High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipeline optimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users’ input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.

Junctional membrane complexes facilitate excitation-contraction coupling in skeletal and cardiac muscle cells by forming subcellular invaginations that maintain close (≤20 nm) proximity of ryanodine receptors (RyRs) on the sarcoplasmic reticulum (SR) with voltage-dependent Ca²⁺ channels in the plasma membrane. In fully differentiated smooth muscle cells, junctional membrane complexes occur as distributed sites of peripheral coupling. We investigated the role of the cytoskeleton in maintaining peripheral coupling and associated Ca²⁺ signaling networks within native smooth muscle cells of mouse and rat cerebral arteries. Using live-cell confocal and superresolution microscopy, we found that the tight interactions between the SR and the plasma membrane in these cells relied on arching microtubule structures present at the periphery of smooth muscle cells and were independent of the actin cytoskeleton. Loss of peripheral coupling associated with microtubule depolymerization altered the spatiotemporal properties of localized Ca²⁺ sparks generated by the release of Ca²⁺ through type 2 RyRs (RyR2s) on the SR and decreased the number of sites of colocalization between RyR2s and large-conductance Ca²⁺-activated K⁺ (BK) channels. The reduced BK channel activity associated with the loss of SR–plasma membrane interactions was accompanied by increased pressure–induced constriction of cerebral resistance arteries. We conclude that microtubule structures maintain peripheral coupling in contractile smooth muscle cells, which is crucial for the regulation of contractility and cerebral vascular tone.

Sung-Cherl Jung, Dawon Kang, and Eun-A Ko. Korean J Physiol Pharmacol 2025;29:139-55. https://doi.org/10.4196/kjpp.24.309

Objective: To undertake a comprehensive analysis of the differential expression of the G protein-coupled receptor ( GPCR) genes in order to construct a GPCR gene signature for human glioma prognosis. Methods: This current study investigated several glioma transcriptomic datasets and identified the GPCR genes potentially associated with glioma severity. Results: A gene signature comprising 13 GPCR genes (nine upregulated and four downregulated genes in high-grade glioma) was developed. The predictive power of the 13-gene signature was tested in two validation cohorts and a strong positive correlation (Spearman’s rank correlation test: ρ = 0.649 for the Validation1 cohort; ρ = 0.693 for the Validation2 cohort) was observed between the glioma grade and 13-gene based severity score in both cohorts. The 13-gene signature was also predictive of glioma prognosis based on Kaplan–Meier survival curve analyses and Cox proportional hazard regression analysis in four cohorts of patients with glioma. Conclusions: Knowledge of GPCR gene expression in glioma may help researchers gain a better understanding of the pathogenesis of high-grade glioma. Further studies are needed to validate the association between these GPCR genes and glioma pathogenesis.

Sung-Cherl Jung, Tong Zhou, and Eun-A Ko. Korean J Physiol Pharmacol 2023;27:85-94. https://doi.org/10.4196/kjpp.2023.27.1.85

Min Seok Woo, Eun-Jin Kim, Dong Kun Lee, Chung Eun Lee, Eun-A Ko and Dawon Kang. Journal of Animal Reproduction and Biotechnology 2024;39:81-7. https://doi.org/10.12750/JARB.39.2.81