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He, G.

Publications and source records attributed to He, G..

At least 19 recordsLinked to original sources

A multimodal precision oncology platform integrating genomics,single-cell transcriptomics, and functional drug screening expandstherapeutic options in pediatric brain tumors

Precision oncology for pediatric brain tumors mainly relies on clinically validated, targetable genomic alterations, which are often sparse, yield few matched therapies, and do not guide chemotherapy selection. We developed a tiered framework in which deep whole-genome sequencing and single-cell RNA sequencing extend targeted-therapy selection, with ex vivo functional drug screening supporting selection of both targeted therapies and chemotherapies. We applied it to 17 children with malignant CNS tumors. The approach expanded recommendations beyond genomics: functional screening provided every patient with at least one high-confidence targeted-therapy or chemotherapy nomination, while single-cell RNA sequencing identified an average of 37 druggable gene targets per patient, producing a fourfold increase in high-confidence targeted-therapy recommendations. In an exploratory analysis, functional classifications were directionally consistent with outcomes in 8 of 10 evaluable cases, although combination treatments and radiation precluded causal attribution. Single-cell profiling also identified subclone-specific vulnerabilities, supporting prospective evaluation of the platform's feasibility and predictive utility.

cancer biology↗

Online synaptic credit assignment in active dendrites

Credit assignment in neural networks is usually formulated as the computation of an abstract error gradient. Whether such a gradient can take a physical, causal form in biophysically detailed multi-compartment neuron models, and enable online, supervised learning, remains unclear. Here we show that the gradient of a detailed neuron's voltage with respect to a synaptic weight is itself a voltage. Differentiating the discrete backward-Euler update solved by standard simulators yields equations of the same form as the original voltage dynamics, driven by gradient currents. Replaying weight-specific gradient currents forward in time reproduces the exact gradient with high fidelity in an L5 pyramidal neuron across diverse input regimes (R2 > 0.998). Pairing the replayed gradient voltage with a local learning signal yields a causal, online learning rule. A single L5 pyramidal neuron with active dendrites learns to reproduce target voltage trajectories containing calcium plateaus and bursts, and recurrent networks of detailed neurons learn to generate target temporal patterns, far outperforming a readout-only control. These results demonstrate that synaptic credit assignment can be implemented online by the voltage dynamics of detailed neurons, potentially suggesting a physical substrate for gradient-based supervised learning in the brain.

neuroscience↗

A Single-cell Spatiotemporal Atlas Encoding Age-dependent Evolution of the Ischemic Penumbra

Aging is the predominant risk factor for stroke and a major driver of poor neurological recovery. Although the ischemic penumbra represents a key therapeutic target, its age-dependent molecular landscape remains poorly understood. Here, we generated a spatiotemporal transcriptomic atlas of the post-stroke penumbra in young (3-4 months) and aged (19-20 months) mice at acute (day 3) and chronic (day 14) stages following transient focal cerebral ischemia. Integrating spatial transcriptomics with dissociated single-cell RNA sequencing revealed a persistent transcriptomic penumbra (TP) that remains detectable for at least 14 days after stroke in both age groups, far beyond the window suggested by conventional imaging modalities. Aging profoundly altered TP organization, resulting in dysregulated energy metabolism, impaired neurovascular repair, diminished pro-reparative cell-cell communication, and extensive remodeling of transcriptional regulatory networks. Using this atlas, we further identified regulatory hub genes whose predicted perturbation could simultaneously counteract multiple age-associated pathological pathways. These data define the molecular architecture of the aging post-stroke penumbra and provide a publicly accessible framework for mechanistic discovery and therapeutic target prioritization aimed at improving recovery after stroke.

neuroscience↗

Comparative genomics reveals shared accessory regions between members of two Fusarium species complexes virulent on garden pea

The contribution of accessory or conditionally dispensable chromosomes to host-specific virulence was first demonstrated in members of the Fusarium solani species complex (FSSC) that are pathogens of garden pea, Pisum sativum L. The phenomenon has since been shown to exist in many fungal plant pathogens, including the closely related F. oxysporum species complex (FOSC). Genome analysis of members of the FSSC and FOSC pathogenic on pea revealed a diverse size range of the accessory genome of these fungi. Despite the [~]65 million years of diverging time, regions on a chromosome known to carry host-specific virulence factors for pea, including the cytochrome P450 pisatin demethylase (PDA) and other pea pathogenicity (PEP) genes, were present in all genomes of these pea pathogens. Genes directly involved in virulence on pea - PEP2, PDA, and PEP5- were the most frequently clustered together. Transcriptome analysis of fungal mycelia treated with the pea phytoalexin pisatin, identified 1,155 differentially expressed genes where many were involved in cellular stress responses. As wilt pathogens that invade host xylem, members of the FOSC encode more putative effectors, when compared to those in the FSSC, and several FOSC effectors were identified to confer race specificity. The conservation of part of the accessory genomes across two evolutionarily diverged species complexes suggests a common origin. Horizontal transfer of accessory chromosomes containing genetic loci involved in pathogenesis for garden pea offers a parsimonious explanation of the polyphyletic origin of host specificity.

genomics↗

Prediction of Lignocellulose Degradation Potential of Wood Decay Fungi using Comparative Genomic Analysis

The Agaricomycotina accounts for roughly a third of all described fungi. They are important due to their wide range of lifestyles and economic and environmental relevance. Certain agaricomycetes act as lignocellulose degraders, playing a significant role in forest ecosystems and bioremediation processes. These wood-decaying fungi have historically been classified as mostly white- or brown-rot based on their ability to degrade lignin, with white-rot fungi possessing a collection of lignocellulose-degrading enzymes, which are reduced or absent in brown-rot fungi. Here, we sequenced and annotated the genome of the agaricomycete Crepidotus cesatii CBS 511.95 and explored its genome and predicted enzymatic content in a comparative context. The 36.04 Mbp genome is in 235 scaffolds, with 3.34% repeat content and 12,891 predicted genes. We found that the PFAM distributions of identified orthogroups suggested that C. cesatii shows patterns more similar to white-rot fungi compared to brown-rot fungi. Additionally, C. cesatii contained multiple copies of CAZymes CBM1 and AA9 involved in hydrolysis of lignocellulose, similar to white-rot fungi. On the other hand, according to the Conserved Unique Peptide Patterns (CUPP) data for AA2 peroxidases, the key enzymes in lignin degradation, C. cesatii is more similar to brown-rot fungi. Based on our analyses we predict that C. cesatii is another representation of the continuum of wood decaying modes between white and brown rot fungi combining genetic features of both types of fungi.

genetics↗

A unified law for inhibitory control in active dendrites

Neuronal computation depends on the balance between excitation and inhibition, yet how this balance is implemented across the dendritic tree remains unclear. Classical views predict that inhibition should be most effective near the soma or along the path from excitation to output, but many interneuron subtypes preferentially target remote dendritic compartments. This apparent paradox is sharpened by active dendrites, where local NMDA spikes, calcium plateaus and backpropagating action potentials can make distal branches powerful contributors to somatic firing. Here we develop an analytical framework that extracts general principles of inhibition from biophysically detailed multi-compartment simulations. By reformulating the implicit voltage update of detailed neuron models as a matrix recursion, we derive exact voltage sensitivities to inhibitory synaptic perturbations. This leads to a unified {Phi}-a law: the somatic impact of inhibition factorizes into a global dendritic susceptibility term and a local synaptic perturbation term. Using this law to map inhibitory leverage and identify optimal inhibitory interventions, we show that active dendritic excitation can shift inhibitory hot zones from perisomatic regions toward distal or intermediate compartments. Across neocortical, hippocampal and striatal neuron models, the same response law explains convergent inhibitory strategies despite distinct cellular mechanisms. Our framework turns detailed numerical simulation into analytical theory, providing a general principle for how diverse dendritic inhibition controls active neurons.

neuroscience↗

NG2-targeting macrophages inhibit 3D invasion of patient-derived glioblastoma spheroids

Engineering macrophages with chimeric antigen receptors is emerging as a promising cancer therapeutic. Chimeric antigen receptor-expressing macrophages (CAR-Ms) engineered to recognize tumor-specific antigens have been shown to inhibit tumor growth and activate adaptive immune responses, leading to robust tumor control in animal studies. Based on this work, clinical trials have been initiated. While the trials have shown promise, challenges remain. The dynamic interactions between CAR-Ms and cancer cells and the exact mechanisms driving anti-tumor effects remain poorly defined. Defining the dynamic interactions between CAR-Ms and cancer cells will provide critical insights for optimizing future CAR-M design and improving therapeutic efficacy. We sought to directly visualize CAR-M interactions with glioblastoma cells at high-resolution and in real-time using CAR-Ms engineered to recognize Neural-Glial Antigen 2 (NG2), an antigen expressed on glioblastoma cells. Using patient-derived glioblastoma cells, we formed glioblastoma spheroids and embedded them in a 3D matrix together with CAR-Ms. Using time-lapse microscopy, as expected, we found that NG2-targeting CAR-Ms engulfed glioblastoma cells. However, excitingly, we found that NG2-targeting CAR-Ms blocked >85% of glioblastoma cell invasion in 3D. This inhibition of glioblastoma invasion was not due to a significant change in CAR-M polarization states. Together, these data suggest that NG2-targeting CAR-Ms both engulf glioblastoma cells and block glioblastoma invasive behavior.

cancer biology↗

MetaSAG: A Tool for Multi-level Exploration and Taxonomic Analysis of Microbial Single-Amplified Genomes

Microbial single-amplified genome (SAG) sequencing technologies have elevated microbial research resolution to the single-cell level. However, neither upstream data processing nor downstream analysis has been fully developed, greatly limiting the research in strain level. Herein, we developed MetaSAG (Multi-level Exploration and Taxonomic Analysis of microbial Single-Amplified Genomes), which enables accurate and rapid taxonomic classification of microbial SAGs. MetaSAG outperforms existing method in species classification certainty, computational efficiency, and sensitivity of low abundance species identification. In addition, MetaSAG enables species-level functional analysis, as well as strain-level evolutionary analysis. With the help of MetaSAG, we discovered the parasitic relationship between phages and bacteria, identifying multiple susceptible bacteria and a broad spectrum of phages. Furthermore, we developed MetaK-Lytic (k-mer-based meta-learning framework to predict phage lytic ability) to achieve accurate prediction of phage lytic activity based on 31-mer short sequences, which is well adapted to the characteristics of incomplete SAG sequences. Overall, we offer a comprehensive integrated tool that can parse microbial SAG data from raw data to the strain level to decipher the functional ecology of microbial dark matter, with broad implications for microbial ecology and phage therapy (https://github.com/liangcheng-hrbmu/MetaSAG). Significance StatementThis work provides a comprehensive framework for high-resolution SAG data analysis. The developed pipeline improves taxonomic annotation sensitivity and speed. Strain-level tracking enables dynamic evolutionary and functional insights, while single-cell bacterial-virus network reconstruction reveals precise interaction patterns. The novel annotation-free short sequence-based MetaK-Lytic facilitates functional prediction of uncharacterized phage sequences. Integrated into the MetaSAG platform, these tools deliver a streamlined, multi-level solution for interpreting SAG data, advancing studies in microbial ecology, evolution, and virology.

microbiology↗

Structural Dynamics of the Dopamine D2 Receptor with a Non-Basic Ligand

Progress in understanding protein-ligand interactions is revolutionizing drug design, especially for G protein-coupled receptors (GPCRs), which are targets for 35% of marketed drugs.1 The dopamine D2 receptor (D2R) represents a key drug target in schizophrenia and Parkinsons disease.2 While structural studies have clarified its interactions with classical ligands, the behavior of atypical, non-basic ligands like D2AAK2 remains unclear. Notably, D2AAK2 shows strong selectivity for D2R over the closely related D3R, despite identical binding pocket composition. Here, we present a cryo-EM structure of D2AAK2 bound to D2R, showing that aspartate 3.32 serves as the main anchoring point, even though the compound lacks a basic nitrogen atom. Using enhanced sampling molecular dynamics simulations and experimental approaches, we uncover a complex binding energy landscape. Simulations suggest that D2AAK2 receptor subtype selectivity between identical binding sites arises from different energy barriers for their conformational changes. Non-basic ligands offer advantages such as better brain penetration and improved pharmacokinetics.3,4 This study provides the first structural insights into a non-basic ligand targeting D2R, paving the way for developing more effective, selective drugs.

pharmacology and toxicology↗

Kinase KEY1 controls pyrenoid condensate size throughout the cell cycle by disrupting phase separation interactions

Biomolecular condensates spatially organize cellular functions, but the regulation of their size, number, dissolution, and re-condensation is poorly understood. The pyrenoid, an algal biomolecular condensate that mediates one-third of global CO2 fixation, typically exists as one large condensate per chloroplast, but during cell division it transiently dissolves and reconfigures into multiple smaller condensates. Here, we identify a kinase, KEY1, in the model alga Chlamydomonas reinhardtii that regulates pyrenoid condensate size and number dynamics throughout the cell cycle and is necessary for normal pyrenoid function and growth. Unlike wild type, key1 mutant cells have multiple smaller condensates throughout the cell cycle that fail to dissolve during cell division. We show that KEY1 localizes to the condensates and promotes their dissolution by disrupting interactions between their core constituents, the CO2-fixing enzyme Rubisco and its linker protein EPYC1, through EPYC1 phosphorylation. We develop a biophysical model that recapitulates KEY1-mediated condensate size and number regulation and suggests a mechanism for controlling condensate position. These data provide a foundation for the mechanistic understanding of the regulation of size, number, position, and dissolution in pyrenoids and other biomolecular condensates.

cell biology↗

Interaction of Carbamoyl-Phosphate Synthase 1 with Agmatinase in the Liver of Torpid Bats

Mammalian torpor imposes unique metabolic constraints, yet the mechanisms of nitrogen metabolism during this state remain unclear. In this study, we show that the urea cycle is selectively regulated rather than broadly suppressed in torpid bats. A significantly increased abundance of carbamoyl-phosphate synthase 1 (CPS1) maintained its functional capacity during torpor and arousal in Myotis ricketti. Proteomic analyses and confocal microscopy identified a specific association and co-localization between CPS1 and agmatinase (AGMAT), an ATP-independent enzyme involved in nitrogen metabolism. Co-localization of CPS1 and AGMAT was also observed in torpid Rhinolophus ferrumequinum, a phylogenetically distant species. Fluorescence resonance energy transfer (FRET) further supported an indirect CPS1-AGMAT interaction. Most urea cycle enzymes exhibited stable or only moderately reduced expression during bat torpor, and metabolic profiling demonstrated sustained nitrogen flux. Together, these findings reveal a conserved adaptive mechanism that maintains urea cycle function, potentially enhancing osmotic stability and energy efficiency during prolonged fasting and water scarcity.

zoology↗

In Vivo Non-Invasive Observation of Dynamic Changes in Hair Shaft, Melanin, and Collagen During the C57BL/6 Follicle Cycle

This study systematically monitored hair shaft, melanin, and collagen dynamics in vivo using non-invasive methods, enabling continuous observation over multiple hair follicle cycles. The hair shafts of C57BL/6 mice were observed in vivo using dermoscopy, photography, and electron microscopy. Second harmonic generation (SHG) and photography were employed for in vivo observation and quantification of melanin dynamics throughout the hair follicle cycle--specifically during the anagen phase, catagen phase, and telogen phase stages. Accuracy was validated via hematoxylin and eosin (H&E) staining. Additionally, SHG was used for in vivo observation and quantification of collagen changes across the same hair cycle phases, with validation performed using a liquid crystal polarizing imaging system and Sirius Red staining. This project provides non-invasive and in vivo methods for evaluating hair shafts, melanin, and collagen fibers across different growth cycles, thereby supporting non-invasive research in the development of active agents for hair loss treatment.

animal behavior and cognition↗

On the analysis of genetic association with long-read sequencing data

Long-read sequencing (LRS) technologies have enhanced the ability to resolve complex genomic architecture and determine the phase relationships of genetic variants over long distances. Although genome-wide association studies (GWAS) identify individual variants associated with complex traits, they do not typically account for whether multiple associated signals at a locus may act in cis or trans, or whether they reflect allelic heterogeneity. As a result, effects that arise specifically from phase relationships may remain hidden in analyses using short-read and microarray data. While the advent of LRS has enabled accurate measurement of phase in population cohorts, statistical methods that leverage phase in genetic association analysis remain underdeveloped. Here, we introduce the Regression on Phase (RoP) method, which directly models cis and trans phase effects between variants under a regression framework. In simulations, RoP outperforms genotype interaction tests that detect phase effects indirectly, and distinguishes in-cis from in-trans phase effects. We implemented RoP at two cystic fibrosis (CF) modifier loci discovered by GWAS. At the chromosome 7q35 trypsinogen locus, RoP confirmed that two variants contributed independently (allelic heterogeneity). At the SLC6A14 locus on chromosome X, phase analysis uncovered a coordinated regulatory mechanism in which a promoter variant modulates lung phenotypes in individuals with CF when acting in cis with a lung-specific enhancer (E2765449/enhD). This coordinated regulation was confirmed in functional studies. These findings highlight the potential of leveraging phase information from LRS in genetic association studies. Analyzing phase effects with RoP can provide deeper insights into the complex genetic architectures underlying disease phenotypes, ultimately guiding more informed functional investigations and potentially revealing new therapeutic targets. Author summaryTraditional genetic association studies typically link individual genetic variants to diseases but often neglect how variants may jointly affect outcomes based on their arrangement across maternal and paternal chromosomes, known as phase relationships. Understanding phase effects is essential for uncovering the mechanisms underlying complex diseases. Recent advances in long-read sequencing technology allow precise measurement of phase relationships over extensive chromosome regions; however, statistical methods for analyzing these effects remain limited. We developed a novel statistical approach called Regression on Phase (RoP) to directly assess these complex genetic interactions. Our simulation studies demonstrated that RoP effectively identifies effects dependent on specific phase arrangements. Applying RoP to genetic variants contributing to cystic fibrosis (CF) revealed phase-dependent mechanisms affecting CF-related lung disease, which were missed by traditional methods. Analyzing phase effects with RoP can advance our understanding of disease mechanisms, guide future functional studies, and ultimately support the development of personalized medicine.

genetics↗

Systematic Evaluation of Somatic Contamination in Germline Genomes

Large-scale genomic initiatives like the UK Biobank (UKB) have revolutionized our understanding of human disease. These studies typically assume that blood-derived DNA faithfully reflects an individuals germline genome. However, this assumption is challenged by somatic mutations arising from processes like clonal hematopoiesis. Although standard bioinformatics pipelines employ variant allele frequency (VAF)-based filtering to mitigate such contamination, the efficacy of these approaches requires systematic evaluation. By systematically analyzing whole-exome sequencing (WES) and whole-genome sequencing (WGS) data from large cohorts, including the UK Biobank, The Cancer Genome Atlas (TCGA), and the 1000 Genomes Project, we revealed critical limitations in current filtering methodologies. We found that the mutational spectrum of rare "germline" variants is highly similar to that of somatic mutations. Furthermore, we uncovered these variants show significant associations with phenotypes such as age, sex, and smoking status, established drivers of somatic mutagenesis. This persistent somatic contamination introduces substantial confounder effects, potentially generating spurious associations and reverse causality in genetic studies. Our work underscores the urgent reconsideration of two fundamental aspects of genomic research: (1) refinement of variant filtering strategies to better distinguish true germline variants from somatic contaminants, and (2) incorporation of somatic mutagenesis factors as essential covariates in study design. Our findings provide a basic framework for improving the accuracy and interpretability of large-scale genomic studies.

genomics↗

Dynamic Resting-State Network Markers of Disruptive Behavior Problems in Youth

BackgroundChildhood disruptive behavior problems are linked to aberrant integrity within large-scale cognitive control networks. However, it is unclear if transitory or dynamic variation in the functional brain architecture is a marker of disruptive behavior problems. The current study tested whether functional connectivity across dynamic networks is distinctly associated with the transdiagnostic symptom domain of disruptive behavior problems in children. MethodsParticipants were aged 9-10 years from the Adolescent Brain Cognitive Development (ABCD) Study, who completed resting-state fMRI (N=877). We employed a dynamic connectivity approach leveraging a hidden semi-Markov model (HSMM) to identify transient properties of brain networks and states. Models estimated the time spent in each state (occupancy time) and the number of consecutive timepoints in a state (dwell time) for each participant. Linear regression models were utilized to identify distinct associations between dynamic properties (occupancy and sojourn times) and severity of disruptive behavior problems, accounting for other commonly co-occurring symptoms. ResultsDynamic network markers of disruptive behavior problems included increased time in network states characterized by globally aberrant connectivity patterns in circuitry involved in cognitive control including frontoparietal and dorsal attention networks. Replication of findings was found in a held-out sample of resting-state fMRI runs in which greater severity of disruptive behavior problems was uniquely linked to greater occupancy time in similarly characterized brain states. ConclusionTransdiagnostic, dynamic resting-state markers of disruptive behavior problems in youth may assist in the development of brain-based biomarkers for monitoring treatment outcomes, assessing circuit target engagement and informing clinical decisions.

neuroscience↗

NUSAP1 regulates mitotic processes via KIF2C interaction and AURKA phosphorylation in primary microcephaly

Primary microcephaly (PM) is a neurodevelopmental disorder characterized by a significantly smaller head than normal. Despite the identification of several genes associated with PM subtypes, the etiology remains unclear in a significant proportion of patients. Here, we reported two de novo nonsense mutations in the NUSAP1 gene identified in two independent PM families: c.1209 C>G (p.Tyr403*) and c.1213C>T (p.Gln405*). Nusap1-edited mouse models, designed to mimic Human NUSAP1 mutations, replicated the microcephaly phenotype, characterized by a small brain and thinner cerebral cortex. We demonstrated that Nusap1 was specifically expressed in neural stem/progenitor cells (NSPCs), and the number of NSPCs was significantly reduced in Nusap1-edited mice. In vitro experiments revealed that NUSAP1 mutations disrupted mitotic metaphase chromosome alignment, increased polyploidy, induced cell cycle arrest, and promoted apoptosis. Co-IP confirmed direct interactions between NUSAP1 and KIF2C, and showed that truncating mutations impaired their binding. Furthermore, NUSAP1 rescued the cell cycle arrest and increased apoptosis caused by KIF2C overexpression in NUSAP1 KO HEK293T cells, whereas the mutant truncated protein failed to exert such a rescue effect. We also uncovered an interaction between NUSAP1 and Aurora kinase A (AURKA). Intriguingly, subsequent experiments revealed that AURKA-mediated phosphorylation of NUSAP1 modulates its binding affinity to KIF2C. In conclusion, our findings established NUSAP1 as a novel pathogenic gene for PM. We proposed a model in which AURKA-mediated phosphorylation of NUSAP1 regulates its interaction with KIF2C, balancing spindle microtubule stability and depolymerization to ensure proper sister chromatid segregation. Disruptions in this pathway impair NSPCs mitosis, thereby affecting neocortical development.

genetics↗

A pan-positive allosteric modulator that mediates sustainable GPCR activation

Approximately 1/3 of all clinical drugs exert their therapeutic effects by modulating the activity of G protein-coupled receptors (GPCRs). Thus, there is a constant interest in finding novel ways to modulate GPCR activity. In this work, through a newly established Survival Pressure Selection (SPS) method for high-throughput screening of GPCR agonists, we discover that atazanavir functions as an agonist for GPR119. Further studies suggest that atazanavir is capable of activating a number of Family A GPCRs, including the {beta}1 adrenergic receptor ({beta}1AR), the {beta}2 adrenergic receptor ({beta}2AR) and the opoioid receptor (OR). Cryo-EM structures reveal that atazanavir binds to an allosteric pocket near TM6/7 in both GPR119 and the {beta}1AR. Pharmacological studies suggest that atazanavir mediates non-canonical signaling of Family A GPCRs. During this process, G protein and {beta}-arrestin are spatially close, but {beta}-arrestin forms a complex with the receptor and G protein instead of desensitizing G protein signaling, thus resulting in sustained G protein signaling. The work expands the signal transduction modes and pharmacological properties of allosteric modulators for GPCRs and provides a general starting point for developing therapeutics targeting this allosteric site.

biochemistry↗

A GPCR-G protein-β-arrestin megacomplex induced by an allosteric modulator

GPCRs signal through both G protein pathways and {beta}-arrestin pathways. Previous work suggests that {beta}-arrestins bind to GPCRs through different modes, including core engagement and tail engagement. Core engagement competes with G proteins and terminates G protein signaling, while tail engagement can coexist with G proteins, mediating sustained intracellular activation of the receptor - a process dependent on the high affinity between {beta}-arrestin and the phosphorylated C-terminus of the receptor. In this study, we determined the structure of a GPCR - G protein - {beta}-arrestin-1 complex stabilized by an allosteric modulator. The compound, atazanavir, acts like molecular glue to anchor {beta}-arrestin-1 to the receptors TM6 and TM7 regions. This pendulum binding mode is structurally compatible with simultaneous G protein binding. We further demonstrate that the atazanavir-mediated {beta}-arrestin recruitment does not require the receptors C-terminal region. This work illustrates a novel paradigm of GPCR-G protein-{beta}-arrestin1 megacomplex assembly and opens up new avenues for modulating GPCR function.

biochemistry↗