Biology subjects

Perkins, C.

Publications and source records attributed to Perkins, C..

3 recordsLinked to original sources

bioRxiv2026

RIC-3 Interacts Directly with the 5-HT3A Receptor to Mediate Trafficking Across Subcellular Compartments

The serotonin type 3A (5-HT3A) receptor is a pentameric ligand-gated ion channel (pLGIC) in central and peripheral neurons that conducts sodium and potassium ions upon serotonin binding. 5-HT3 receptors modulate neurotransmission and synaptic plasticity, influencing mood, sleep, appetite, and addiction. Disruptions in serotonin signaling are linked to central nervous system disorders, including schizophrenia, anxiety and depression. Clinically, these receptors are targeted by antagonists to treat chemotherapy-induced nausea and vomiting. The functional surface expression of these channels is regulated by the chaperone protein Resistant to Inhibitors of Cholinesterase 3 (RIC-3) that promotes plasma-membrane expression, maturation, and trafficking of 5-HT3A and nicotinic acetylcholine receptors. Our previous work identified a duplicated RIC-3 binding motif within the 5-HT3A intracellular domain (ICD). However, it was unclear whether this interaction reflected native conditions. Here, we used a recombinant 5-HT3A ICD peptide in peptide-resin pull-down assays to investigate RIC-3 Interactions in plasma membrane (PM) fractions from Xenopus oocytes, endoplasmic reticulum (ER) fractions from SH-SY5Y cells, and mouse brain tissue. Across all tested systems, the 5-HT3A ICD peptide specifically bound RIC-3. Furthermore, RIC-3 knockdown (RIC-3 KD) SH-SY5Y cells showed a marked reduction in peptide binding and decreased surface levels of nAChR7 and 5-HT3A receptors. These results demonstrate RIC-3-5-HT3A ICD interaction in native cellular contexts and support a role for RIC-3 in regulating receptor surface expression and neuronal signaling.

neuroscience↗

bioRxiv2024

Evolutionary Systems Biology Identifies Genetic Trade-offs In Rice Defense Against Above- and Belowground Attackers

Like other plants, wild and domesticated rice species (Oryza nivara, O. rufipogon, and O. sativa) evolve in environments with various biotic and abiotic stresses that fluctuate in intensity through space and time. Microbial pathogens and invertebrate herbivores such as plant-parasitic nematodes and caterpillars show geographical and temporal variation in activity patterns and may respond differently to certain plant defensive mechanisms. As such, plant interactions with multiple community members may result in conflicting selection pressures on genetic polymorphisms. Here, through assays with different above- and belowground herbivores, the fall armyworm (Spodoptera frugiperda) and the southern root-knot nematode (Meloidogyne incognita), respectively, and comparison with rice responses to microbial pathogens, we identify potential genetic trade-offs at the KSL8 and MG1 loci on chromosome 11. KSL8 encodes the first committed step towards biosynthesis of either stemarane- or stemodane-type diterpenoids through the japonica (KSL8-jap) or indica (KSL8-ind) allele. Knocking out KSL8-jap and CPS4, encoding an enzyme that acts upstream in diterpenoid synthesis, in japonica rice cultivars increased resistance to S. frugiperda and decreased resistance to M. incognita. Furthermore, MG1 resides in a haplotype that provided resistance to M. incognita, while alternative haplotypes are involved in mediating resistance to the rice blast fungus Magnaporthe oryzae and other pests and pathogens. Finally, KSL8 and MG1 alleles are located within trans-species haplotypes and may be evolving under long-term balancing selection. Our data are consistent with a hypothesis that polymorphisms at KSL8 and MG1 may be maintained through complex and diffuse community interactions.

systems biology↗

bioRxiv2021

Progressive and predictive markers of disease evolution: platelet transcriptome in chronic myeloproliferative neoplasms

Predicting disease progression remains a particularly challenging endeavor in chronic degenerative disorders and cancer, thus limiting early detection, risk stratification, and preventive interventions. Here, profiling the spectrum of chronic myeloproliferative neoplasms (MPNs) as a model, we identify the blood platelet transcriptome as a proxy for highly sensitive progression biomarkers that also enables prediction of advanced disease via machine learning algorithms. Using RNA sequencing (RNA-seq), we derive disease-relevant gene expression in purified platelets from 120 peripheral blood samples constituting two time-separated cohorts of patients diagnosed with one of three MPN subtypes at sample acquisition - essential thrombocythemia, ET (n=24), polycythemia vera, PV (n=33), and primary or post ET/PV secondary myelofibrosis, MF (n=42), and healthy donors (n=21). The MPN platelet transcriptome reveals an incremental molecular reprogramming that is independent of patient driver mutation status or therapy and discriminates each clinical phenotype. Leveraging this dataset that shows a characteristic progressive expression gradient across MPN, we develop a machine learning model (Lasso-penalized regression) and predict advanced subtype MF at high accuracy and under two conditions of external validation: i) temporal: our two Stanford cohorts, AUC-ROC of 0.96; and ii) geographical: independently published data of an additional n=25 MF and n=46 healthy donors, AUC-ROC of 0.97). Lasso-derived signatures offer a robust core set of < 5 MPN transcriptome markers that are progressive in expression. Mechanistic insights from our data highlight impaired protein homeostasis as a prominent driver of MPN evolution, with persistent integrated stress response. We also identify JAK inhibitor-specific signatures and other interferon, proliferation, and proteostasis-associated markers as putative targets for MPN-directed therapy. Our platelet transcriptome snapshot of chronic MPNs demonstrates a proof of principle for disease risk stratification and progression beyond genetic data alone, with potential utility in other progressive disorders. HighlightsLeveraging two independent and mutually validating MPN patient cohorts, we identify progressive transcriptomic markers that also enable externally validated prediction in MPNs. Our platelet RNA-Seq data identifies impaired protein homeostasis as prominent in MPN progression and offers putative targets of therapy. VISUAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=135 HEIGHT=200 SRC="FIGDIR/small/435190v3_ufig1.gif" ALT="Figure 1"> View larger version (42K): org.highwire.dtl.DTLVardef@72dc16org.highwire.dtl.DTLVardef@cf5096org.highwire.dtl.DTLVardef@b3d460org.highwire.dtl.DTLVardef@3c0ddc_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗