Aberrant DJ-1 expression underlies L-type calcium channel hypoactivity in dendrites in tuberous sclerosis complex and Alzheimer's disease.

L-type voltage-gated calcium (Ca2+) channels (L-VGCC) dysfunction is implicated in several neurological and psychiatric diseases. While a popular therapeutic target, it is unknown whether molecular mechanisms leading to disrupted L-VGCC across neurodegenerative disorders are conserved. Importantly, L-VGCC integrate synaptic signals to facilitate a plethora of cellular mechanisms; however, mechanisms that regulate L-VGCC channel density and subcellular compartmentalization are understudied. Herein, we report that in disease models with overactive mammalian target of rapamycin complex 1 (mTORC1) signaling (or mTORopathies), deficits in dendritic L-VGCC activity are associated with increased expression of the RNA-binding protein (RBP) Parkinsonism-associated deglycase (DJ-1). DJ-1 binds the mRNA coding for the alpha and auxiliary Ca2+ channel subunits CaV1.2 and α2δ2, and represses their mRNA translation, only in the disease states, specifically preclinical models of tuberous sclerosis complex (TSC) and Alzheimer's disease (AD). In agreement, DJ-1-mediated repression of CaV1.2/α2δ2 protein synthesis in dendrites is exaggerated in mouse models of AD and TSC, resulting in deficits in dendritic L-VGCC calcium activity. Finding of DJ-1-regulated L-VGCC activity in dendrites in TSC and AD provides a unique signaling pathway that can be targeted in clinical mTORopathies.

A transformer-based deep-learning approach for classifying brain metastases into primary organ sites using clinical whole-brain MRI images.

Treatment decisions for brain metastatic disease rely on knowledge of the primary organ site and are currently made with biopsy and histology. Here, we develop a deep-learning approach for accurate non-invasive digital histology with whole-brain magnetic resonance imaging (MRI) data. Contrast-enhanced T1-weighted and fast spoiled gradient echo brain MRI exams (n = 1,582) were preprocessed and input to the proposed deep-learning workflow for tumor segmentation, modality transfer, and primary site classification into one of five classes. Tenfold cross-validation generated an overall area under the receiver operating characteristic curve (AUC) of 0.878 (95% confidence interval [CI]: 0.873,0.883). These data establish that whole-brain imaging features are discriminative enough to allow accurate diagnosis of the primary organ site of malignancy. Our end-to-end deep radiomic approach has great potential for classifying metastatic tumor types from whole-brain MRI images. Further refinement may offer an invaluable clinical tool to expedite primary cancer site identification for precision treatment and improved outcomes.

HSP90-CDC37 functions as a chaperone for the oncogenic FGFR3-TACC3 fusion.

The FGFR3-TACC3 (F3-T3) fusion gene was discovered as an oncogenic molecule in glioblastoma and bladder cancers, and has subsequently been found in many cancer types. Notably, F3-T3 was found to be highly expressed in both untreated and matched recurrence glioblastoma under the concurrent radiotherapy and temozolomide (TMZ) treatment, suggesting that targeting F3-T3 is a valid strategy for treatment. Here, we show that the F3-T3 protein is a client of heat shock protein 90 (HSP90), forming a ternary complex with the cell division cycle 37 (CDC37). Deprivation of HSP90 or CDC37 disrupts the formation of the ternary complex, which destabilizes glycosylated F3-T3, and thereby suppresses F3-T3 oncogenic activity. Gliomas harboring F3-T3 are resistant to TMZ chemotherapy. HSP90 inhibitors sensitized F3-T3 glioma cells to TMZ via the inhibition of F3-T3 activation and potentiated TMZ-induced DNA damage. These results demonstrate that F3-T3 oncogenic function is dependent on the HSP90 chaperone system and suggests a new clinical option for targeting this genetic aberration in cancer.

Role of FMRP in rapid antidepressant effects and synapse regulation.

Rapid antidepressants are novel treatments for major depressive disorder (MDD) and work by blocking N-methyl-D-aspartate receptors (NMDARs), which, in turn, activate the protein synthesis pathway regulated by mechanistic/mammalian target of rapamycin complex 1 (mTORC1). Our recent work demonstrates that the RNA-binding protein Fragile X Mental Retardation Protein (FMRP) is downregulated in dendrites upon treatment with a rapid antidepressant. Here, we show that the behavioral effects of the rapid antidepressant Ro-25-6981 require FMRP expression, and treatment promotes differential mRNA binding to FMRP in an mTORC1-dependent manner. Further, these mRNAs are identified to regulate transsynaptic signaling. Using a novel technique, we show that synapse formation underlying the behavioral effects of Ro-25-6981 requires GABABR-mediated mTORC1 activity in WT animals. Finally, we demonstrate that in an animal model that lacks FMRP expression and has clinical relevance for Fragile X Syndrome (FXS), GABABR activity is detrimental to the effects of Ro-25-6981. These effects are rescued with the combined therapy of blocking GABABRs and NMDARs, indicating that rapid antidepressants alone may not be an effective treatment for people with comorbid FXS and MDD.

Screening the Molecular Framework Underlying Local Dendritic mRNA Translation.

In the last decade, bioinformatic analyses of high-throughput proteomics and transcriptomics data have enabled researchers to gain insight into the molecular networks that may underlie lasting changes in synaptic efficacy. Development and utilization of these techniques have advanced the field of learning and memory significantly. It is now possible to move from the study of activity-dependent changes of a single protein to modeling entire network changes that require local protein synthesis. This data revolution has necessitated the development of alternative computational and statistical techniques to analyze and understand the patterns contained within. Thus, the focus of this review is to provide a synopsis of the journey and evolution toward big data techniques to address still unanswered questions regarding how synapses are modified to strengthen neuronal circuits. We first review the seminal studies that demonstrated the pivotal role played by local mRNA translation as the mechanism underlying the enhancement of enduring synaptic activity. In the interest of those who are new to the field, we provide a brief overview of molecular biology and biochemical techniques utilized for sample preparation to identify locally translated proteins using RNA sequencing and proteomics, as well as the computational approaches used to analyze these data. While many mRNAs have been identified, few have been shown to be locally synthesized. To this end, we review techniques currently being utilized to visualize new protein synthesis, a task that has proven to be the most difficult aspect of the field. Finally, we provide examples of future applications to test the physiological relevance of locally synthesized proteins identified by big data approaches.