Direct interrogation of context-dependent GPCR activity with a universal biosensor platform.

G protein-coupled receptors (GPCRs) are the largest family of druggable proteins encoded in the human genome, but progress in understanding and targeting them is hindered by the lack of tools to reliably measure their nuanced behavior in physiologically relevant contexts. Here, we developed a collection of compact ONE vector G-protein Optical (ONE-GO) biosensor constructs as a scalable platform that can be conveniently deployed to measure G-protein activation by virtually any GPCR with high fidelity even when expressed endogenously in primary cells. By characterizing dozens of GPCRs across many cell types like primary cardiovascular cells or neurons, we revealed insights into the molecular basis for G-protein coupling selectivity of GPCRs, pharmacogenomic profiles of anti-psychotics on naturally occurring GPCR variants, and G-protein subtype signaling bias by endogenous GPCRs depending on cell type or upon inducing disease-like states. In summary, this open-source platform makes the direct interrogation of context-dependent GPCR activity broadly accessible.

Revealing the Activity of Trimeric G-proteins in Live Cells with a Versatile Biosensor Design.

Heterotrimeric G-proteins (Gαßγ) are the main transducers of signals from GPCRs, mediating the action of countless natural stimuli and therapeutic agents. However, there are currently no robust approaches to directly measure the activity of endogenous G-proteins in cells. Here, we describe a suite of optical biosensors that detect endogenous active G-proteins with sub-second resolution in live cells. Using a modular design principle, we developed genetically encoded, unimolecular biosensors for endogenous Gα-GTP and free Gßγ: the two active species of heterotrimeric G-proteins. This design was leveraged to generate biosensors with specificity for different heterotrimeric G-proteins or for other G-proteins, such as Rho GTPases. Versatility was further validated by implementing the biosensors in multiple contexts, from characterizing cancer-associated G-protein mutants to neurotransmitter signaling in primary neurons. Overall, the versatile biosensor design introduced here enables studying the activity of endogenous G-proteins in live cells with high fidelity, temporal resolution, and convenience.

Fine-tuning GPCR-mediated neuromodulation by biasing signaling through different G protein subunits.

G-protein-coupled receptors (GPCRs) mediate neuromodulation through the activation of heterotrimeric G proteins (Gαßγ). Classical models depict that G protein activation leads to a one-to-one formation of Gα-GTP and Gßγ species. Each of these species propagates signaling by independently acting on effectors, but the mechanisms by which response fidelity is ensured by coordinating Gα and Gßγ responses remain unknown. Here, we reveal a paradigm of G protein regulation whereby the neuronal protein GINIP (Gα inhibitory interacting protein) biases inhibitory GPCR responses to favor Gßγ over Gα signaling. Tight binding of GINIP to Gαi-GTP precludes its association with effectors (adenylyl cyclase) and, simultaneously, with regulator-of-G-protein-signaling (RGS) proteins that accelerate deactivation. As a consequence, Gαi-GTP signaling is dampened, whereas Gßγ signaling is enhanced. We show that this mechanism is essential to prevent the imbalances of neurotransmission that underlie increased seizure susceptibility in mice. Our findings reveal an additional layer of regulation within a quintessential mechanism of signal transduction that sets the tone of neurotransmission.

Small-molecule targeting of GPCR-independent noncanonical G-protein signaling in cancer.

Activation of heterotrimeric G-proteins (Gαßγ) by G-protein-coupled receptors (GPCRs) is a quintessential mechanism of cell signaling widely targeted by clinically approved drugs. However, it has become evident that heterotrimeric G-proteins can also be activated via GPCR-independent mechanisms that remain untapped as pharmacological targets. GIV/Girdin has emerged as a prototypical non-GPCR activator of G proteins that promotes cancer metastasis. Here, we introduce IGGi-11, a first-in-class small-molecule inhibitor of noncanonical activation of heterotrimeric G-protein signaling. IGGi-11 binding to G-protein α-subunits (Gαi) specifically disrupted their engagement with GIV/Girdin, thereby blocking noncanonical G-protein signaling in tumor cells and inhibiting proinvasive traits of metastatic cancer cells. In contrast, IGGi-11 did not interfere with canonical G-protein signaling mechanisms triggered by GPCRs. By revealing that small molecules can selectively disable noncanonical mechanisms of G-protein activation dysregulated in disease, these findings warrant the exploration of therapeutic modalities in G-protein signaling that go beyond targeting GPCRs.

Augmented interpretation of HER2, ER, and PR in breast cancer by artificial intelligence analyzer: enhancing interobserver agreement through a reader study of 201 cases.

BACKGROUND: Accurate classification of breast cancer molecular subtypes is crucial in determining treatment strategies and predicting clinical outcomes. This classification largely depends on the assessment of human epidermal growth factor receptor 2 (HER2), estrogen receptor (ER), and progesterone receptor (PR) status. However, variability in interpretation among pathologists pose challenges to the accuracy of this classification. This study evaluates the role of artificial intelligence (AI) in enhancing the consistency of these evaluations. METHODS: AI-powered HER2 and ER/PR analyzers, consisting of cell and tissue models, were developed using 1,259 HER2, 744 ER, and 466 PR-stained immunohistochemistry (IHC) whole-slide images of breast cancer. External validation cohort comprising HER2, ER, and PR IHCs of 201 breast cancer cases were analyzed with these AI-powered analyzers. Three board-certified pathologists independently assessed these cases without AI annotation. Then, cases with differing interpretations between pathologists and the AI analyzer were revisited with AI assistance, focusing on evaluating the influence of AI assistance on the concordance among pathologists during the revised evaluation compared to the initial assessment. RESULTS: Reevaluation was required in 61 (30.3%), 42 (20.9%), and 80 (39.8%) of HER2, in 15 (7.5%), 17 (8.5%), and 11 (5.5%) of ER, and in 26 (12.9%), 24 (11.9%), and 28 (13.9%) of PR evaluations by the pathologists, respectively. Compared to initial interpretations, the assistance of AI led to a notable increase in the agreement among three pathologists on the status of HER2 (from 49.3 to 74.1%, p < 0.001), ER (from 93.0 to 96.5%, p = 0.096), and PR (from 84.6 to 91.5%, p = 0.006). This improvement was especially evident in cases of HER2 2+ and 1+, where the concordance significantly increased from 46.2 to 68.4% and from 26.5 to 70.7%, respectively. Consequently, a refinement in the classification of breast cancer molecular subtypes (from 58.2 to 78.6%, p < 0.001) was achieved with AI assistance. CONCLUSIONS: This study underscores the significant role of AI analyzers in improving pathologists' concordance in the classification of breast cancer molecular subtypes.

An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between pathologists due to the consideration of both immune and tumour cell positivity. METHODS AND RESULTS: An artificial intelligence (AI)-powered PD-L1 CPS analyser was developed using 1,275,907 cells and 6175.42 mm2 of tissue annotated by pathologists, extracted from 400 PD-L1 22C3-stained whole slide images of UC. We validated the AI model on 543 UC PD-L1 22C3 cases collected from three institutions. There were 446 cases (82.1%) where the CPS results (CPS ≥10 or <10) were in complete agreement between three pathologists, and 486 cases (89.5%) where the AI-powered CPS results matched the consensus of two or more pathologists. In the pathologist's assessment of the CPS, statistically significant differences were noted depending on the source hospital (P = 0.003). Three pathologists reevaluated discrepancy cases with AI-powered CPS results. After using the AI as a guide and revising, the complete agreement increased to 93.9%. The AI model contributed to improving the concordance between pathologists across various factors including hospital, specimen type, pathologic T stage, histologic subtypes, and dominant PD-L1-positive cell type. In the revised results, the evaluation discordance among slides from different hospitals was mitigated. CONCLUSION: This study suggests that AI models can help pathologists to reduce discrepancies between pathologists in quantifying immunohistochemistry including PD-L1 22C3 CPS, especially when evaluating data from different institutions, such as in a telepathology setting.

Improving Rebar Twist Prediction Exploiting Unified-Channel Attention-Based Image Restoration and Regression Techniques.

Recent research has made significant progress in automated unmanned systems utilizing Artificial Intelligence (AI)-based image processing to optimize the rebar manufacturing process and minimize defects such as twisting during production. Despite various studies, including those employing data augmentation through Generative Adversarial Networks (GANs), the performance of rebar twist prediction has been limited due to image quality degradation caused by environmental noise, such as insufficient image quality and inconsistent lighting conditions in rebar processing environments. To address these challenges, we propose a novel approach for real-time rebar twist prediction in manufacturing processes. Our method involves restoring low-quality grayscale images to high resolution and employing an object detection model to identify and track rebar endpoints. We then apply regression analysis to the coordinates obtained from the bounding boxes to estimate the error rate of the rebar endpoint positions, thereby determining the occurrence of twisting. To achieve this, we first developed a Unified-Channel Attention (UCA) module that is robust to changes in intensity and contrast for grayscale images. The UCA can be integrated into image restoration models to more accurately detect rebar endpoint characteristics in object detection models. Furthermore, we introduce a method for predicting the future positions of rebar endpoints using various linear and non-linear regression models. The predicted positions are used to calculate the error rate in rebar endpoint locations, determined by the distance between the actual and predicted positions, which is then used to classify the presence of rebar twisting. Our experimental results demonstrate that integrating the UCA module with our image restoration model significantly improved existing models in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics. Moreover, employing regression models to predict future rebar endpoint positions enhances the F1 score for twist prediction. As a result, our approach offers a practical solution for rapid defect detection in rebar manufacturing processes.

Wide-Field Super-Resolution Optical Fluctuation Imaging through Dynamic Near-Field Speckle Illumination.

Stochastic optical fluctuation imaging (SOFI) generates super-resolution fluorescence images by emphasizing the positions of fluorescent emitters via statistical analysis of their on-and-off blinking dynamics. In SOFI with speckle illumination (S-SOFI), the diffraction-limited grain size of the far-field speckles prevents independent blinking of closely located emitters, becoming a hurdle to realize the full super-resolution granted by SOFI processing. Here, we present a surface-sensitive super-resolution technique exploiting dynamic near-field speckle illumination to bring forth the full super-resolving power of SOFI without blinking fluorophores. With our near-field S-SOFI technique, up to 2.8- and 2.3-fold enhancements in lateral spatial resolution are demonstrated with computational and experimental fluorescent test targets labeled with conventional fluorophores, respectively. Fluorescent beads separated by 175 nm are also super-resolved by near-field speckles of 150 nm grain size, promising sub-100 nm resolution with speckle patterns of much smaller grain size.

The QPLEX™ Plus Assay Kit for the Early Clinical Diagnosis of Alzheimer's Disease.

We recently developed a multiplex diagnostic kit, QPLEX™ Alz plus assay kit, which captures amyloid-ß1-40, galectin-3 binding protein, angiotensin-converting enzyme, and periostin simultaneously using microliters of peripheral blood and utilizes an optimized algorithm for screening Alzheimer's disease (AD) by correlating with cerebral amyloid deposition. Owing to the demand for early AD detection, we investigate the potential of our kit for the early clinical diagnosis of AD. A total of 1395 participants were recruited, and their blood samples were analyzed with the QPLEX™ kit. The average of QPLEX™ algorithm values in each group increased gradually in the order of the clinical progression continuum of AD: cognitively normal (0.382 ± 0.150), subjective cognitive decline (0.452 ± 0.130), mild cognitive impairment (0.484 ± 0.129), and AD (0.513 ± 0.136). The algorithm values between each group showed statistically significant differences among groups divided by Mini-Mental State Examination and Clinical Dementia Rating. The QPLEX™ algorithm values could be used to distinguish the clinical continuum of AD or cognitive function. Because blood-based diagnosis is more accessible, convenient, and cost- and time-effective than cerebral spinal fluid or positron emission tomography imaging-based diagnosis, the QPLEX™ kit can potentially be used for health checkups and the early clinical diagnosis of AD.

Retrofitting of an implant-supported surveyed crown under an existing removable partial denture by using an acrylic resin template.

Retrofitting a crown to an existing removable partial denture (RPD) is a complex process and requires additional clinical and laboratory procedures. Various methods have been described for retrofitting a new tooth-supported crown. However, if an abutment tooth has to be extracted, descriptions of techniques for restoring a new edentulous site with an implant-supported crown retrofitted to an existing RPD are lacking. Therefore, this technical report describes a straightforward approach to fabricating an implant-supported surveyed crown fitted to an existing RPD by using an acrylic resin template.

Neurotoxicity of phenylalanine on human iPSC-derived cerebral organoids.

Phenylketonuria (PKU) is a common genetic metabolic disorder that causes phenylalanine accumulation in the blood. The most serious symptoms are related to the brain, as intellectual disability, seizure, and microcephaly are commonly found in poorly treated PKU patients and the babies of maternal PKU. However, the mechanism of hyperphenylalaninemia on human neurodevelopment is still unclear. Here we utilized human induced pluripotent stem cell (iPSC)-derived cerebral organoids to investigate the neurotoxicity of hyperphenylalaninemia. Cerebral organoids at days 40 or 100 were treated with different concentrations of phenylalanine for 5 days. After phenylalanine treatments, the cerebral organoids displayed alterations in organoid size, induction of apoptosis, and depletion of neural progenitor cells. However, phenylalanine did not have an impact on neurons and glia, including astrocytes, immature oligodendrocytes, and mature oligodendrocytes. Remarkably, a reduction in the thickness of the cortical rosettes and a decrease in myelination at the intermediate zone were inspected with the elevated phenylalanine concentrations. RNA-seq of phenylalanine-treated organoids revealed that gene sets related to apoptosis, p53 signaling pathway, and TNF signaling pathway via NF-kB were enriched in upregulated genes, while those related to cell cycle and amino acid metabolism were enriched in downregulated genes. In addition, there were several microcephaly disease genes, such as ASPM, LMNB1, and CENPE, ranked at the top of the downregulated genes. These findings indicate that phenylalanine exposure may contribute to microcephaly, abnormal cortical expansion, and myelination lesions in the developing human brain.

Intracellular Ca2+ accumulation triggered by caffeine provokes resistance against a broad range of biotic stress in rice.

Chemical pesticides are still frequently overused to diminish such crop loss caused by biotic stress despite the threat to humans and the environment. Thus, it is urgent to find safer and more effective defense strategies. In this study, we report that caffeine, implanted through a transgenic approach, enhances resistance against variable biotic stresses in rice without fitness cost. Caffeine-producing rice (CPR) was generated by introducing three N-methyltransferase genes involved in the biosynthesis of caffeine in coffee plants. The CPR plants have no differences in morphology and growth compared to their wild-type counterparts, but they show strongly enhanced resistance to both bacterial leaf blight, rice blast, and attack of white-backed planthoppers. Caffeine acts as a repellent agent against rice pathogens. Moreover, caffeine triggers a series of Ca2+ signalling-like processes to synthesize salicylic acid (SA), a hormone associated with plant resistance. In CPR, phosphodiesterase was inhibited by caffeine, cAMP and cGMP increased, intracellular Ca2+ increased, phenylalanine lyase (PAL) was activated by OsCPK1, and SA synthesis was activated. This finding is a novel strategy to improve resistance against the biotic stresses of crops with a special type of defense inducer.

Development and validation of a deep learning algorithm detecting 10 common abnormalities on chest radiographs.

We aimed to develop a deep learning algorithm detecting 10 common abnormalities (DLAD-10) on chest radiographs, and to evaluate its impact in diagnostic accuracy, timeliness of reporting and workflow efficacy.DLAD-10 was trained with 146 717 radiographs from 108 053 patients using a ResNet34-based neural network with lesion-specific channels for 10 common radiological abnormalities (pneumothorax, mediastinal widening, pneumoperitoneum, nodule/mass, consolidation, pleural effusion, linear atelectasis, fibrosis, calcification and cardiomegaly). For external validation, the performance of DLAD-10 on a same-day computed tomography (CT)-confirmed dataset (normal:abnormal 53:147) and an open-source dataset (PadChest; normal:abnormal 339:334) was compared with that of three radiologists. Separate simulated reading tests were conducted on another dataset adjusted to real-world disease prevalence in the emergency department, consisting of four critical, 52 urgent and 146 nonurgent cases. Six radiologists participated in the simulated reading sessions with and without DLAD-10.DLAD-10 exhibited area under the receiver operating characteristic curve values of 0.895-1.00 in the CT-confirmed dataset and 0.913-0.997 in the PadChest dataset. DLAD-10 correctly classified significantly more critical abnormalities (95.0% (57/60)) than pooled radiologists (84.4% (152/180); p=0.01). In simulated reading tests for emergency department patients, pooled readers detected significantly more critical (70.8% (17/24) versus 29.2% (7/24); p=0.006) and urgent (82.7% (258/312) versus 78.2% (244/312); p=0.04) abnormalities when aided by DLAD-10. DLAD-10 assistance shortened the mean±sd time-to-report critical and urgent radiographs (640.5±466.3 versus 3371.0±1352.5 s and 1840.3±1141.1 versus 2127.1±1468.2 s, respectively; all p<0.01) and reduced the mean±sd interpretation time (20.5±22.8 versus 23.5±23.7 s; p<0.001).DLAD-10 showed excellent performance, improving radiologists' performance and shortening the reporting time for critical and urgent cases.

A mutant KRAS-induced factor REG4 promotes cancer stem cell properties via Wnt/ß-catenin signaling.

Mutant KRAS provides a driving force for enhancement of cancer stem cells (CSCs) characteristics contributing transformation of colorectal cancer (CRC) cells harboring adenomatous polyposis coli (APC) mutations. Here, we identified the factors mediating the promotion of CSCs properties induced by KRAS mutation through microarray analyses of genes specifically induced in CRC spheroids harboring both KRAS and APC mutations. Among them, REG4 was identified as a key factor since CRISPR/Cas9-mediated knockout of REG4 most significantly affected the stem cell characteristics in which CSCs markers were effectively suppressed. We show that REG4 mediates promotion of CSCs properties via Wnt/ß-catenin signaling in various in vitro studies including tumor organoid systems. Furthermore, expression patterns of CSCs markers and REG4 correlated in intestinal tumors from Apcmin/+ /KrasG12D LA2 mice and in CRC patient tissues harboring both KRAS and APC mutations. The role of REG4 in the tumor-initiating capacity accompanied by enhancement of CSCs characteristics was also revealed by NSG mice xenograft system. Collectively, our study highlights the importance of REG4 in promoting CSCs properties induced by KRAS mutation, and provides a new therapeutic strategy for CRC harboring both APC and KRAS mutations.

Snapshot hyperspectral light field imaging using image mapping spectrometry.

In this Letter, we present a snapshot hyperspectral light field imaging system using a single camera. By integrating an unfocused light field camera with a snapshot hyperspectral imager, the image mapping spectrometer, we captured a five-dimensional (5D) ($x,y,u,v,\lambda $x,y,u,v,λ) ($x,y,$x,y, spatial coordinates; $u,v,$u,v, emittance angles; $\lambda ,$λ, wavelength) datacube in a single camera exposure. The corresponding volumetric image ($x,y,z$x,y,z) at each wavelength is then computed through a scale-depth space transform. We demonstrated the snapshot advantage of our system by imaging the spectral-volumetric scenes in real time.

Plasma tau/amyloid-ß1-42 ratio predicts brain tau deposition and neurodegeneration in Alzheimer's disease.

One of the hallmarks of Alzheimer's disease is abnormal deposition of tau proteins in the brain. Although plasma tau has been proposed as a potential biomarker for Alzheimer's disease, a direct link to brain deposition of tau is limited. Here, we estimated the amount of in vivo tau deposition in the brain by PET imaging and measured plasma levels of total tau (t-tau), phosphorylated tau (p-tau, T181) and amyloid-ß1-42. We found significant correlations of plasma p-tau, t-tau, p-tau/amyloid-ß1-42, and t-tau/amyloid-ß1-42 with brain tau deposition in cross-sectional and longitudinal manners. In particular, t-tau/amyloid-ß1-42 in plasma was highly predictive of brain tau deposition, exhibiting 80% sensitivity and 91% specificity. Interestingly, the brain regions where plasma t-tau/amyloid-ß1-42 correlated with brain tau were similar to the typical deposition sites of neurofibrillary tangles in Alzheimer's disease. Furthermore, the longitudinal changes in cerebral amyloid deposition, brain glucose metabolism, and hippocampal volume change were also highly associated with plasma t-tau/amyloid-ß1-42. These results indicate that combination of plasma tau and amyloid-ß1-42 levels might be potential biomarkers for predicting brain tau pathology and neurodegeneration.

Development of a fast calibration method for image mapping spectrometry.

An image mapping spectrometer (IMS) is a snapshot hyperspectral imager that simultaneously captures both the spatial (x, y) and spectral (λ) information of incoming light. The IMS maps a three-dimensional (3D) datacube (x, y, λ) to a two-dimensional (2D) detector array (x, y) for parallel measurement. To reconstruct the original 3D datacube, one must construct a lookup table that connects voxels in the datacube and pixels in the raw image. Previous calibration methods suffer from either low speed or poor image quality. We herein present a slit-scan calibration method that can significantly reduce the calibration time while maintaining high accuracy. Moreover, we quantitatively analyzed the major artifact in the IMS, the striped image, and developed three numerical methods to correct for it.

A biochemical and genetic discovery pipeline identifies PLCδ4b as a nonreceptor activator of heterotrimeric G-proteins.

Recent evidence has revealed that heterotrimeric G-proteins can be activated by cytoplasmic proteins that share an evolutionarily conserved sequence called the Gα-binding-and-activating (GBA) motif. This mechanism provides an alternative to canonical activation by G-protein-coupled receptors (GPCRs) and plays important roles in cell function, and its dysregulation is linked to diseases such as cancer. Here, we describe a discovery pipeline that uses biochemical and genetic approaches to validate GBA candidates identified by sequence similarity. First, putative GBA motifs discovered in bioinformatics searches were synthesized on peptide arrays and probed in batch for Gαi3 binding. Then, cDNAs encoding proteins with Gαi3-binding sequences were expressed in a genetically-modified yeast strain that reports mammalian G-protein activity in the absence of GPCRs. The resulting GBA motif candidates were characterized by comparison of their biochemical, structural, and signaling properties with those of all previously described GBA motifs in mammals (GIV/Girdin, DAPLE, Calnuc, and NUCB2). We found that the phospholipase Cδ4 (PLCδ4) GBA motif binds G-proteins with high affinity, has guanine nucleotide exchange factor activity in vitro, and activates G-protein signaling in cells, as indicated by bioluminescence resonance energy transfer (BRET)-based biosensors of G-protein activity. Interestingly, the PLCδ4 isoform b (PLCδ4b), which lacks the domains required for PLC activity, bound and activated G-proteins more efficiently than the full-length isoform a, suggesting that PLCδ4b functions as a G-protein regulator rather than as a PLC. In summary, we have identified PLCδ4 as a nonreceptor activator of G-proteins and established an experimental pipeline to discover and characterize GBA motif-containing proteins.

WDR76 degrades RAS and suppresses cancer stem cell activation in colorectal cancer.

BACKGROUND: Stabilization of RAS is a key event for the hyper-activation of Wnt/ß-catenin signaling and activation of cancer stem cell (CSC) in colorectal cancer (CRC). WD Repeat protein 76 (WDR76) mediates the polyubiquitination-dependent degradation of RAS in hepatocellular carcinoma (HCC). We investigated whether WDR76 destabilizes RAS and acts as a tumor suppressor inhibiting CSC activation in CRC. METHODS: We generated mice with deletion of Wdr76 (Wdr76-/-) and crosses of Wdr76-/- with ApcMin/+ (Wdr76-/-; ApcMin/+) and compared them with wildtype mice (Wdr76+/+) and ApcMin/+ mice (Wdr76+/+; ApcMin/+), respectively. Intestinal crypt lengthening, tumorigenesis and CSC activation were analyzed by histology, immunohistochemistry, and immunoblotting. CRC cell line was engineered to stably express or knockdown WDR76 or control vector and was analyzed after spheroid culture. RESULTS: Wdr76-/- mice, with increased Ras level, displayed crypt elongation and hyper-proliferation. Wdr76-/-; ApcMin/+ mice developed more tumors with bigger sizes than ApcMin/+ mice and their tumors showed increased proliferation and CSC activation with elevated RAS and ß-catenin levels. In CRC cells, overexpression or knockdown of WDR76 decreased or increased the numbers and sizes of CRC spheroids with inhibition or activation of CSC markers, respectively. In human CRC, lower level of WDR76 was associated with poor patient survival. CONCLUSIONS: In analyses of mice with deletion of Wdr76 and CRC spheroids, we found that RAS stability plays important roles in tumorigenesis by affecting proliferation and CSC activation. Our results suggest that destabilization of RAS by WDR76 is a potential strategy for targeting malignant CRC involving CSC activation.

Retinoic-acid-mediated HRas stabilization induces neuronal differentiation of neural stem cells during brain development.

Ras signaling is tightly regulated during neural stem cell (NSC) differentiation, and defects in this pathway result in aberrant brain development. However, the mechanism regulating Ras signaling during NSC differentiation was unknown. Here, we show that stabilized HRas specifically induces neuronal differentiation of NSCs. Lentivirus-mediated HRas overexpression and knockdown resulted in stimulation and inhibition, respectively, of NSC differentiation into neuron in the ex vivo embryo. Retinoic acid, an active metabolite of vitamin A, promoted neuronal differentiation of NSCs by stabilizing HRas, and HRas knockdown blocked the retinoic acid effect. Vitamin-A-deficient mice displayed abnormal brain development with reduced HRas levels and a reduced thickness of the postmitotic region containing differentiated neurons. All of these abnormal phenotypes were rescued with the restoration of HRas protein levels achieved upon feeding with a retinoic-acid-supplemented diet. In summary, this study shows that retinoic acid stabilizes HRas protein during neurogenesis, and that this is required for NSC differentiation into neurons and murine brain development.