was predicted by a progressive concentration of total cortical activity within the impending seizure emergence zone. Outside of seizures and IEDs, similar events were detected in WT and Kcnt1m/m mice, suggesting that the spatial structure of interictal activity was largely preserved. Several features of these events, however, were altered in Kcnt1m/m mice. Most event types were briefer, and their intensity more variable, across Kcnt1m/m mice; mice showing more intense activity spent more time in seizure. Furthermore, the rate of events whose spatial profile overlapped with where seizures and IEDs emerged was increased in Kcnt1m/m mice. Taken together, these results demonstrate that an epilepsy-causing K+ channel variant broadly alters physiology. Yet, outside of seizures and IEDs, it acts not to produce novel types of cortical activity, but rather to modulate its amount. The areas where seizures and IEDs emerge show excessively frequent and intense interictal activity and the mean intensity of an individuals cortical activity predicts its seizure burden. These findings provide critical guidance for targeting future research and therapy development.

neuroscience↗

bioRxiv2021

Data Representation in the DARPA SD2 Program

1Modern scientific enterprises are often highly complex and multidisciplinary, particularly in areas like synthetic biology where the subject at hand is itself inherently complex and multidisciplinary. Collaboration across many organizations is necessary to efficiently tackle such problems [6, 15], but remains difficult. The challenge is further amplified by automation that increases the pace at which new information can be produced, and particularly so for matters of fundamental research, where concepts and definitions are inherently fluid and may rapidly change as an investigation evolves [7]. The DARPA program Synergistic Discovery and Design (SD2) aimed to address these challenges by organizing the development of data-driven methods to accelerate discovery and improve design robustness, with one of the key domains under study being synthetic biology. The program was specifically organized such that teams provided complementary types of expertise and resources, and without any team being in a dominant organizational position, such that subject-matter investigations would necessarily require peer-level collaboration across multiple team boundaries. With more than 100 researchers across more than 20 organizations, several of which ran experimental facilities with high-throughput automation, participants were forced to confront challenges around effective data sharing. The default architecture for scientific collaboration is essentially one of anarchy, with ad-hoc bilateral relations between pairs of collaborators or experimental phases (Figure 1(a)). This was by necessity the case during early phases of the SD2 program as well, in which incorporating new tools into pipelines was ad-hoc and time-consuming, and data was generally disconnected from genetic designs and experimental plans. The other typical approach for collaboration is one of "command and control", in which a dominant organization determines the data sharing content and format for all participants (Figure 1(b)). This can be efficient, but tends to be limited in flexibility and extensibility, rendering it unsuitable for research collaboration, as indeed was found when we attempted this approach during the first year of the SD2 program. We addressed these problems with the application of distributed standards to create a "flexible rendezvous" model of collaboration (Figure 1(c)), enabling information flow to track evolving collaborative relationships, improving the sharing and utility of information across the community and supporting accelerated rates of experimentation. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/460644v1_fig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@fc7371org.highwire.dtl.DTLVardef@1ff2733org.highwire.dtl.DTLVardef@66af53org.highwire.dtl.DTLVardef@1809640_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1:C_FLOATNO Architectures for data sharing: bilateral relations (a), command and control (b), and flexible rendezvous (c). C_FIG

synthetic biology↗

bioRxiv2021

Prediction of Whole-Cell Transcriptional Response with Machine Learning

Applications in synthetic and systems biology can benefit from measuring whole-cell response to biochemical perturbations. Execution of experiments to cover all possible combinations of perturbations is infeasible. In this paper, we present the host response model (HRM), a machine learning approach that takes the cell response to single perturbations as the input and predicts the whole cell transcriptional response to the combination of inducers. We find that the HRM is able to qualitatively predict the directionality of dysregulation to a combination of inducers with an accuracy of >90% using data from single inducers. We further find that the use of known prior, known cell regulatory networks doubles the predictive performance of the HRM (an R2 from 0.3 to 0.65). This tool will significantly reduce the number of high-throughput sequencing experiments that need to be run to characterize the transcriptional impact of the combination of perturbations on the host.

bioinformatics↗

bioRxiv2019

mTORC1 and mTORC2 regulate distinct aspects of glutamatergic synaptic transmission.

Although mTOR signaling is known as a broad regulator of cell growth and proliferation, in neurons it regulates synaptic transmission, which is thought to be a major mechanism through which altered mTOR signaling leads to neurological disease. Although previous studies have delineated postsynaptic roles for mTOR, whether it regulates presynaptic function is largely unknown. Moreover, the mTOR kinase operates in two complexes, mTORC1 and mTORC2, suggesting that mTORs role in synaptic transmission may be complex-specific. To better understand each complexs role in synaptic transmission, we genetically inactivated mTORC1 or mTORC2 in cultured mouse glutamatergic hippocampal neurons. Inactivation of either complex reduced neuron growth and evoked EPSCs, however, mTORC1 exerted its effects on eEPSCs at the postsynapse and mTORC2 at the presynapse. Furthermore, inactivation of each complex altered specific modes of synaptic vesicle release, suggesting that mTORC1 and mTORC2 differentially modulate postsynaptic responsiveness and presynaptic release to optimize glutamatergic synaptic transmission.

neuroscience↗

bioRxiv2018

Olanzapine: a full and potent agonist at the hM4D(Gi) DREADD amenable to clinical translation of chemogenetics

Designer receptors exclusively activated by designer drugs (DREADDs) derived from muscarinic receptors are a powerful tool to test causality in basic neuroscience, but are also potentially amenable to clinical translation. A major obstacle is however that the widely-used agonist clozapine-N-oxide undergoes conversion to clozapine, which penetrates the blood-brain barrier but has an unfavorable side effect profile. Perlapine has been reported to activate DREADDs at nanomolar concentrations, but is not approved for use in humans by the Food and Drug Administration or European Medicines Agency, limiting its translational potential. Here we report that the atypical antipsychotic drug olanzapine, widely available in various formulations, is a full and potent agonist of the human muscarinic-receptor M4-based DREADD, facilitating clinical translation of chemogenetics to treat CNS diseases.

neuroscience↗