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Cao, S.

Publications and source records attributed to Cao, S..

At least 55 records · Page 3Linked to original sources

Neural control of coordinated wing and leg movements during a terrestrial threat display

Threat displays are a common form of social communication. In flying species, such displays often involve stereotypical, choreographed wing and leg movements. How the brain coordinates characteristic movements of these appendages to transmit social signals is poorly understood. In male Drosophila, threat displays flexibly combine wing displays with rapid turns and charges. Here we identify two brain modules downstream of a superordinate threat command center. Each contains two neurons that combinatorially generate threat-specific appendicular movements: one comprises two descending neurons synergistically controlling wing threat; the other contains two interneurons that antagonistically control turns and charges. Within-module neuronal coactivation evokes appendage-specific actions recapitulating those evoked by the upstream center. These data uncover a hierarchical combinatorial circuit that coordinates wing and leg movements during a terrestrial social display. More generally, our findings identify an instantiation of a Tinbergenian hierarchical behavioral control system for social communication and reveal a novel underlying implementation logic.

neuroscience↗

GRASS-NB: Group-structured variable selection for spatial negative binomial data with applications to cancer registry and spatial omics

Spatially structured, overdispersed count data with high-dimensional predictors are increasingly observed across studies from population-level epidemiology to cellular-level spatial omics. Feature selection is critical to identify influential predictors, such as key risk factors or biomarkers. Few Bayesian studies have assessed negative binomial regression (NBR) models with standard variable selection priors, like the mixture spike-and-slab (SS) or continuous horseshoe (HS), but mostly under aspatial settings. Features often form groups; for instance, in population surveys, caloric intake and physical activity may fall under "Diet & Exercise", while cigarette use and smoking laws belong to "Smoking". We propose a flexible NBR model that accommodates spatial autocorrelation and introduces a novel group-structured prior by hybridizing SS and HS shrinkage. The models performance with different priors is evaluated in terms of specificity, precision, and computational cost under challenging scenarios, including "large p, small n" cases. We further apply the model to CDC state-level cancer data, comprising demographic, screening, and behavioral covariates, to identify key drivers and population-level risk factors, and to a melanoma spatial omics dataset for predictive modeling expression of gene. An efficient R package is provided on GitHub.

bioinformatics↗

SpaceSequest: A unified pipeline for spatial transcriptomics data analysis

BackgroundSpatial transcriptomics has emerged as one of the most powerful tools for gaining biological insights, enabling researchers to uncover intricate relationships between gene expression patterns and tissue architecture. Recent advances in the field have resulted in a variety of new platforms, including Visium, Visium HD, and Xenium from 10x Genomics, as well as GeoMx and CosMx from NanoString Technologies, which has now been acquired by the Bruker Corporation. However, the existence of diverse spatial transcriptomics platforms and various data formats poses challenges in standardizing data analysis. Thus, there remains a critical gap in the availability of a comprehensive pipeline capable of conducting end-to-end analysis that is necessary to extract biological insights from multiple spatial transcriptomics platforms. ResultsHere, we present SpaceSequest, a tailored pipeline that utilizes cutting-edge computational methodologies to conduct a thorough analysis, enabling the extraction of crucial biological insights from five major spatial transcriptomics technologies. SpaceSequest performs (1) standardized quality control and general data processing, (2) key analyses customized for each spatial platform, (3) automated cell type annotation and deconvolution, and (4) high-quality figure and analysis result generation. In addition, SpaceSequest allows for smooth integration with cellxgene VIP and Quickomics for user-friendly data access and interactive visualization. ConclusionsSpaceSequest is a unified and comprehensive pipeline designed for the analysis, visualization, and publication of spatial transcriptomics data from various platforms. The source code is available at https://github.com/interactivereport/SpaceSequest. To facilitate seamless installation and usage, we have also created a detailed Bookdown tutorial that can be accessed through https://interactivereport.github.io/SpaceSequest/tutorial/docs/index.html.

bioinformatics↗

S3R: Spatially Smooth and Sparse Regression Reveals High-Dimensional Regulatory Networks in Spatial Transcriptomics

Spatial transcriptomics (ST) data demands models that recover how associations among molecular and cellular features change across tissue while contending with noise, collinearity, cell mixing, and thousands of predictors. We present Spatially Smooth Sparse Regression (S3R), a general statistical framework that estimates location-specific coefficients linking a response feature to high-dimensional spatial predictors. S3R unites structured sparsity with a minimum-spanning-tree-guided smoothness penalty, yielding coefficient fields that are coherent within neighborhoods yet permit sharp boundaries. In synthetic data, S3R accurately recovers spatially varying effects, selects relevant predictors, and preserves known boundaries. Applied to Visium-based ST data, S3R recapitulates layer-specific target-TF associations in human dorsolateral prefrontal cortex with concordant layer-wise correlations in matched single-cell data. In acute Haemophilus ducreyi skin infection, S3R converts spot-level gene expression mixtures into cell type-attributed expression fields, revealing per-cell type spatial gradients, and improving concordance of spatially variable gene calls when tests are applied to these demixed fields. In pancreatic ductal adenocarcinoma, S3R builds cross-cell-type, cross-gene co-variation tensors that quantify cell-cell interaction strength at gene-pair resolution and nominate interacting genes whose pathway enrichments align with established stromal-epithelial and immune crosstalk. An efficient implementation scales to large assays, and on a Xenium-based breast cancer dataset, S3R delineates the contributions at gene-gene, local neighborhood, and global context-level to target gene expression. Because responses and predictors in S3R are user-defined, it could flexibly address diverse biological questions within a single, scalable, and interpretable regression framework.

bioinformatics↗

Integrative Multi-omics Deciphers the Assembly Rules of the Human Skin Microbiome and Highlights Lipid-Mediated Niche Partitioning

Understanding the ecological assembly mechanisms of microbial communities is critical for deciphering host-microbiota interactions, however, such processes remain poorly characterized in cutaneous ecosystems. By profiling a non-industrialized rural cohort, this study deciphers the assembly rules governing the human skin microbiome and reveals an evolutionarily conserved core microbiota structured by niche selection. Microbial -diversity peaks when C. acnes and M. osloensis reach equilibrium, supporting the Intermediate Disturbance Hypothesis. Host lipids, rather than aqueous compounds, emerged as the primary drivers of microbial niche partitioning. We further demonstrate that C. acnes and Malassezia restricta function as core metabolic engineers, expressing diverse lipases that hydrolyze triglycerides (TGs) into free fatty acids (FFAs), thereby maintaining skin surface lipid homeostasis ([~]85% TG + FFA). Notably, virtually all skin surface FFAs are microbially derived and function as "public goods", enabling cross-feeding that supports auxotrophic members and fosters mutualistic interactions. Importantly, in vitro coculture experiments confirm that C. acnes establish a survival niche for M. restricta. Our findings support a paradigm shift from a taxonomic to a functional understanding of the skin ecosystem, underscoring microbial lipid metabolism as a central orchestrator of ecological stability and a promising target for therapeutic intervention. O_FIG O_LINKSMALLFIG WIDTH=182 HEIGHT=200 SRC="FIGDIR/small/674196v1_ufig1.gif" ALT="Figure 1"> View larger version (62K): org.highwire.dtl.DTLVardef@1274fa5org.highwire.dtl.DTLVardef@1ddfa07org.highwire.dtl.DTLVardef@49f917org.highwire.dtl.DTLVardef@807a14_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGRAPHIC ABSTRACTC_FLOATNO C_FIG

microbiology↗

Pancreatic Cancer Induces Population-Specific Switching of Myosin Isoforms and Discrete Activation of Cachexia Genes in Skeletal Muscle Myocytes

Skeletal muscle loss in pancreatic cancer is a significant cause of morbidity and mortality for patients. In order to understand myocytes changes we examined myonuclei- and myofiber-specific dynamics during pancreatic cancer cachexia progression. Single-nucleus RNA-seq was used to interrogate myonuclear gene expression, and RNAscope and immunofluorescence characterized myofiber-specific changes. Bulk RNA-seq of skeletal muscle provided a whole-muscle transcriptomic profile. Cachexia induces a progressive loss of muscle differentiation factor Maf and its target Myh4, accompanied by increased expression of Myh1 and Myh2. This myofiber dedifferentiation occurs without evidence for fiber type shifting, regeneration, or proliferation. Single-nuclei analysis reveals global shifts in myofiber gene expression identity including the identification of a cachexia only myonuclear subpopulation. Cachexia gene expression was not restricted solely to this PDAC-specific myonuclear subpopulation and did not overlap with Myh1 and Myh2 expressing myonuclei early in cachexia. Altogether, PDAC cachexia elicits distinct transcriptional responses across different myonuclear populations. These results reveal population-specific heterogeneity in cachexia gene activation, rather than a uniform upregulation of cachexia mediators across muscle tissue. Our data suggest that myonuclei fate occurs prior to overt muscle wasting when cachexia gene expression only modestly overlaps with differentiation factors, with a strong association after irreversible muscle wasting. These findings explain the challenge of effectively targeting skeletal muscle wasting in cancer cachexia requires addressing the changing cell population induced through non overlapping mechanisms.

cancer biology↗

Comparative snRNAseq study of C9orf72, SOD1, and sALS spinal cord

Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease characterized by the loss of motor neurons, yet the cell-type specific molecular alterations within the spinal cords are not well characterized. In this study, we conducted deep molecular profiling of spinal cord tissues donated by people living with sporadic, C9orf72, and SOD1-ALS using single-nucleus RNA sequencing (snRNAseq). We observed numerous distinct gene expression patterns and enriched pathways among ALS types. However, when focusing on common features, we identified activation of stress-response and inflammatory pathways in specific microglia subtypes, as well as disrupted vesicle transport and synaptic function in a ventral inhibitory neuronal subtype. Notably, CPLX3, a SNARE regulator, was uniquely expressed in alpha-motor neurons and was commonly downregulated across all ALS types. While this study uncovers the molecular heterogeneity underlying ALS, it also highlights shared pathways within specific cell types, especially in the ventral inhibitory neurons that have been less explored in ALS research.

neuroscience↗

Interleukin enhancer binding factor 2 (Ilf2) and kidney epithelial stress resilience

BackgroundEpithelial cells in the renal medulla are continuously exposed to hyperosmolality, hypoxia, and oxidative stress, yet they display remarkable resilience. The transcriptional programs that endow this intrinsic stress tolerance remain incompletely defined. MethodsWe integrated single-nucleus RNA sequencing of mouse kidneys, computational transcription factor (TF) prioritization, and single-cell CRISPR interference screening (Perturb-seq) in inner medullary collecting duct (IMCD3) cells to systematically identify TFs mediating epithelial stress adaptation. The role of Ilf2 (Interleukin Enhancer Binding Factor 2) was further evaluated in IMCD3 cells by bulk RNA-seq, splicing analysis, functional assays under hyperosmotic stress, as well as in a mouse model of kidney ischemia-reperfusion injury (IRI) and kidney tissues from patients with early and advanced chronic kidney disease (CKD). ResultsComputational prediction and Perturb-seq identified known and novel TFs, that regulate gene expression programs in kidney medullary tubules. Among them, Ilf2 (also called NF45) emerged as a previously unrecognized regulator: Ilf2 knockdown in IMCD3 cells disrupted transcriptional and splicing programs linked to cell proliferation, cytoskeletal organization, and stress adaptation. Ilf2-deficient cells exhibited reduced proliferation, impaired nuclear integrity, and increased sensitivity to hyperosmotic stress. In mouse kidneys, Ilf2 expression increased during tubular repair after IRI, accompanied by induction of Ilf2-dependent transcripts and splicing events. Human kidneys with advanced CKD displayed elevated expression and cytoplasmic translocation of ILF2, suggesting a conserved stress-adaptive response. ConclusionsILF2 orchestrates both transcriptional and post-transcriptional regulatory mechanisms to sustain kidney epithelial stress resilience. Our findings highlight ILF2 as a potential tubular stress biomarker and therapeutic target for enhancing renoprotection. Key PointsO_LIIlf2 has significant and profound impact on gene expression programs in cultured kidney medullary epithelial cells (IMCD3). C_LIO_LIIlf2 confers cellular resilience, nuclear integrity, RNA splicing, proliferative capacity, and osmotic resistance in IMCD3 cells. C_LIO_LIInjured kidneys display increased levels of Ilf2 and up-regulation of genes and splicing events related to Ilf2 function. C_LI

cell biology↗

Dehydration promotes intracellular lipid synthesis and accumulation

Lipids can be considered a water reservoir used to offset dehydration stress as their oxidation by the mitochondria generates water. However, whether dehydration and the ensuing hypertonic stress directly regulate lipid synthesis is unknown. We found that hypertonic stress decreases cellular oxygen consumption, increases intracellular lipid synthesis, and favors glutamine oxidation as a carbon precursor for lipid synthesis via remodeling mitochondrial metabolism. These findings provide a mechanism whereby cellular dehydration leads to intracellular lipid accumulation, functionally linking water availability to lipid storage.

physiology↗

The Lipid Hydrolase ABHD6 is a Therapeutic Target in Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD)-Related Hepatocellular Carcinoma

Primary liver cancer accounts for approximately 700,000 deaths worldwide annually ranking third in cancer-related mortality, with hepatocellular carcinoma (HCC) comprising the majority of these tumors. Metabolic dysfunction-associated steatotic liver disease (MASLD) is currently a leading cause of HCC in the United States. We previously identified the lipid hydrolase alpha/beta hydrolase domain 6 (ABHD6) as a key mediator of the development of metabolic syndrome and intimately involved in cell signaling, making it a prime target for investigation in MASLD-related HCC. ABHD6 displays higher expression within HCC tumor cores when compared to adjacent non-tumor liver tissue in human subjects. Using an in vivo antisense oligonucleotide (ASO)-driven knockdown approach, we have shown the inhibition of ABHD6 prevents the development and progression of HCC in an obesity/MASLD-driven mouse model. Additionally, a xenograft model using the human Huh7 cell line displayed reduced tumor engraftment and growth with ABHD6 genetic deletion and small molecule inhibition. ABHD6 knockout cells demonstrated increased levels of bis(monoacylglycerol)phosphates (BMPs), lipids relevant to high fat diet-induced lysosomal dysfunction, and knockout cells also demonstrated altered autophagy and lysosomal activity using in vitro model of saturated fatty acid-induced lipotoxicity. These studies reveal novel lipid signaling mechanisms by which MASLD progresses towards HCC and provide support for ABHD6 as a therapeutic target in HCC. SignificanceWe have identified that alpha/beta hydrolase domain 6 (ABHD6) plays a role in lysosomal membrane lipid remodeling pathways that are relevant in obesity/MASLD-driven HCC. Inhibitors targeting ABHD6 reorganize lysosomal lipid homeostasis to improve outcomes in HCC.

cancer biology↗

SVPG: A pangenome-based structural variant detection approach and rapid augmentation of pangenome graphs with new samples

Breakthrough advances in long-read sequencing technologies have opened unprecedented opportunities to study genetic variations through comprehensive pangenome analysis. However, the availability of structural variant (SV) calling tools that can effectively leverage pangenome information is limited. In addition, efficient construction of pangenome graphs becomes increasingly challenging with acquisition of larger number of samples. In this study, we present SVPG, an approach that leverages haplotype-resolved pangenome reference for accurate SV detection and rapid pangenome graph augmentation from long-read sequencing data. Compared to state-of-the-art SV callers, SVPG maintained superior overall performance across different sequencing technologies and coverages. SVPG also achieved notable improvements in calling rare and individual-specific SVs on both simulated and real somatic datasets. Furthermore, in a benchmark involving 20 samples, SVPG accelerated pangenome graph augmentation by nearly 10-fold compared to traditional augmentation strategies. We believe that this novel SVPG method, has the potential to revolutionize SV detection and serve as an effective and essential tool, offering new possibilities for advancing pangenomic research.

bioinformatics↗

CSLAN: Cross-Species Latent Alignment Network for Trauma-Related Cell-Type Classification

Understanding cellular identity and function across species is fundamental to deciphering conserved biological principles and translating insights from model organisms to human health. However, integrating single-cell transcriptomic data, particularly from resource-limited human studies, with comprehensive datasets from model organisms like mice remains a significant challenge due to evolutionary divergence and technical variability. Here, we introduce the Cross-Species Latent Alignment Network (CSLAN), a transfer learning framework that effectively bridges this gap for robust cell type classification. CSLAN employs a novel strategy involving focused feature selection on source (mouse) data followed by pretraining an encoder-decoder architecture. Crucially, for transfer to human data, only the encoder is fine-tuned while the latent space processor and the decoder, which encapsulate learned, conserved cell type signatures, remains fixed. This targeted adaptation mechanism allows CSLAN to achieve exceptional accuracy (e.g., 96.67% for human immune cells in a trauma context) by efficiently aligning human gene expression profiles to a biologically informed latent space derived from mouse data. Our findings establish a powerful and broadly applicable paradigm for cross-species knowledge transfer, enabling deeper biological insights from limited human scRNA-seq data by leveraging the wealth of information from model systems, with significant implications for translational research and the construction of comprehensive, cross-species cell atlases.

bioinformatics↗

Deep-learning-enabled multi-omics analyses for prediction of future metastasis in cancer

Metastasis remains the leading cause of cancer-related mortality, yet predicting future metastasis is a major clinical challenge due to the lack of validated biomarkers and effective assessment methods. Here, we present EmitGCL, a deep-learning framework that accurately predicts future metastasis and its corresponding biomarkers. Based on a comprehensive benchmarking comparison, EmitGCL outperformed other computational tools across six cancer types from seven cohorts of patients with superior sensitivity and specificity. It captured occult metastatic cells in a patient with a lymph node-negative breast cancer, who was declared to have no evidence of disease by conventional imaging methods but was later confirmed to have a metastatic disease. Notably, EmitGCL identified HSP90AA1 and HSP90AB1 as predictable biomarkers for future breast cancer metastasis, which was validated across five independent cohorts of patients (n=420). Furthermore, we demonstrated YY1 transcription factor as a key driver of breast cancer metastasis which was validated through in-silico and CRISPR-based migration assays, suggesting that YY1 is a potential therapeutic target for deterring metastasis.

bioinformatics↗

Single-Cell Epigenomics Uncovers Heterochromatin Instability and Transcription Factor Dysfunction during Mouse Brain Aging

The mechanisms regulating transcriptional changes in brain aging remain poorly understood. Here, we use single-cell epigenomics to profile chromatin accessibility and gene expression across eight brain regions in the mouse brain at 2, 9, and 18 months of age. In addition to a significant decline in progenitor cell populations involved in neurogenesis and myelination, we observed widespread and concordant changes of transcription and chromatin accessibility during aging in glial and neuronal cell types. These alterations are accompanied by dysregulation of master transcription factors and a shift toward stress-responsive programs driven by AP-1, indicating a progressive loss of cell identity with aging. We also identify region- and cell-type-specific heterochromatin decay, characterized by increased accessibility at H3K9me3-marked domains, activation of transposable elements, and upregulation of long non-coding RNAs, particularly in glutamatergic neurons. Together, these results reveal age-related disruption of heterochromatin maintenance and transcriptional programs, identify vulnerable brain regions and cell types, and pinpoint key molecular pathways altered in brain aging. HighlightsO_LISingle-cell multimodal profiling across eight brain regions reveals coordinated chromatin and transcriptional shifts during aging C_LIO_LIAge-related depletion of progenitor cells coincides with dysregulation of key developmental transcription factors C_LIO_LICell identity maintenance is compromised with the decline of master transcription factors C_LIO_LIHeterochromatin destabilization accompanied by activation of AP-1, transposable elements, pseudogene families, and long non-coding RNAs C_LI

genomics↗

Cell-type-specific transposable element demethylation and TAD remodeling in the aging mouse brain

Aging is a major risk factor for neurodegenerative diseases, yet underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types. We observed that age-related methylation changes were more pronounced in non-neuronal cells. Transposable element methylation alone distinguished age groups, showing cell-type-specific genome-wide demethylation. Chromatin conformation analysis demonstrated age-related increases in TAD boundary strength with enhanced accessibility at CTCF binding sites. Spatial transcriptomics across 895,296 cells revealed regional heterogeneity during aging within identical cell types. Finally, we developed novel deep-learning models that accurately predict age-related gene expression changes using multi-modal epigenetic features, providing mechanistic insights into gene regulation. This dataset advances our understanding of brain aging and offers potential translational applications. HighlightsO_LISingle-cell multi-omic profiling maps the epigenetic and spatial transcriptomic landscape of brain aging across multiple regions. C_LIO_LICell-type-specific genome-wide demethylation of retrotransposable elements correlates with increased chromatin accessibility and expression. C_LIO_LIElevated TAD boundary strength emerges as a unique marker of brain aging associated with CTCF gaining accessibility. C_LIO_LIA novel deep-learning model reveals the significance of epigenetic features on age-related transcriptomic changes across genes. C_LI

genomics↗

Discovery of D2469079A, A novel selective Toll-Like Receptor 7/8 Antagonist with Brain Penetrance

Aberrant activation of endosomal Toll-like receptors (TLRs) 7 and 8, which recognize single-stranded RNA, is strongly implicated in the pathogenesis of autoimmune diseases such as systemic lupus erythematosus (SLE). And the frequent neuropsychiatric involvement in SLE highlights the need for brain-penetrant therapies. Here, we report the identification and characterization of D2469079A, a novel, potent dual TLR7/TLR8 antagonist (low nM IC50 in humanized HEK-Blue assays) selective against hTLR9. D2469079A demonstrates favorable in vitro properties, including adequate solubility and high microsomal stability. It exhibited desirable pharmacokinetic profiles across multiple preclinical species (mice, rats, dogs) and achieved significant cerebrospinal fluid (CSF) exposure, confirming its high permeability and brain penetration. Consistent in vivo efficacy was observed in both acute and chronic stimulation models. Importantly, D2469079A displayed a favorable safety profile, lacking significant liabilities related to CYP450 inhibition/induction, hERG interaction, or toxicity in a 14-day rat study. These findings position D2469079A as a promising drug candidate with excellent drug-like characteristics for the treatment of TLR7/8-driven autoimmune diseases, potentially including neuropsychiatric manifestations. We are actively seeking partners for collaborative clinical development.

immunology↗

MeDCycFold: A Rosetta Distillation Model to Accelerate Structure Prediction of Cyclic Peptides with Backbone N-methylation and D-amino Acids

Cyclic peptides with backbone N-methylated amino acids(BNMeAAs) and D-amino acids(D-AAs) have gained attention for their stability, membrane permeability, and other therapeutic potentials. Currently, Rosetta can predict their structures using energy calculations, but this method is heavily time-consuming. Moreover, structural data for cyclic peptides containing BNMeAAs and D-AAs are extremely insufficient to build a data-driven structure prediction model. To address these problems, we propose MeDCycFold, a deep learning-based Rosetta distillation model by fine-tuning the AlphaFold model. First, a cyclic peptide structure dataset is constructed using Rosetta by sampling massive conformations for cyclic peptides with BNMeAAs and D-AAs and evaluating their energy scores. Then, the AlphaFold model is fine-tuned with the extended 56 BNMeAAs and D-AAs. Besides, a relative position cyclic matrix is introduced for head-to-tail cyclization in the cyclic peptides. Finally, a force field is employed to reduce clashes in the predicted structures. Empirical experiments show that our proposed MeDCycFold speeds up structure prediction by 49 times while maintaining the prediction accuracy comparable to Rosetta, which can greatly accelerate the development of cyclic peptide drugs.

bioinformatics↗

PCMT1 generates the C-terminal cyclic imide degron on CRBN substrates

The E3 ligase substrate adapter cereblon (CRBN), the primary target of clinical agents thalidomide and lenalidomide, recognizes endogenous substrates bearing the C-terminal cyclic imide modification. Although C-terminal cyclic imides can form spontaneously, an enzyme that regulates the formation of these modifications and thereby promotes a biological pathway connecting substrates to CRBN is unknown. Here, we report that protein carboxymethyltransferase (PCMT1) promotes formation of the C-terminal cyclic imide on C-terminal asparagine residues of CRBN substrates. PCMT1 and CRBN co-regulate the levels of metabolic enzymes glutamine synthetase (GLUL) and inorganic pyrophosphatase 1 (PPA1) in vitro, in cells, and in vivo, and this regulation is associated with the proepileptic phenotype of CRBN knockout mouse models. The discovery of an enzyme that regulates CRBN substrates through the C-terminal cyclic imide modification reveals a previously unknown biological pathway that is perturbed by thalidomide derivatives and provides a biochemical basis for the connection between multiple biological processes and CRBN.

biochemistry↗