Pan-cancer functional analysis of somatic mutations in G protein-coupled receptors

Multi-omics reveals bufadienolide Q-markers of Bufonis Venenum based on antitumor activity and cardiovascular toxicity in zebrafish

BACKGROUND

Bufonis Venenum (BV) is a traditional animal-based Chinese medicine with therapeutic effects against cancer. However, its clinical use is significantly restricted due to associated cardiovascular risks. BV's value in China's market is typically assessed based on "content priority," focusing on indicator components. However, these components of BV possess both antitumor activity and toxicity, and the correlation between the antitumor activity and toxicity of BV has not yet been elucidated.

PURPOSE

This study employs an integrated multi-omics approach to identify bufadienolide Q-markers and explore the correlation between BV's antitumor activity and toxicity. The aim is to establish a more comprehensive method for BV's quality.

METHODS

Normal zebrafish and HepG2 xenograft zebrafish were chosen as activity and toxicity evaluation models. Ultra-high performance liquid chromatography (UHPLC) coupled with a linear ion trap orbitrap (LTQ-Orbitrap) mass spectrometry was used to quantify eight batches of BV and key "toxic and effective" components were screened out. Transcriptomic and metabolomic analyses were performed to elucidate the regulatory mechanisms underlying the antitumor activity and cardiovascular toxicity of the key components in BV.

RESULTS

Eight key "toxic and effective" compounds were identified: resibufogenin, cinobufagin, arenobufagin, bufotalin, bufalin, gamabufotalin, desacetylcinobufagin, and telocinobufagin. The findings showed that bufalin and cinobufagin interfered with calcium homeostasis through CaV and CaSR, induced cardiotoxicity, and upregulated CASP9 to activate myocardial cell apoptosis. However, desacetylcinobufagin exhibited greater potential in terms of anti-tumor effects. Combining the results of untargeted and targeted metabolomics revealed that desacetylcinobufagin could have a callback effect on differential lipids and correct abnormal energy and amino acid metabolism caused by cancer, similar to cinobufagin and bufalin. Microscale thermophoresis (MST) ligand binding measurements also showed that the binding of desacetylcinobufagin to GPX4 has a more potent ability to induce ferroptosis in tumor cells compared to cinobufagin.

CONCLUSION

An innovative evaluation method based on the zebrafish was developed to investigate the relationship between the toxicity and efficacy of BV. This study identified toxicity and activity Q-markers and explored the mechanism between the two effects of BV. The research data could offer valuable insights into the efficacy of BV. Additionally, desacetylcinobufagin, an active ingredient with low toxicity, was found to enhance the quality of BV.

The lncRNA TRG-AS1 promotes the growth of colorectal cancer cells through the regulation of P2RY10/GNA13

BACKGROUND

The lncRNA TRG-AS1 and its co-expressed gene P2RY10 are important for colorectal cancer (CRC) occurrence and development. The purpose of our research was to explore the roles of TRG-AS1 and P2RY10 in CRC progression.

METHODS

The abundance of TRG-AS1 and P2RY10 in CRC cell lines (HT-29 and LoVo) and normal colon cells FHC was determined and difference between CRC cells and normal cells was compared. LoVo cells were transfected with si-TRG-AS1 and si-P2RY10 constructs. Subsequently, the viability, colony formation, and migration of the transfected cells were analyzed using cell counting kit-8, clonogenicity, and scratch-wound/Transwell® assays, respectively. Cells overexpressing GNA13 were used to further explore the relationship between TRG-AS1 and P2RY10 along with their downstream functions. Finally, nude mice were injected with different transfected cell types to observe tumor formation in vivo.

RESULTS

TRG-AS1 and P2RY10 were significantly upregulated in HT-29 and LoVo compared to FHC cells. TRG-AS1 knockdown and P2RY10 silencing suppressed the viability, colony formation, and migration of LoVo cells. TRG-AS1 knockdown downregulated the expression of P2RY10, GNA12, and GNA13, while P2RY10 silencing downregulated the expression of TRG-AS1, GNA12, and GNA13. Additionally, GNA13 overexpression reversed the cell growth and gene expression changes in LoVo cells induced by TRG-AS1 knockdown or P2RY10 silencing. In vivo experiments revealed that CRC tumor growth was suppressed by TRG-AS1 knockdown and P2RY10 silencing.

CONCLUSIONS

TRG-AS1 knockdown repressed the growth of HT-29 and LoVo by regulating P2RY10 and GNA13 expression.

Ligand-Free Signaling of G-Protein-Coupled Receptors: Physiology, Pharmacology, and Genetics

G-protein-coupled receptors (GPCRs) are ubiquitous sensors and regulators of cellular functions. Each GPCR exists in complex aggregates with multiple resting and active conformations. Designed to detect weak stimuli, GPCRs can also activate spontaneously, resulting in basal ligand-free signaling. Agonists trigger a cascade of events leading to an activated agonist-receptor G-protein complex with high agonist affinity. However, the ensuing signaling process can further remodel the receptor complex to reduce agonist affinity, causing rapid ligand dissociation. The acutely activated ligand-free receptor can continue signaling, as proposed for rhodopsin and μ opioid receptors, resulting in robust receptor activation at low agonist occupancy with enhanced agonist potency. Continued receptor stimulation can further modify the receptor complex, regulating sustained ligand-free signaling-proposed to play a role in opioid dependence. Basal, acutely agonist-triggered, and sustained elevated ligand-free signaling could each have distinct functions, reflecting multi-state conformations of GPCRs. This review addresses basal and stimulus-activated ligand-free signaling, its regulation, genetic factors, and pharmacological implications, focusing on opioid and serotonin receptors, and the growth hormone secretagogue receptor (GHSR). The hypothesis is proposed that ligand-free signaling of 5-HT2A receptors mediate therapeutic effects of psychedelic drugs. Research avenues are suggested to close the gaps in our knowledge of ligand-free GPCR signaling.

3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors

Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive models. State-of-the-art protein descriptors, however, are calculated from the protein sequence and neglect the dynamic nature of proteins. This dynamic nature can be computationally simulated with molecular dynamics (MD). Here, novel 3D dynamic protein descriptors (3DDPDs) were designed to be applied in bioactivity prediction tasks with PCM models. As a test case, publicly available G protein-coupled receptor (GPCR) MD data from GPCRmd was used. GPCRs are membrane-bound proteins, which are activated by hormones and neurotransmitters, and constitute an important target family for drug discovery. GPCRs exist in different conformational states that allow the transmission of diverse signals and that can be modified by ligand interactions, among other factors. To translate the MD-encoded protein dynamics two types of 3DDPDs were considered: one-hot encoded residue-specific (rs) and embedding-like protein-specific (ps) 3DDPDs. The descriptors were developed by calculating distributions of trajectory coordinates and partial charges, applying dimensionality reduction, and subsequently condensing them into vectors per residue or protein, respectively. 3DDPDs were benchmarked on several PCM tasks against state-of-the-art non-dynamic protein descriptors. Our rs- and ps3DDPDs outperformed non-dynamic descriptors in regression tasks using a temporal split and showed comparable performance with a random split and in all classification tasks. Combinations of non-dynamic descriptors with 3DDPDs did not result in increased performance. Finally, the power of 3DDPDs to capture dynamic fluctuations in mutant GPCRs was explored. The results presented here show the potential of including protein dynamic information on machine learning tasks, specifically bioactivity prediction, and open opportunities for applications in drug discovery, including oncology.