Biology subjects

Barlow, K. A.

Publications and source records attributed to Barlow, K. A..

3 recordsLinked to original sources

bioRxiv2025

Hypoxia modulates human pulmonary arterial adventitial fibroblast phenotype through HIF-1α activation

Hypoxic pulmonary hypertension (HPH) develops in association with diseases characterized by low oxygen levels leading to pulmonary artery (PA) narrowing and death. Hypoxia has been linked to increased PA collagen and changes in PA adventitial fibroblast (PAAF) metabolism. However, the mechanisms by which hypoxia regulates PAAF function are unknown. Hypoxia-inducible factor-1 (HIF-1) is a subunit of a transcription factor that is degraded in normoxia but stabilized in hypoxia and is involved in extracellular matrix remodeling by fibroblasts. We examined the role of hypoxia and HIF-1 in regulating PAAF function. Human PAAF (HPAAF) were cultured in normoxic and hypoxic conditions. Cells were further treated with HIF1- inhibitor or no drug. Protein expression, mRNA expression, enzyme activity, and metabolite concentration were examined. Male C57BL6/J mice were exposed to 0 or 10 days of hypoxia after which right ventricular hemodynamics and tissue metabolism were assessed. Hypoxia led to an increase in collagen content and decrease in matrix metalloproteinase-2 (MMP2) activity. HIF-1 inhibition limited collagen accumulation and restored MMP2 activity. HPAAF demonstrated elevated lactic acid concentration and decreased ATP in hypoxia. HIF-1 inhibition blunted these effects. Mice exposed to hypoxia developed significant elevation in right ventricle systolic pressures and had decreased ATP levels in pulmonary tissue. This study investigated the mechanisms by which hypoxia drives HPAAF-mediated collagen accumulation and metabolic changes. We identify the key role of HIF-1 in regulating changes. These findings provide important insights into understanding HPAAF-mediated PA remodeling and help identify possible novel therapeutic targets.

bioengineering↗

bioRxiv2019

Computational design of a modular protein sense/response system

Sensing and responding to signals is a fundamental ability of living systems, but despite remarkable progress in computational design of new protein structures, there is no general approach for engineering arbitrary new protein sensors. Here we describe a generalizable computational strategy for designing sensor/actuator proteins by building binding sites de novo into heterodimeric protein-protein interfaces and coupling ligand sensing to modular actuation via split reporters. Using this approach, we designed protein sensors that respond to farnesyl pyrophosphate, a metabolic intermediate in the production of valuable compounds. The sensors are functional in vitro and in cells, and the crystal structure of the engineered binding site matches the design model with atomic accuracy. Our computational design strategy opens broad avenues to link biological outputs to new signals.\n\nOne Sentence SummaryAn engineering strategy to design modular synthetic signaling systems that respond to new small molecule inputs.

biophysics↗

bioRxiv2017

Flex ddG: Rosetta ensemble-based estimation of changes in protein-protein binding affinity upon mutation

Computationally modeling changes in binding free energies upon mutation (interface {Delta}{Delta}G) allows large-scale prediction and perturbation of protein-protein interactions. Additionally, methods that consider and sample relevant conformational plasticity should be able to achieve higher prediction accuracy over methods that do not. To test this hypothesis, we developed a method within the Rosetta macromolecular modeling suite (flex ddG) that samples conformational diversity using \"backrub\" to generate an ensemble of models, then applying torsion minimization, side chain repacking and averaging across this ensemble to estimate interface {Delta}{Delta}G values. We tested our method on a curated benchmark set of 1240 mutants, and found the method outperformed existing methods that sampled conformational space to a lesser degree. We observed considerable improvements with flex ddG over existing methods on the subset of small side chain to large side chain mutations, as well as for multiple simultaneous non-alanine mutations, stabilizing mutations, and mutations in antibody-antigen interfaces. Finally, we applied a generalized additive model (GAM) approach to the Rosetta energy function; the resulting non-linear reweighting model improved agreement with experimentally determined interface DDG values, but also highlights the necessity of future energy function improvements.

biophysics↗