Biology subjects

Wang, V. M.

Publications and source records attributed to Wang, V. M..

5 recordsLinked to original sources

bioRxiv2026

Control of wildtype zebrafish optomotor response with a photoswitchable drug

For animals to interact effectively with their environment, the brain must integrate sensory information and generate appropriate motor responses. Multiple neuronal circuits contribute to this process, and identifying their roles remains a central focus in neuroscience. The recently developed photoswitchable compound Carbadiazocine controls neuronal firing across species. It modulates larval zebrafish locomotion and alleviates neuropathic pain in rodents in a reversible, light-induced manner. Given its effects on both motor and somatosensory circuits, we investigated the impact of Carbadiazocine on sensorimotor behaviors. We focused on the optomotor response in zebrafish larvae and assessed its potential as a tool for circuit perturbation and behavioral analysis, for the first time combined with photopharmacology. We performed experiments in head-fixed and free-swimming larvae to assess their capacity to detect and follow the direction of optic flow, as well as to characterize swimming speed patterns and individual tail bout properties following administration of the two Carbadiazocine photoisomers. In both paradigms, treatment with the pre-illuminated compound led to a decrease in accuracy in responding to optic flow (correct turning percentage dropping from [~]95 % to [~]80 % in head-fixed experiments and correctness decreasing from [~]65 % to [~]20 % in free-swimming experiments). Speed analysis revealed an increased number and duration of fast movements with a decrease in number and duration of slow movements, even during periods without visual stimulation. Tail bout analysis further showed an increase in 15-30 Hz bout frequencies, corresponding to incomplete, irregular tail movements. All these effects were absent when the dark-relaxed compound was administered. Together, these findings deepen our understanding of sensorimotor transformations and lay the foundations to probe native neuronal circuits underlying behavior in diverse animal species using a wide dynamic range of photostimulation patterns.

animal behavior and cognition↗

bioRxiv2024

Norepinephrine changes behavioral state via astroglial purinergic signaling

Both neurons and glia communicate via diffusible neuromodulatory substances, but the substrates of computation in such neuromodulatory networks are unclear. During behavioral transitions in the larval zebrafish, the neuromodulator norepinephrine drives fast excitation and delayed inhibition of behavior and circuit activity. We find that the inhibitory arm of this feedforward motif is implemented by astroglial purinergic signaling. Neuromodulator imaging, behavioral pharmacology, and perturbations of neurons and astroglia reveal that norepinephrine triggers astroglial release of adenosine triphosphate, extracellular conversion into adenosine, and behavioral suppression through activation of hindbrain neuronal adenosine receptors. This work, along with a companion piece by Lefton and colleagues demonstrating an analogous pathway mediating the effect of norepinephrine on synaptic connectivity in mice, identifies a computational and behavioral role for an evolutionarily conserved astroglial purinergic signaling axis in norepinephrine-mediated behavioral and brain state transitions.

neuroscience↗

bioRxiv2020

Automated quality control and cell identification of droplet-based single-cell data using dropkick

A major challenge for droplet-based single-cell sequencing technologies is distinguishing true cells from uninformative barcodes in datasets with disparate library sizes confounded by high technical noise (i.e. batch-specific ambient RNA). We present dropkick, a fully automated software tool for quality control and filtering of single-cell RNA sequencing (scRNA-seq) data with a focus on excluding ambient barcodes and recovering real cells bordering the quality threshold. By automatically determining dataset-specific training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode. Using simulated and real-world scRNA-seq data, we benchmarked dropkick against a conventional thresholding approach and EmptyDrops, a popular computational method, demonstrating greater recovery of rare cell types and exclusion of empty droplets and noisy, uninformative barcodes. We show for both low and high-background datasets that dropkicks weakly supervised model reliably learns which genes are enriched in ambient barcodes and draws a multidimensional boundary that is more robust to dataset-specific variation than existing filtering approaches. dropkick provides a fast, automated tool for reproducible cell identification from scRNA-seq data that is critical to downstream analysis and compatible with popular single-cell analysis Python packages.

bioinformatics↗

bioRxiv2020

Non-canonical open reading frames encode functional proteins essential for cancer cell survival

A key question in genome research is whether biologically active proteins are restricted to the [~]20,000 canonical, well-annotated genes, or rather extend to the many non-canonical open reading frames (ORFs) predicted by genomic analyses. To address this, we experimentally interrogated 553 ORFs nominated in ribosome profiling datasets. Of these 553 ORFs, 57 (10%) induced a viability defect when the endogenous ORF was knocked out using CRISPR/Cas9 in 8 human cancer cell lines, 257 (46%) showed evidence of protein translation when ectopically expressed in HEK293T cells, and 401 (73%) induced gene expression changes measured by transcriptional profiling following ectopic expression across 4 cell types. CRISPR tiling and start codon mutagenesis indicated that the biological effects of these non-canonical ORFs required their translation as opposed to RNA-mediated effects. We selected one of these ORFs, G029442--renamed GREP1 (Glycine-Rich Extracellular Protein-1)--for further characterization. We found that GREP1 encodes a secreted protein highly expressed in breast cancer, and its knock-out in 263 cancer cell lines showed preferential essentiality in breast cancer derived lines. Analysis of the secretome of GREP1-expressing cells showed increased abundance of the oncogenic cytokine GDF15, and GDF15 supplementation mitigated the growth inhibitory effect of GREP1 knock-out. Taken together, these experiments suggest that the non-canonical ORFeome is surprisingly rich in biologically active proteins and potential cancer therapeutic targets deserving of further study.

cancer biology↗

bioRxiv2019

Non-oncology drugs are a source of previously unappreciated anti-cancer activity

Anti-cancer uses of non-oncology drugs have been found on occasion, but such discoveries have been serendipitous and rare. We sought to create a public resource containing the growth inhibitory activity of 4,518 drugs tested across 578 human cancer cell lines. To accomplish this, we used PRISM, which involves drug treatment of molecularly barcoded cell lines in pools. Relative barcode abundance following treatment thus reflects cell line viability. We found that an unexpectedly large number of non-oncology drugs selectively inhibited subsets of cancer cell lines. Moreover, the killing activity of the majority of these drugs was predictable based on the molecular features of the cell lines. Follow-up of several of these compounds revealed novel mechanisms. For example, compounds that kill by inducing PDE3A-SLFN12 complex formation; vanadium-containing compounds whose killing is dependent on the sulfate transporter SLC26A2; the alcohol dependence drug disulfiram, which kills cells with low expression of metallothioneins; and the anti-inflammatory drug tepoxalin, whose killing is dependent on high expression of the multi-drug resistance gene ABCB1. These results illustrate the potential of the PRISM drug repurposing resource as a starting point for new oncology therapeutic development. The resource is available at https://depmap.org.

cancer biology↗