LncRNA CamK-A Regulates Ca2+-Signaling-Mediated Tumor Microenvironment Remodeling.

Cancer cells entail metabolic adaptation and microenvironmental remodeling to survive and progress. Both calcium (Ca2+) flux and Ca2+-dependent signaling play a crucial role in this process, although the underlying mechanism has yet to be elucidated. Through RNA screening, we identified one long noncoding RNA (lncRNA) named CamK-A (lncRNA for calcium-dependent kinase activation) in tumorigenesis. CamK-A is highly expressed in multiple human cancers and involved in cancer microenvironment remodeling via activation of Ca2+-triggered signaling. Mechanistically, CamK-A activates Ca2+/calmodulin-dependent kinase PNCK, which in turn phosphorylates IκBα and triggers calcium-dependent nuclear factor κB (NF-κB) activation. This regulation results in the tumor microenvironment remodeling, including macrophage recruitment, angiogenesis, and tumor progression. Notably, our human-patient-derived xenograft (PDX) model studies demonstrate that targeting CamK-A robustly impaired cancer development. Clinically, CamK-A expression coordinates with the activation of CaMK-NF-κB axis, and its high expression indicates poor patient survival rate, suggesting its role as a potential biomarker and therapeutic target.

Disrupted cerebellar structural connectome in spinocerebellar ataxia type 3 and its association with transcriptional profiles.

Spinocerebellar ataxia type 3 (SCA3) is primarily characterized by progressive cerebellar degeneration, including gray matter atrophy and disrupted anatomical and functional connectivity. The alterations of cerebellar white matter structural network in SCA3 and the underlying neurobiological mechanism remain unknown. Using a cohort of 20 patients with SCA3 and 20 healthy controls, we constructed cerebellar structural networks from diffusion MRI and investigated alterations of topological organization. Then, we mapped the alterations with transcriptome data from the Allen Human Brain Atlas to identify possible biological mechanisms for regional selective vulnerability to white matter damage. Compared with healthy controls, SCA3 patients exhibited reduced global and nodal efficiency, along with a widespread decrease in edge strength, particularly affecting edges connected to hub regions. The strength of inter-module connections was lower in SCA3 group and negatively correlated with the Scale for the Assessment and Rating of Ataxia score, International Cooperative Ataxia Rating Scale score, and cytosine-adenine-guanine repeat number. Moreover, the transcriptome-connectome association study identified the expression of genes involved in synapse-related and metabolic biological processes. These findings suggest a mechanism of white matter vulnerability and a potential image biomarker for the disease severity, providing insights into neurodegeneration and pathogenesis in this disease.

Decreased brain iron deposition based on quantitative susceptibility mapping correlates with reduced neurodevelopmental status in children with autism spectrum disorder.

To investigate potential correlations between the susceptibility values of certain brain regions and the severity of disease or neurodevelopmental status in children with autism spectrum disorder (ASD), 18 ASD children and 15 healthy controls (HCs) were recruited. The neurodevelopmental status was assessed by the Gesell Developmental Schedules (GDS) and the severity of the disease was evaluated by the Autism Behavior Checklist (ABC). Eleven brain regions were selected as regions of interest and the susceptibility values were measured by quantitative susceptibility mapping. To evaluate the diagnostic capacity of susceptibility values in distinguishing ASD and HC, the receiver operating characteristic (ROC) curve was computed. Pearson and Spearman partial correlation analysis were used to depict the correlations between the susceptibility values, the ABC scores, and the GDS scores in the ASD group. ROC curves showed that the susceptibility values of the left and right frontal white matter had a larger area under the curve in the ASD group. The susceptibility value of the right globus pallidus was positively correlated with the GDS-fine motor scale score. These findings indicated that the susceptibility value of the right globus pallidus might be a viable imaging biomarker for evaluating the neurodevelopmental status of ASD children.

Associations of quantitative susceptibility mapping with cortical atrophy and brain connectome in Alzheimer's disease: A multi-parametric study.

Aberrant susceptibility due to iron level abnormality and brain network disconnections are observed in Alzheimer's disease (AD), with disrupted iron homeostasis hypothesized to be linked to AD pathology and neuronal loss. However, whether associations exist between abnormal quantitative susceptibility mapping (QSM), brain atrophy, and altered brain connectome in AD remains unclear. Based on multi-parametric brain imaging data from 30 AD patients and 26 healthy controls enrolled at the China-Japan Friendship Hospital, we investigated the abnormality of the QSM signal and volumetric measure across 246 brain regions in AD patients. The structural and functional connectomes were constructed based on diffusion MRI tractography and functional connectivity, respectively. The network topology was quantified using graph theory analyses. We identified seven brain regions with both reduced cortical thickness and abnormal QSM (p < 0.05) in AD, including the right superior frontal gyrus, left superior temporal gyrus, right fusiform gyrus, left superior parietal lobule, right superior parietal lobule, left inferior parietal lobule, and left precuneus. Correlations between cortical thickness and network topology computed across patients in the AD group resulted in statistically significant correlations in five of these regions, with higher correlations in functional compared to structural topology. We computed the correlation between network topological metrics, QSM value and cortical thickness across regions at both individual and group-averaged levels, resulting in a measure we call spatial correlations. We found a decrease in the spatial correlation of QSM and the global efficiency of the structural network in AD patients at the individual level. These findings may provide insights into the complex relationships among QSM, brain atrophy, and brain connectome in AD.

Relationship between topological efficiency of white matter structural connectome and plasma biomarkers across the Alzheimer's disease continuum.

Both plasma biomarkers and brain network topology have shown great potential in the early diagnosis of Alzheimer's disease (AD). However, the specific associations between plasma AD biomarkers, structural network topology, and cognition across the AD continuum have yet to be fully elucidated. This retrospective study evaluated participants from the Sino Longitudinal Study of Cognitive Decline cohort between September 2009 and October 2022 with available blood samples or 3.0-T MRI brain scans. Plasma biomarker levels were measured using the Single Molecule Array platform, including ß-amyloid (Aß), phosphorylated tau181 (p-tau181), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL). The topological structure of brain white matter was assessed using network efficiency. Trend analyses were carried out to evaluate the alterations of the plasma markers and network efficiency with AD progression. Correlation and mediation analyses were conducted to further explore the relationships among plasma markers, network efficiency, and cognitive performance across the AD continuum. Among the plasma markers, GFAP emerged as the most sensitive marker (linear trend: t = 11.164, p = 3.59 × 10-24 ; quadratic trend: t = 7.708, p = 2.25 × 10-13 ; adjusted R2 = 0.475), followed by NfL (linear trend: t = 6.542, p = 2.9 × 10-10 ; quadratic trend: t = 3.896, p = 1.22 × 10-4 ; adjusted R2 = 0.330), p-tau181 (linear trend: t = 8.452, p = 1.61 × 10-15 ; quadratic trend: t = 6.316, p = 1.05 × 10-9 ; adjusted R2 = 0.346) and Aß42/Aß40 (linear trend: t = -3.257, p = 1.27 × 10-3 ; quadratic trend: t = -1.662, p = 9.76 × 10-2 ; adjusted R2 = 0.101). Local efficiency decreased in brain regions across the frontal and temporal cortex and striatum. The principal component of local efficiency within these regions was correlated with GFAP (Pearson's R = -0.61, p = 6.3 × 10-7 ), NfL (R = -0.57, p = 6.4 × 10-6 ), and p-tau181 (R = -0.48, p = 2.0 × 10-4 ). Moreover, network efficiency mediated the relationship between general cognition and GFAP (ab = -0.224, 95% confidence interval [CI] = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA) or NfL (ab = -0.224, 95% CI = [-0.417 to -0.029], p = .0196 for MMSE; ab = -0.198, 95% CI = [-0.42 to -0.003], p = .0438 for MOCA). Our findings suggest that network efficiency mediates the association between plasma biomarkers, specifically GFAP and NfL, and cognitive performance in the context of AD progression, thus highlighting the potential utility of network-plasma approaches for early detection, monitoring, and intervention strategies in the management of AD.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme.

BACKGROUND: Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients. METHODS: We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model. RESULTS: We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination. CONCLUSIONS: We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

Evaluation of whole-brain oxygen metabolism in Alzheimer's disease using QSM and quantitative BOLD.

OBJECTIVE: The objective of this study was to evaluate the whole-brain pattern of oxygen extraction fraction (OEF), cerebral blood flow (CBF), and cerebral metabolic rate of oxygen consumption (CMRO2) perturbation in Alzheimer's disease (AD) and investigate the relationship between regional cerebral oxygen metabolism and global cognition. METHODS: Twenty-six AD patients and 25 age-matched healthy controls (HC) were prospectively recruited in this study. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. We applied the QQ-CCTV algorithm which combines quantitative susceptibility mapping and quantitative blood oxygen level-dependent models (QQ) for OEF calculation. CBF map was computed from arterial spin labeling and CMRO2 was generated based on Fick's principle. Whole-brain and regional OEF, CBF, and CMRO2 analyses were performed. The associations between these measures in substructures of deep brain gray matter and MMSE scores were assessed. RESULTS: Whole brain voxel-wise analysis showed that CBF and CMRO2 values significantly decreased in AD predominantly in the bilateral angular gyrus, precuneus gyrus and parieto-temporal regions. Regional analysis showed that CBF value decreased in the bilateral caudal hippocampus and left rostral hippocampus and CMRO2 value decreased in left caudal and rostral hippocampus in AD patients. Considering all subjects in the AD and HC groups combined, the mean CBF and CMRO2 values in the bilateral hippocampus positively correlated with the MMSE score. CONCLUSION: CMRO2 mapping with the QQ-CCTV method - which is readily available in MR systems for clinical practice - can be a potential biomarker for AD. In addition, CMRO2 in the hippocampus may be a useful tool for monitoring cognitive impairment.

Methodological evaluation of individual cognitive prediction based on the brain white matter structural connectome.

An emerging trend is to use regression-based machine learning approaches to predict cognitive functions at the individual level from neuroimaging data. However, individual prediction models are inherently influenced by the vast options for network construction and model selection in machine learning pipelines. In particular, the brain white matter (WM) structural connectome lacks a systematic evaluation of the effects of different options in the pipeline on predictive performance. Here, we focused on the methodological evaluation of brain structural connectome-based predictions. For network construction, we considered two parcellation schemes for defining nodes and seven strategies for defining edges. For the regression algorithms, we used eight regression models. Four cognitive domains and brain age were targeted as predictive tasks based on two independent datasets (Beijing Aging Brain Rejuvenation Initiative [BABRI]: 633 healthy older adults; Human Connectome Projects in Aging [HCP-A]: 560 healthy older adults). Based on the results, the WM structural connectome provided a satisfying predictive ability for individual age and cognitive functions, especially for executive function and attention. Second, different parcellation schemes induce a significant difference in predictive performance. Third, prediction results from different data sets showed that dMRI with distinct acquisition parameters may plausibly result in a preference for proper fiber reconstruction algorithms and different weighting options. Finally, deep learning and Elastic-Net models are more accurate and robust in connectome-based predictions. Together, significant effects of different options in WM network construction and regression algorithms on the predictive performances are identified in this study, which may provide important references and guidelines to select suitable options for future studies in this field.

Probing the Substrate Requirements of the In†Vitro Geranylation Activity of Selenouridine Synthase (SelU).

Natural RNA modifications diversify the structures and functions of existing nucleic acid building blocks. Geranyl is one of the most hydrophobic groups recently identified in bacterial tRNAs. Selenouridine synthase (SelU, also called mnmH) is an enzyme with a dual activity which catalyzes selenation and geranylation in tRNAs containing 2-thiouridine using selenophosphate or geranyl-pyrophosphate as cofactors. In this study, we explored the in vitro geranylation process of tRNA anticodon stem loops (ASL) mediated by SelU and showed that the geranylation activity was abolished when U35 was mutated to A35 (ASL-tRNALys (s2U)UU to ASL-tRNAIle (s2U)AU ). By examining the SelU cofactor geranyl-pyrophosphate (gePP) and its analogues, we found that only the geranyl group, but not dimethylallyl- and farnesyl-pyrophosphate with either shorter or longer terpene chains, could be incorporated into ASL. The degree of tRNA geranylation in the end-point analysis for SelU follows the order of ASLLys (s2UUU) ≃ ASLGln (s2UUG) >ASLGlu (s2UUC) . These findings suggest a putative mechanism for substrate discrimination by SelU and reveal key factors that might influence its enzymatic activity. Given that SelU plays an important role in bacterial translation systems, inhibiting this enzyme and targeting its geranylation and selenation pathways could be exploited as a promising strategy to develop SelU-based antibiotics.

Radiomics of dual-energy computed tomography for predicting progression-free survival in patients with early glottic cancer.

Aim: This study aimed to predict progression-free survival (PFS) in patients with early glottic cancer using radiomic features on dual-energy computed tomography iodine maps. Methods: Radiomic features were extracted from arterial and venous phase iodine maps, and radiomic risk scores were determined by univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator regression with tenfold cross-validation. The Kaplan-Meier method was used to evaluate the association between radiomic risk scores and PFS. Results: Patients were stratified into low-risk and high-risk groups using radiomics, the PFS corresponding rates with statistical significance between the two groups. The high-risk group showed better survival, benefiting from laryngectomy. Conclusion: Radiomics could provide a promising biomarker for predicting the PFS of early glottic cancer patients.

Multi-center sparse learning and decision fusion for automatic COVID-19 diagnosis.

The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a sharp increase in hospitalized patients with multi-organ disease pneumonia. Early and automatic diagnosis of COVID-19 is essential to slow down the spread of this epidemic and reduce the mortality of patients infected with SARS-CoV-2. In this paper, we propose a joint multi-center sparse learning (MCSL) and decision fusion scheme exploiting chest CT images for automatic COVID-19 diagnosis. Specifically, considering the inconsistency of data in multiple centers, we first convert CT images into histogram of oriented gradient (HOG) images to reduce the structural differences between multi-center data and enhance the generalization performance. We then exploit a 3-dimensional convolutional neural network (3D-CNN) model to learn the useful information between and within 3D HOG image slices and extract multi-center features. Furthermore, we employ the proposed MCSL method that learns the intrinsic structure between multiple centers and within each center, which selects discriminative features to jointly train multi-center classifiers. Finally, we fuse these decisions made by these classifiers. Extensive experiments are performed on chest CT images from five centers to validate the effectiveness of the proposed method. The results demonstrate that the proposed method can improve COVID-19 diagnosis performance and outperform the state-of-the-art methods.

TRIM59 promotes breast cancer motility by suppressing p62-selective autophagic degradation of PDCD10.

Cancer cells adopt various modes of migration during metastasis. How the ubiquitination machinery contributes to cancer cell motility remains underexplored. Here, we report that tripartite motif (TRIM) 59 is frequently up-regulated in metastatic breast cancer, which is correlated with advanced clinical stages and reduced survival among breast cancer patients. TRIM59 knockdown (KD) promoted apoptosis and inhibited tumor growth, while TRIM59 overexpression led to the opposite effects. Importantly, we uncovered TRIM59 as a key regulator of cell contractility and adhesion to control the plasticity of metastatic tumor cells. At the molecular level, we identified programmed cell death protein 10 (PDCD10) as a target of TRIM59. TRIM59 stabilized PDCD10 by suppressing RING finger and transmembrane domain-containing protein 1 (RNFT1)-induced lysine 63 (K63) ubiquitination and subsequent phosphotyrosine-independent ligand for the Lck SH2 domain of 62 kDa (p62)-selective autophagic degradation. TRIM59 promoted PDCD10-mediated suppression of Ras homolog family member A (RhoA)-Rho-associated coiled-coil kinase (ROCK) 1 signaling to control the transition between amoeboid and mesenchymal invasiveness. PDCD10 overexpression or administration of a ROCK inhibitor reversed TRIM59 loss-induced contractile phenotypes, thereby accelerating cell migration, invasion, and tumor formation. These findings establish the rationale for targeting deregulated TRIM59/PDCD10 to treat breast cancer.

Identification of molecular determinants that govern distinct STIM2 activation dynamics.

The endoplasmic reticulum (ER) Ca2+ sensors stromal interaction molecule 1 (STIM1) and STIM2, which connect ER Ca2+ depletion with extracellular Ca2+ influx, are crucial for the maintenance of Ca2+ homeostasis in mammalian cells. Despite the recent progress in unraveling the role of STIM2 in Ca2+ signaling, the mechanistic underpinnings of its activation remain underexplored. We use an engineering approach to direct ER-resident STIMs to the plasma membrane (PM) while maintaining their correct membrane topology, as well as Förster resonance energy transfer (FRET) sensors that enabled in cellulo real-time monitoring of STIM activities. This allowed us to determine the calcium affinities of STIM1 and STIM2 both in cellulo and in situ, explaining the current discrepancies in the literature. We also identified the key structural determinants, especially the corresponding G residue in STIM1, which define the distinct activation dynamics of STIM2. The chimeric E470G mutation could switch STIM2 from a slow and weak Orai channel activator into a fast and potent one like STIM1 and vice versa. The systemic dissection of STIM2 activation by protein engineering sets the stage for the elucidation of the regulation and function of STIM2-mediated signaling in mammals.

Multi-U-Net: Residual Module under Multisensory Field and Attention Mechanism Based Optimized U-Net for VHR Image Semantic Segmentation.

As the acquisition of very high resolution (VHR) images becomes easier, the complex characteristics of VHR images pose new challenges to traditional machine learning semantic segmentation methods. As an excellent convolutional neural network (CNN) structure, U-Net does not require manual intervention, and its high-precision features are widely used in image interpretation. However, as an end-to-end fully convolutional network, U-Net has not explored enough information from the full scale, and there is still room for improvement. In this study, we constructed an effective network module: residual module under a multisensory field (RMMF) to extract multiscale features of target and an attention mechanism to optimize feature information. RMMF uses parallel convolutional layers to learn features of different scales in the network and adds shortcut connections between stacked layers to construct residual blocks, combining low-level detailed information with high-level semantic information. RMMF is universal and extensible. The convolutional layer in the U-Net network is replaced with RMMF to improve the network structure. Additionally, the multiscale convolutional network was tested using RMMF on the Gaofen-2 data set and Potsdam data sets. Experiments show that compared to other technologies, this method has better performance in airborne and spaceborne images.

Augmented Multicenter Graph Convolutional Network for COVID-19 Diagnosis.

Chest computed tomography (CT) scans of coronavirus 2019 (COVID-19) disease usually come from multiple datasets gathered from different medical centers, and these images are sampled using different acquisition protocols. While integrating multicenter datasets increases sample size, it suffers from inter-center heterogeneity. To address this issue, we propose an augmented multicenter graph convolutional network (AM-GCN) to diagnose COVID-19 with steps as follows. First, we use a 3-D convolutional neural network to extract features from the initial CT scans, where a ghost module and a multitask framework are integrated to improve the network's performance. Second, we exploit the extracted features to construct a multicenter graph, which considers the intercenter heterogeneity and the disease status of training samples. Third, we propose an augmentation mechanism to augment training samples which forms an augmented multicenter graph. Finally, the diagnosis results are obtained by inputting the augmented multi-center graph into GCN. Based on 2223 COVID-19 subjects and 2221 normal controls from seven medical centers, our method has achieved a mean accuracy of 97.76%. The code for our model is made publicly.1.

Optical Control of CRAC Channels Using Photoswitchable Azopyrazoles.

The Ca2+ release-activated Ca2+ (CRAC) channels control many Ca2+-modulated physiological processes in mammals. Hyperactivating CRAC channels are known to cause several human diseases, including Stormorken syndrome. Here, we show the design of azopyrazole-derived photoswitchable CRAC channel inhibitors (designated piCRACs), which enable optical inhibition of store-operated Ca2+ influx and downstream signaling. Moreover, piCRAC-1 has been applied in vivo to alleviate thrombocytopenia and hemorrhage in a zebrafish model of Stormorken syndrome in a light-dependent manner.

Sulfenic Acid-Mediated on-Site-Specific Immobilization of Mitochondrial-Targeted NIR Fluorescent Probe for Prolonged Tumor Imaging.

Mitochondria plays pivotal roles in energy production and apoptotic pathways. Mitochondria-targeting strategy has been recognized as a promising way for cancer theranostics. Thus, spatiotemporally manipulating the prolonged retention of theranostic agents within mitochondria is considerably significant in cancer diagnosis and therapy. Herein, as a proof-of concept, we for the first time report a sulfenic acid-responsive platform on controlled immobilization of probes within mitochondria for prolonged tumor imaging. A novel near-infrared (NIR) probe DATC constructed with a NIR dye (Cy5) as signal unit, a cationic triphenylphosphonium (TPP) for mitochondria targeting, and a sulfenic acid-reactive group (1,3-cyclohexanedione) for mitochondrial fixation was rationally designed and synthesized. This probe displayed good target ability to mitochondria and could act as a promising fluorescent probe for specific visualization of endogenous protein sulfenic acids expressed in the mitochondria. Moreover, the probe could be spontaneously fixed on site through the specific reaction and covalent binding to the sulfenic acids of oxidized proteins under oxidative stress, resulting in enhanced intracellular uptake and prolonged retention. We thus believe that this mitochondria-targeted and locational immobilization strategy may offer a new insight for long-term tumor imaging and effective therapy.

Calcium store refilling and STIM activation in STIM- and Orai-deficient cell lines.

Mediated through the combined action of STIM proteins and Orai channels, store-operated Ca2+ entry (SOCE) functions ubiquitously among different cell types. The existence of multiple STIM and Orai genes has made it difficult to assign specific roles of each STIM and Orai homolog in mediating Ca2+ signals. Using CRISPR/Cas9 gene editing tools, we generated cells with both STIM or all three Orai homologs deleted and directly monitored store Ca2+ and Ca2+ signals. We found that unstimulated, SOCE null KO cells still retain 50~70% of ER Ca2+ stores of wildtype (wt) cells. After brief exposure to store-emptying conditions, acute refilling of ER Ca2+ stores was totally blocked in KO cells. However, after 24 h in culture, stores were eventually refilled. Thus, SOCE is critical for immediate refilling of ER Ca2+ but is dispensable for the maintenance of long-term ER Ca2+ homeostasis. Using the Orai null background triple Orai-KO cells, we examined the plasma membrane translocation properties of a series of truncated STIM1 variants. FRET analysis reveals that, even though PM tethering of STIM1 expedites the activation of STIM1 by facilitating its oligomerization, migration, and accumulation in ER-PM junctions, it is not required for the conformational switch, oligomerization, and clustering of STIM1. Even without overt puncta formation at ER-PM junctions, STIM11-491 and STIM11-666 could still rescue SOCE when expressed in STIM KO cells. Thus, ER-PM trapping and clustering of STIM molecules only facilitates the process of SOCE activation, but is not essential for the activation of Orai channels.

Diffusion Kurtosis Imaging Reflects Glial Fibrillary Acidic Protein (GFAP), Topo IIα, and O6-Methylguanine-DNA Methyltransferase (MGMT) Expression in Astrocytomas.

BACKGROUND Astrocytomas are the most common primary brain neoplasms. Biological indicators of astrocytomas can reflect its biological characteristics. The aim of this study was to assess the expression of the pathological glial fibrillary acidic protein (GFAP) Topo IIα and O6-methylguanine-DNA methyltransferase (MGMT) in astrocytomas using magnetic resonance (MR) diffusion kurtosis imaging (DKI) to evaluate the biological characteristics of astrocytomas. MATERIAL AND METHODS Sixty-six patients with pathologically proven astrocytomas were enrolled in this study. All patients underwent conventional MRI head scanning, DKI scanning, and enhanced scanning under the same conditions. Spearman's rank correlation analysis and Bonferroni correction were used to compare the values of DKI and the expression levels of GFAP, Topo IIα, and MGMT between the 2 groups. RESULTS Mean kurtosis (MK) values were negatively correlated with the expression of GFAP (r=-0.836; P=0.03). However, these were positively correlated with the expression of Topo IIα (r=0.896; P=0.01). Moreover, fractional anisotropy (FA) values were not correlated with the expression of GFAP (r=0.366; P=0.05), Topo IIα (r=-0.562; P=0.05), or MGMT (r=-0.153; P=0.10). CONCLUSIONS MK was significantly associated with the expression of GFAP and Topo IIα. To a certain extent, applying DKI may show the biological behavior of tumor cell differentiation, proliferation activity, invasion, and metastasis, and guide individual treatment.