Biology subjects

Butts, J. C.

Publications and source records attributed to Butts, J. C..

5 recordsLinked to original sources

bioRxiv2026

Hyaluronic Acid-Alginate Hydrazone Crosslinked Hydrogels Support the Generation and Maturation of V2a Interneurons

Injury to the central nervous system (CNS) causes inflammation, cell death, and glial scar formation that inhibits tissue repair. Injectable hydrogels modified with extracellular matrix (ECM)-derived peptides can provide biochemical cues to promote neural tissue repair and serve as a vehicle to deliver therapeutics across the blood-spinal/blood-brain barrier in a minimally invasive manner. We developed an injectable hydrazone crosslinked hyaluronic acid-alginate (HA-Alg) hydrogel for neural tissue repair. We fabricated hydrogels with a range of polymer concentrations and evaluated their physicochemical properties to identify formulations that mimic the stiffness and viscoelastic properties of the CNS tissue environment. Hyaluronic acid was further modified with ECM-derived, cell-adhesive peptides (RGD and IKVAV) to enhance neuronal adhesion and viability. To evaluate the therapeutic potential of our hydrogel platform, we embedded mouse embryonic stem cell aggregates and differentiated them toward mature V2a interneurons. These interneurons are critical for relaying motor signals and represent a promising therapeutic cell population for treating spinal cord injuries. We demonstrated successful enrichment for V2a interneurons in HA-Alg hydrogels containing ECM-derived peptides. Interestingly, both our newly described HA-Alg and established crosslinked HA-HA hydrogels containing IKVAV peptides demonstrated significantly increased neurite length in interneuron enriched cultures compared to hydrogels without peptides. This study demonstrates that the addition of ECM-derived peptides is essential to support the neuronal adhesion and viability required for functional CNS tissue repair. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=135 SRC="FIGDIR/small/729352v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@666b1aorg.highwire.dtl.DTLVardef@17d4borg.highwire.dtl.DTLVardef@1c8ece2org.highwire.dtl.DTLVardef@37fdd5_HPS_FORMAT_FIGEXP M_FIG C_FIG Statement of SignificanceDevelopment of injectable, peptide-modified hyaluronic acid alginate hydrogels to support neuronal maturation for central nervous system tissue repair.

bioengineering↗

bioRxiv2025

Identifying non-coding variant effects at scale via machine learning models of cis-regulatory reporter assays

The inability to interpret the functional impact of non-coding variants has been a major impediment in the promise of precision medicine. While high-throughput experimental approaches such as Massively Parallel Reporter Assays (MPRAs) have made major progress in identifying causal variants and their underlying molecular mechanisms, these tools cannot exhaustively measure variant effects genome-wide. Here we present MPAC, an ensemble of machine-learning models trained on MPRA data that provides accurate and scalable prediction of the cis-regulatory impact of non-coding variants. Using MPAC we predict allelic effects for 575M single nucleotide variants (SNVs) across diverse applications, including complex trait genetics, clinical and tumor sequencing, evolutionary analyses, and saturation mutagenesis. We find MPAC predictions match the performance of empirical MPRAs in identifying causal complex trait-associated alleles. We demonstrate the utility of MPAC by applying it to ClinVar, identifying non-coding pathogenic variation with higher accuracy than other sequence-to-function models. We also nominate 1,892 candidate non-coding cancer drivers by predicting the functional effects of somatic SNVs in the COSMIC database. Next, we evaluate population-level genetic variation by predicting effects for all 514M non-coding SNVs in gnomAD, quantifying the relationship between regulatory function and evolutionary constraint. Finally, we generate prospective functional maps using in-silico saturation mutagenesis across 18,658 human promoters, observing widespread selection against variants predicted to disrupt promoter activity. Collectively, this study establishes the value of non-coding functional predictions and provides a comprehensive, publicly available resource for variant interpretation.

genomics↗

bioRxiv2024

Diverse ancestral representation improves genetic intolerance metrics

Rapidly expanding genomic databases have enabled the identification of regions in the human genome intolerant to variation and thus likely relevant to human disease. However, despite their unprecedented scale, these datasets remain constrained by limited ancestral diversity. Here, we systematically evaluate how genetic diversity impacts gene- and sub-genic intolerance metrics by analyzing whole-exome sequencing data from 460,551 UK Biobank participants and 125,748 gnomAD participants across diverse ancestral backgrounds. Through comprehensive analysis of randomly sampled datasets with varying ancestral compositions, we demonstrate that genetic diversity, rather than sample size alone, drives the performance of intolerance metrics in identifying functionally critical genes and genic sub-regions. Notably, scores trained on variation observed in African and Admixed American ancestral groups showed superior resolution in detecting neurodevelopmental disease risk genes and haploinsufficient genes compared to scores trained on variation observed in European ancestry cohorts. Most strikingly, the Missense Tolerance Ratio (MTR) trained on 43,000 multi-ancestry exomes demonstrated greater predictive power than when trained on a nearly 10-fold larger dataset of 440,000 non-Finnish European exomes, indicating that European ancestry-based scores are approaching saturation. These findings establish ancestral diversity as the fundamental determinant for advancing intolerance metrics and underscore the urgent scientific and ethical imperative for enhanced population representation in genomic resources to fully realize the potential of precision medicine and drug discovery.

genetics↗

bioRxiv2023

Machine-guided design of synthetic cell type-specific cis-regulatory elements

Cis-regulatory elements (CREs) control gene expression, orchestrating tissue identity, developmental timing, and stimulus responses, which collectively define the thousands of unique cell types in the body. While there is great potential for strategically incorporating CREs in therapeutic or biotechnology applications that require tissue specificity, there is no guarantee that an optimal CRE for an intended purpose has arisen naturally through evolution. Here, we present a platform to engineer and validate synthetic CREs capable of driving gene expression with programmed cell type specificity. We leverage innovations in deep neural network modeling of CRE activity across three cell types, efficient in silico optimization, and massively parallel reporter assays (MPRAs) to design and empirically test thousands of CREs. Through in vitro and in vivo validation, we show that synthetic sequences outperform natural sequences from the human genome in driving cell type-specific expression. Synthetic sequences leverage unique sequence syntax to promote activity in the on-target cell type and simultaneously reduce activity in off-target cells. Together, we provide a generalizable framework to prospectively engineer CREs and demonstrate the required literacy to write regulatory code that is fit-for-purpose in vivo across vertebrates.

synthetic biology↗

bioRxiv2021

Passive clearing and 3D lightsheet imaging of the intact and injured spinal cord in mice

The spinal cord contains a diverse array of sensory and motor circuits that are essential for normal function. Spinal cord injury (SCI) permanently disrupts neural circuits through initial mechanical damage, as well as a cascade of secondary injury events that further expand the spinal cord lesion, resulting in permanent paralysis. Tissue clearing and 3D imaging have recently emerged as promising techniques to improve our understanding of the complex neural circuitry of the spinal cord and the changes that result from damage due to SCI. However, the application of this technology for studying the intact and injured spinal cord remains limited. Here we optimized the passive CLARITY technique (PACT) to obtain gentle and efficient clearing of the murine spinal cord without the need for specialized equipment. We demonstrate that PACT clearing enables 3D imaging of multiple fluorescent labels in the spinal cord to assess molecularly defined neuronal populations, acute inflammation, long-term tissue damage, and cell transplantation. Collectively, these procedures provide a framework for expanding the utility of tissue clearing to enhance the study of spinal cord neural circuits, as well as cellular- and tissue-level changes that occur following SCI.

neuroscience↗