Biology subjects

Chou, K.

Publications and source records attributed to Chou, K..

3 recordsLinked to original sources

bioRxiv2026

Control of motility and cell shape of Haloferax volcanii is linked by a transcriptional regulator

Archaea rely on motility and morphological plasticity to navigate their environments, yet the transcriptional regulation of these processes remains poorly understood. In Haloferax volcanii, archaellum-dependent motility is transcriptionally regulated, but an EarA-like master regulator is absent. Here, we identify CsmR as a transcriptional regulator that links archaellum biogenesis and cell-shape transitions in H. volcanii. Deletion of csmR abolished detectable motility, whereas overexpression increased motility and promoted a sustained rod-like morphology. Comparative transcriptomics defined a CsmR-associated regulon that includes archaellum and chemotaxis genes as well as rod-shape determinants (e.g., Sph3 and RdfA), and upstream motif enrichment supports a direct role for CsmR in transcriptional control. Furthermore, csmR and cirA, a KaiC-like regulator, share extensive transcriptional overlap, with CirA likely fine-tuning CsmR-mediated regulation through post-translational modification. These findings establish CsmR as a key integrator of motility and cell shape regulation in Haloferax volcanii, suggesting that haloarchaea coordinate these fundamental processes through an unidentified transcriptional network. Moreover, Northern blotting and cell shape observation suggest that transcription factor RosR is involved in the regulation of an sRNA that shares extensive overlap with the cirA gene, possibly fine-tuning the effect of CirA on the regulation of the archaellum cluster and the rod shape determinants sph3 and rdfA. Understanding this interplay provides new insights into archaeal adaptability and may reveal broader regulatory principles in prokaryotic cell biology.

microbiology↗

bioRxiv2023

Terpenes from Cannabis sativa Induce Antinociception in Mouse Chronic Neuropathic Pain via Activation of Spinal Cord Adenosine A2A Receptors

Terpenes are small hydrocarbon compounds that impart aroma and taste to many plants, including Cannabis sativa. A number of studies have shown that terpenes can produce pain relief in various pain states in both humans and animals. However, these studies were methodologically limited and few established mechanisms of action. In our previous work, we showed that the terpenes geraniol, linalool, {beta}-pinene, - humulene, and {beta}-caryophyllene produced cannabimimetic behavioral effects via multiple receptor targets. We thus expanded this work to explore the efficacy and mechanism of these Cannabis terpenes in relieving chronic pain. We first tested for antinociceptive efficacy by injecting terpenes (200 mg/kg, IP) into male and female CD- 1 mice with chemotherapy-induced peripheral neuropathy (CIPN) or lipopolysaccharide-induced inflammatory pain, finding that the terpenes produced roughly equal efficacy to 10 mg/kg morphine or 3.2 mg/kg WIN55,212. We further found that none of the terpenes produced reward as measured by conditioned place preference, while low doses of terpene (100 mg/kg) combined with morphine (3.2 mg/kg) produced enhanced antinociception vs. either alone. We then used the adenosine A2A receptor (A2AR) selective antagonist istradefylline (3.2 mg/kg, IP) and spinal cord-specific CRISPR knockdown of the A2AR to identify this receptor as the mechanism for terpene antinociception in CIPN. In vitro cAMP and binding studies and in silico modeling studies further suggested that the terpenes act as A2AR agonists. Together these studies identify Cannabis terpenes as potential therapeutics for chronic neuropathic pain, and identify a receptor mechanism in the spinal cord for this activity.

neuroscience↗

bioRxiv2019

StormGraph: An automated graph-based algorithm for quantitative clustering analysis of single-molecule localization microscopy data

Clustering of proteins is crucial for many cellular processes and can be imaged at nanoscale resolution using single-molecule localization microscopy (SMLM). Ideally, molecular clustering in regions of interest (ROIs) from SMLM images would be assessed using computational methods that are robust to sample and experimental heterogeneity, account for uncertainties in localization data, can analyze both 2D and 3D data, and have practical computational requirements in terms of time and hardware. While analyzing surface protein clustering on B lymphocytes using SMLM, we encountered limitations with existing cluster analysis methods. This inspired us to develop StormGraph, an algorithm using graph theory and community detection to identify clusters in heterogeneous sets of 2D and 3D SMLM data while accounting for localization uncertainties. StormGraph generates both multi-level and single-level clusterings and can quantify cluster overlap for two-color SMLM data. Importantly, StormGraph automatically determines scale-dependent thresholds from the data using scale-independent input parameters. This makes identical choices of input parameter values suitable for disparate ROIs, eliminating the need to tune parameters for different ROIs in heterogeneous SMLM datasets. We show that StormGraph outperforms existing algorithms at analyzing heterogeneous sets of simulated SMLM ROIs where ground-truth clusters are known. Applying StormGraph to real SMLM data in 2D, we reveal that B-cell antigen receptors (BCRs) reside in a heterogeneous combination of small and large clusters following stimulation, which suggests for the first time that two conflicting models of BCR activation are not mutually exclusive. We also demonstrate application of StormGraph to real two-color and 3D SMLM data.

biophysics↗