Biology subjects

Zimmerman, L.

Publications and source records attributed to Zimmerman, L..

4 recordsLinked to original sources

bioRxiv2025

CRISPR screening reveals genetic regulators associated with the evolution of eye degeneration

Determining the genetic factors contributing to trait evolution is critical for understanding how and why traits evolve; however, establishing which genes underlie the evolution of complex traits remains challenging. The freshwater fish Astyanax mexicanus, a species that includes blind, cave-dwelling and eyed, surface-dwelling fish, is a powerful model for evolutionary genetics. While genetic mapping studies in this species previously identified genomic regions associated with cave-derived traits, few causative genes and genetic changes have been identified. Here, we develop methods to identify and rapidly functionally assess candidate genes in A. mexicanus, focusing on a defining trait of cave animals, eye loss. Candidate genes were identified based on whether they fell within an eye-related quantitative trait locus, were differentially expressed between surface and cave eyes, and showed evidence of positive selection in cavefish. Single-nucleus RNA-sequencing revealed that these candidate genes were expressed in multiple cell types during development, including those in different tissues of the eye. CRISPR-Cas9-based mutagenesis demonstrated that disruption of nine of these candidate genes in surface fish resulted in altered eye size. Perturbation of one of these genes, fibulin-7 (fbln7), revealed changes in eye size across multiple stages of eye development. Together, this work identified multiple genes associated with the evolution of eye degeneration in A. mexicanus. Further, this study represents a roadmap for rapid identification and functional assessment of candidate genes implicated in the evolution of traits in cavefish that can be applied to other evolutionary genetic models.

genetics↗

bioRxiv2025

StabilizeIT: An Automated Workflow for Protein Stabilization

The industrial application of enzymes is often hampered by poor stability and low expression yields. While computational tools can predict stabilizing mutations, many are bound by restrictive licenses that hinder their broader adoption. To address this, we developed StabilizeIT, a powerful, open-access webserver for enhancing protein stability and expression. StabilizeIT integrates a pipeline of curated open-source tools such as ProteinMPNN, AlphaFold2 and SaProt with our state-of-the-art model, SolvIT, which accurately predicts heterologous expression titers in E. coli. This unique combination allows for the simultaneous optimization of melting temperature (Tm) and solubility. The pipeline exhibits remarkable speed, generating dozens of high-quality candidates with predicted high titers and increased stability in under an hour, streamlining the path to experimental validation. To demonstrate its efficacy, StabilizeIT was used to engineer multiple enzymes in our novel biosynthetic pathway for Hyaluronic Acid. The resulting variants showed greatly enhanced thermal stability and expression, proving the pipelines real-world utility. StabilizeIT is now available to the community, offering an accessible and validated solution to accelerate the development of robust proteins for diverse applications. The webserver is freely available at https://stabilizeit.enzymit.com

bioinformatics↗

bioRxiv2023

Context-Dependent Design of Induced-fit Enzymes using Deep Learning Generates Well Expressed, Thermally Stable and Active Enzymes

The potential of engineered enzymes in practical applications is often constrained by limitations in their expression levels, thermal stability, and the diversity and magnitude of catalytic activities. De-novo enzyme design, though exciting, is challenged by the complex nature of enzymatic catalysis. An alternative promising approach involves expanding the capabilities of existing natural enzymes to enable functionality across new substrates and operational parameters. To this end we introduce CoSaNN (Conformation Sampling using Neural Network), a novel strategy for enzyme design that utilizes advances in deep learning for structure prediction and sequence optimization. By controlling enzyme conformations, we can expand the chemical space beyond the reach of simple mutagenesis. CoSaNN uses a context-dependent approach that accurately generates novel enzyme designs by considering non-linear relationships in both sequence and structure space. Additionally, we have further developed SolvIT, a graph neural network trained to predict protein solubility in E.Coli, as an additional optimization layer for producing highly expressed enzymes. Through this approach, we have engineered novel enzymes exhibiting superior expression levels, with 54% of our designs expressed in E.Coli, and increased thermal stability with more than 30% of our designs having a higher Tm than the template enzyme. Furthermore, our research underscores the transformative potential of AI in protein design, adeptly capturing high order interactions and preserving allosteric mechanisms in extensively modified enzymes. These advancements pave the way for the creation of diverse, functional, and robust enzymes, thereby opening new avenues for targeted biotechnological applications.

bioinformatics↗

bioRxiv2021

Tibial nerve stimulation increases vaginal blood perfusion and bone mineral density and yield load in ovariectomized rats

Introduction and HypothesisHuman menopause transition and post-menopausal syndrome, driven by reduced ovarian activity and estrogen levels, are associated with an increased risk for symptoms including but not limited to sexual dysfunction, metabolic disease, and osteoporosis. Current treatments are limited in efficacy and may have adverse consequences, so investigation for additional treatment options is necessary. Previous studies have demonstrated that tibial nerve stimulation (TNS) or electro-acupuncture near the tibial nerve are minimally invasive treatments that increase vaginal blood perfusion or serum estrogen in the rat model. We hypothesized that TNS would protect against harmful reproductive and systemic changes associated with menopause. MethodsWe examined the effects of twice weekly TNS (0.2 ms pulse width, 20 Hz, 2x motor threshold) under ketamine-xylazine anesthesia in ovariectomized (OVX) female Sprague Dawley rats on menopause-associated physiological parameters including serum estradiol, body weight, blood glucose, bone health, and vaginal blood flow. Rats were split into three groups (n = 10 per group): 1) intact control (no stimulation), 2) OVX control (no stimulation), and 3) OVX stimulation (treatment group). ResultsTNS did not affect serum estradiol levels, body weight, or blood glucose. TNS transiently increased vaginal blood perfusion during stimulation for up to 5 weeks after OVX and increased areal bone mineral density and yield load of the right femur (side of stimulation) compared to the unstimulated OVX control. ConclusionTNS may ameliorate some symptoms associated with menopause. Additional studies to elucidate the full potential of TNS on menopause-associated symptoms under different experimental conditions are warranted. SummaryPercutaneous tibial nerve stimulation increases vaginal blood perfusion, areal bone mineral density, and femur yield load in an ovariectomized rat model of menopause.

physiology↗