Biology subjects

Parker, J. A.

Publications and source records attributed to Parker, J. A..

8 recordsLinked to original sources

bioRxiv2026

Suboptimal human inference reflects an efficient and flexible information bottleneck

Human inference is often suboptimal in ways that vary across individuals and tasks. We propose that at least some of this variability reflects information-processing limits. To test this idea, we developed a task-general application of the information-bottleneck framework to quantify how much information individuals use (information capacity) and how effectively they use it (information efficiency). We applied this framework to human choice behavior on two distinct inference tasks and found that individual differences in performance were driven largely by variation in information capacity, while information efficiency remained consistently high. This pattern of variable capacity and high efficiency also applied to a subset of participants who appeared inefficient overall, but were efficient when evaluated relative to a simplified (heuristic) encoding of the observations. Regardless of whether participants based their strategies on full or simplified encodings, they tended to adjust their performance to different task demands by changing their information capacity while maintaining efficiency. We interpret these findings in terms of new analytical results linking optimal inference under information-capacity constraints to evidence-based choice noise, suggesting that a key driver of human behavioral variability is rational adaptation to flexible information-processing limits.

neuroscience↗

bioRxiv2026

Repurposing the HMG-CoA Reductase Inhibitor Atorvastatin for SRD5A3-CDG

SRD5A3-CDG is a rare autosomal recessive congenital disorder of glycosylation characterized by multisystemic dysfunction, including neurological, psychomotor, cognitive, and visual impairments. Approximately 60 cases have been reported, with treatment limited to symptomatic management. SRD5A3 encodes a polyprenal reductase enzyme essential for synthesizing dolichol, a lipid carrier of the oligosaccharide precursor in N-glycosylation. To address the lack of effective treatments and disease models suitable for high-throughput screening, we developed the first C. elegans model of SRD5A3-CDG, harboring the homozygous W19X nonsense mutation commonly observed in patients. This model recapitulates disease-relevant phenotypes, including developmental delays, neurological dysfunction, and mevalonate pathway dysregulation. Using this model, we conducted a high-throughput motility-based drug repurposing screen and identified atorvastatin, an FDA-approved HMG-CoA reductase inhibitor, as a repurposing candidate. Atorvastatin rescued disease-relevant phenotypes in the worm model and restored polyprenol-to-dolichol ratios in patient fibroblasts. These findings highlight atorvastatin as a promising drug repurposing candidate for SRD5A3-CDG.

cell biology↗

bioRxiv2025

Characterization of a C9orf72 Knockout Danio rerio model for ALS and cross-species validation of potential therapeutics screened in Caenorhabditis elegans.

Intronic hexanucleotide repeat expansions in the C9orf72 gene represent the most common genetic cause of the neurodegenerative diseases amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. This expansion decreases C9orf72 expression in affected patients, indicating that loss of C9orf72 function (LOF) acts as a pathogenic mechanism. Several models using Danio rerio (zebrafish) for C9orf72 depletion have been developed to explore disease mechanisms and the consequences of C9orf72 LOF. However, inconsistencies exist in reported phenotypes, and many have yet to be validated in stable germline ablation models. To address this, we created a zebrafish C9orf72 knockout model using CRISPR/Cas9. The C9orf72 LOF model demonstrates, in a generally dose-dependent manner, increased larval mortality, persistent growth reduction, and motor deficits. Additionally, homozygous C9orf72 LOF larvae exhibited mild overbranching of spinal motoneurons. To identify potential therapeutic compounds, we performed a screen on an established Caenorhabditis elegans (C. elegans) C9orf72 homologue (alfa-1) LOF model, identifying 12 compounds that enhanced motility, reduced neurodegeneration, and alleviated paralysis phenotypes. Motivated by the shared motor phenotype, 2 of those compounds were tested in our zebrafish C9orf72 LOF model. Pizotifen malate was found to significantly improve motor deficits in C9orf72 LOF zebrafish larvae. We introduce a novel zebrafish C9orf72 knockout model that exhibits phenotypic differences from depletion models, providing a valuable tool for in vivo C9orf72 research and ALS therapeutic validation. Furthermore, we identify pizotifen malate as a promising compound for further preclinical evaluation. Author SummaryAmyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by the progressive loss of motor neurons, with no curative treatments currently available. The most common genetic cause is a hexanucleotide repeat expansion in the C9orf72 gene, which reduces its expression and implicates loss-of-function (LOF) as a disease mechanism. However, the complete functions of C9orf72 and its role in ALS remain unclear. Zebrafish models with indirect partial reduction of C9orf72 expression have shown promise in recapitulating key aspects of ALS, but inconsistencies have been observed across these models. To address these challenges, we developed a stable genetic C9orf72 LOF zebrafish model to study the effects of its LOF, validate previous findings, and test potential ALS therapeutics. Our model displays swimming activity deficits, reduced growth, increased mortality, and mild spinal motor neuron abnormalities. We demonstrated that pizotifen malate significantly improved motor function in both our model and a similar well-established worm model. These results underscore the differences between indirect depletion and direct genetic LOF models while identifying pizotifen malate as a promising candidate for preclinical testing. This zebrafish model serves as a valuable tool for understanding C9orf72-associated ALS mechanisms and advancing therapeutic development.

genetics↗

bioRxiv2024

FAIRSCAPE: An Evolving AI-readiness Framework for Biomedical Research

ObjectiveBiomedical datasets intended for use in AI applications require packaging with rich pre-model metadata to support model development that is explainable, ethical, epistemically grounded and FAIR (Findable, Accessible, Interoperable, Reusable). MethodsWe developed FAIRSCAPE, a digital commons environment, using agile methods, in close alignment with the team developing the AI-readiness criteria and with the Bridge2AI data production teams. Work was initially based on an existing provenance-aware framework for clinical machine learning. We incrementally added RO-Crate data+metadata packaging and exchange methods, client-side packaging support, provenance visualization, and support metadata mapped to the AI-readiness criteria, with automated AI-readiness evaluation. LinkML semantic enrichment and Croissant ML-ecosystem translations were also incorporated. ResultsThe FAIRSCAPE framework generates, packages, evaluates, and manages critical pre-model AI-readiness and explainability information with descriptive metadata and deep provenance graphs for biomedical datasets. It provides ethical, schema, statistical, and semantic characterization of dataset releases, licensing and availability information, and an automated AI-readiness evaluation across all 28 AI-readiness criteria. We applied this framework to successive, large-scale releases of multimodal datasets, progressively increasing dataset AI-readiness to full compliance. ConclusionFAIRSCAPE enables AI-readiness in biomedical datasets using standard metadata components and has been used to establish this pattern across a major, multimodal NIH data generation program. It eliminates early-stage opacity apparent in many biomedical AI applications and provides a basis for establishing end-to-end AI explainability.

bioinformatics↗

bioRxiv2024

A PERTURBATION CELL ATLAS OF HUMAN INDUCED PLURIPOTENT STEM CELLS

Towards comprehensively investigating the genotype-phenotype relationships governing the human pluripotent stem cell state, we generated an expressed genome-scale CRISPRi Perturbation Cell Atlas in KOLF2.1J human induced pluripotent stem cells (hiPSCs) mapping transcriptional and fitness phenotypes associated with 11,739 targeted genes. Using the transcriptional phenotypes, we created a minimum distortion embedding map of the pluripotent state, demonstrating rich recapitulation of protein complexes, such as strong co-clustering of MRPL, BAF, SAGA, and Ragulator family members. Additionally, we uncovered transcriptional regulators that are uncoupled from cell fitness, discovering potential novel pluripotency (JOSD1, RNF7) and metabolic factors (ZBTB41). We validated these findings via phenotypic, protein-interaction, and metabolic tracing assays. Finally, we propose a contrastive human-cell engineering framework (CHEF), a machine learning architecture that learns from perturbation cell atlases to predict perturbation recipes that achieve desired transcriptional states. Taken together, our study presents a comprehensive resource for interrogating the regulatory networks governing pluripotency.

bioengineering↗

bioRxiv2020

TDP-43 stabilizes transcripts encoding stress granule protein G3BP1: potential relevance to ALS/FTD

TDP-43 nuclear depletion and concurrent cytoplasmic accumulation in vulnerable neurons is a hallmark feature of progressive neurodegenerative proteinopathies such as amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). Cellular stress signalling and stress granule dynamics are now recognized to play a role in ALS/FTD pathogenesis. Defective stress granule assembly is associated with increased cellular vulnerability and death. G3BP1 (Ras-GAP SH3-domain-binding protein 1) is a critical stress granule assembly factor. Here, we define that TDP-43 stabilizes G3BP1 transcripts via direct binding of a highly conserved cis regulatory element within the 3UTR. Moreover, we show in vitro and in vivo that nuclear TDP-43 depletion is sufficient to reduce G3BP1 protein levels. Finally, we establish that G3BP1 transcripts are reduced in ALS/FTD patient neurons bearing TDP-43 cytoplasmic inclusions/nuclear depletion. Thus, our data suggest that, in ALS/FTD, there is a compromised stress granule response in disease-affected neurons due to impaired G3BP1 mRNA stability caused by TDP-43 nuclear depletion. These data implicate TDP-43 and G3BP1 loss of function as contributors to disease.

neuroscience↗

bioRxiv2020

Raf promotes dimerization of the Ras G-domain with increased allosteric connections

Ras dimerization is critical for Raf activation, yet Ras alone does not dimerize. Here we show that the Ras binding domain of Raf (Raf-RBD) induces robust Ras dimerization at low surface densities on supported lipid bilayers and, to a lesser extent, in solution as observed by size exclusion chromatography and confirmed by SAXS. Community network analysis based on molecular dynamics (MD) simulations show robust allosteric connections linking the two Raf-RBD D113 residues, located in the Galectin scaffold protein binding site of each Raf-RBD molecule and 85 [A] apart on opposite ends of the dimer complex. Our results suggest that Raf-RBD binding and Ras dimerization are concerted events that lead to a high-affinity signaling complex at the membrane that we propose is an essential unit in the macromolecular assembly of higher order Ras/Raf/Galectin complexes important for signaling through the Ras/Raf/MEK/ERK pathway.

biophysics↗

bioRxiv2019

Modeling pupil responses to rapid sequential events

Pupil size is an easily accessible, noninvasive online indicator of various perceptual and cognitive processes. Pupil measurements have the potential to reveal continuous processing dynamics throughout an experimental trial, including anticipatory responses. However, the relatively sluggish ([~]2 s) response dynamics of pupil dilation make it challenging to connect changes in pupil size to events occurring close together in time. Researchers have used models to link changes in pupil size to specific trial events, but such methods have not been systematically evaluated. Here we developed and evaluated a general linear model (GLM) pipeline that estimates pupillary responses to multiple rapid events within an experimental trial. We evaluated the modeling approach using a sample dataset in which multiple sequential stimuli were presented within 2-s trials. We found: (1) Model fits improved when the pupil impulse response function (puRF) was fit for each observer. PuRFs varied substantially across individuals but were consistent for each individual. (2) Model fits also improved when pupil responses were not assumed to occur simultaneously with their associated trial events, but could have non-zero latencies. For example, pupil responses could anticipate predictable trial events. (3) Parameter recovery confirmed the validity of the fitting procedures, and we quantified the reliability of the parameter estimates for our sample dataset. (4) A cognitive task manipulation modulated pupil response amplitude. We provide our pupil analysis pipeline as open-source software (Pupil Response Estimation Toolbox: PRET) to facilitate the estimation of pupil responses and the evaluation of the estimates in other datasets.

neuroscience↗