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Biology subjects

Shapiro, G.

Publications and source records attributed to Shapiro, G..

4 recordsLinked to original sources

Cross-species analysis of GNB1 I80T encephalopathy: conserved developmental, epileptic and neuronal transcriptome signatures

GNB1 encephalopathy (GNB1E) is a rare neurodevelopmental disorder caused by mutations in GNB1 gene encoding the G protein subunit G{beta}1. Mechanisms linking these variants to neurological dysfunction remain unclear. We investigated the prevalent p.Ile80Thr (I80T) variant using combined clinical, cellular, and in vivo approaches. Longitudinal evaluation of a GNB1E patient revealed developmental delay, progressive peripheral spasticity, and epilepsy with Spike-Wave Activation in Sleep. Heterozygous knock-in Gnb1I80T/+ mice exhibited disease-relevant phenotypes, including impaired early development, mild adult motor and cognitive deficits and epileptiform cortical spike-and-wave discharges. Transcriptomic analysis identified 323 genes concordantly dysregulated in mouse cortex and cortical human neuronal cultures from patient-derived induced pluripotent cells. This gene set was enriched for ion-channel function, epilepsy-associated genes, and Gs/adenylyl cyclase signaling pathway. Our integrated analysis establishes the first cross-species model for GNB1E, suggests common neurological mechanisms and molecular pathways linked to GNB1E, and provides a framework for mechanistic and therapeutic studies. TeaserConserved human/mouse neurological and transcriptomic signatures in GNB1 encephalopathy.

physiology↗

Nanobubble-Based Ultrasound Localization Microscopy through Interactive Adaptive Processing

This study presents the use of sub-micron nanobubbles (NBs) as contrast agents for ultrasound localization microscopy (ULM), a super-resolution imaging technique that visualizes microvascular structure and flow beyond the acoustic diffraction limit. While ULM has traditionally relied on micron-sized microbubbles (MBs), the reduced dimensions and prolonged circulation times of NBs make them attractive candidates for localization-based imaging. However, their weaker acoustic responses present significant challenges for reliable detection and tracking. To address this challenge, we developed the ULM Master GUI, an interactive framework for optimization of the complete ULM processing pipeline. Using custom ultrasound-compatible wall-less gelatin flow phantoms containing vessel-mimicking channels and bifurcations ranging from 100 to 500 m, we demonstrate that NB-based ULM achieves velocity reconstruction and flow partitioning measurements comparable to conventional MB-based ULM. Across all investigated geometries, NBs faithfully reproduced the underlying flow patterns and hemodynamic behavior despite their substantially reduced acoustic scattering. These findings establish the feasibility of NB-based ULM, expand the range of contrast agents available for localization microscopy, and provide a foundation for future super-resolution ultrasound imaging using nanoscale acoustic contrast agents. The ULM processing GUI is publicly available at https://github.com/grisha1998/ulm-super-resolution-toolbox.

bioengineering↗

Development of a High-throughput Assay to Identify Novel Reactivators of Acetylcholinesterase

Acetylcholinesterase (AChE) is a key enzyme in the nervous system capable of breaking down acetylcholine (ACh). ACh is a critical neurotransmitter that signals between nerve cells and muscles. Organophosphates (OPs), used as insecticides and nerve agents, irreversibly inhibit AChE by forming a covalent bond with the enzymes active site. Since exposure to these nerve agents can quickly lead to death, many researchers have strived to discover new drugs that will reverse the inhibition of AChE by such agents. The goal was to develop a high-throughput miniaturized assay for testing large numbers of compounds for AChE reactivation quickly and reliably. Here we describe our miniaturized assay that utilizes the sarin surrogate nitrophenyl isopropyl methylphosphonate (NIMP) in an Amplex Red coupled assay and its validation with 42 compounds selected from an in silico virtual screen.

biochemistry↗

MatchMiner: An open source computational platform for real-time matching of cancer patients to precision medicine clinical trials using genomic and clinical criteria

BackgroundMolecular profiling of cancers is now routine at many cancer centers, and the number of precision cancer medicine clinical trials, which are informed by profiling, is steadily rising. Additionally, these trials are becoming increasingly complex, often having multiple arms and many genomic eligibility criteria. Currently, it is a challenging for physicians to match patients to relevant clinical trials using the patients genomic profile, which can lead to missed opportunities. Automated matching against uniformly structured and encoded genomic eligibility criteria is essential to keep pace with the complex landscape of precision medicine clinical trials.\n\nResultsTo meet these needs, we built and deployed an automated clinical trial matching platform called MatchMiner at the Dana-Farber Cancer Institute (DFCI). The platform has been integrated with Profile, DFCIs enterprise genomic profiling project, which contains tumor profile data for >20,000 patients, and has been made available to physicians across the Institute. As no current standard exists for encoding clinical trial eligibility criteria, a new language called Clinical Trial Markup Language (CTML) was developed, and over 178 genomically-driven clinical trials were encoded using this language. The platform is open source and freely available for adoption by other institutions.\n\nConclusionMatchMiner is the first open platform developed to enable computational matching of patient-specific genomic profiles to precision cancer medicine clinical trials. Creating MatchMiner required developing clinical trial eligibility standards to support genome-driven matching and developing intuitive interfaces to support practical use-cases. Given the complexity of tumor profiling and the rapidly changing multi-site nature of genome-driven clinical trials, open source software is the most efficient, scalable, and economical option for matching cancer patients to clinical trials.

bioinformatics↗