Characterization of a new selective glucocorticoid receptor modulator with anorexigenic activity.

Obesity, a worldwide epidemic, leads to various metabolic disorders threatening human health. In response to stress or fasting, glucocorticoid (GC) levels are elevated to promote food intake. This involves GC-induced expression of the orexigenic neuropeptides in agouti-related protein (AgRP) neurons of the hypothalamic arcuate nucleus (ARC) via the GC receptor (GR). Here, we report a selective GR modulator (SGRM) that suppresses GR-induced transcription of genes with non-classical glucocorticoid response elements (GREs) such as Agrp-GRE, but not with classical GREs, and via this way may serve as a novel anti-obesity agent. We have identified a novel SGRM, 2-O-trans-p-coumaroylalphitolic acid (Zj7), a triterpenoid extracted from the Ziziphus jujube plant, that selectively suppresses GR transcriptional activity in Agrp-GRE without affecting classical GREs. Zj7 reduces the expression of orexigenic genes in the ARC and exerts a significant anorexigenic effect with weight loss in both high fat diet-induced obese and genetically obese db/db mouse models. Transcriptome analysis showed that Zj7 represses the expression of a group of orexigenic genes including Agrp and Npy induced by the synthetic GR ligand dexamethasone (Dex) in the hypothalamus. Taken together, Zj7, as a selective GR modulator, showed beneficial metabolic activities, in part by suppressing GR activity in non-classical GREs in orexigenic genes. This study demonstrates that a potential anorexigenic molecule may allow GRE-specific inhibition of GR transcriptional activity, which is a promising approach for the treatment of metabolic disorders.

Unified framework for brain connectivity-based biomarkers in neurodegenerative disorders.

Background: Brain connectivity is useful for deciphering complex brain dynamics controlling interregional communication. Identifying specific brain phenomena based on brain connectivity and quantifying their levels can help explain or diagnose neurodegenerative disorders. Objective: This study aimed to establish a unified framework to identify brain connectivity-based biomarkers associated with disease progression and summarize them into a single numerical value, with consideration for connectivity-specific structural attributes. Methods: This study established a framework that unifies the processes of identifying a brain connectivity-based biomarker and mapping its abnormality level into a single numerical value, called a biomarker abnormality summarized from the identified connectivity (BASIC) score. A connectivity-based biomarker was extracted in the form of a connected component associated with disease progression. BASIC scores were constructed to maximize Kendall's rank correlation with the disease, considering the spatial autocorrelation between adjacent edges. Using functional connectivity networks, we validated the BASIC scores in various scenarios. Results: Our proposed framework was successfully applied to construct connectivity-based biomarker scores associated with disease progression, characterized by two, three, and five stages of Alzheimer's disease, and reflected the continuity of brain alterations as the diseases advanced. The BASIC scores were not only sensitive to disease progression, but also specific to the trajectory of a particular disease. Moreover, this framework can be utilized when disease stages are measured on continuous scales, resulting in a notable prediction performance when applied to the prediction of the disease. Conclusion: Our unified framework provides a method to identify brain connectivity-based biomarkers and continuity-reflecting BASIC scores that are sensitive and specific to disease progression.

On the reliability of deep learning-based classification for Alzheimer's disease: Multi-cohorts, multi-vendors, multi-protocols, and head-to-head validation.

Structural changes in the brain due to Alzheimer's disease dementia (ADD) can be observed through brain T1-weighted magnetic resonance imaging (MRI) images. Many ADD diagnostic studies using brain MRI images have been conducted with machine-learning and deep-learning models. Although reliability is a key in clinical application and applicability of low-resolution MRI (LRMRI) is a key to broad clinical application, both are not sufficiently studied in the deep-learning area. In this study, we developed a 2-dimensional convolutional neural network-based classification model by adopting several methods, such as using instance normalization layer, Mixup, and sharpness aware minimization. To train the model, MRI images from 2,765 cognitively normal individuals and 1,192 patients with ADD from the Samsung medical center cohort were exploited. To assess the reliability of our classification model, we designed external validation in multiple scenarios: (1) multi-cohort validation using four additional cohort datasets including more than 30 different centers in multiple countries, (2) multi-vendor validation using three different MRI vendor subgroups, (3) LRMRI image validation, and finally, (4) head-to-head validation using ten pairs of MRI images from ten individual subjects scanned in two different centers. For multi-cohort validation, we used the MRI images from 739 subjects from the Alzheimer's Disease Neuroimaging Initiative cohort, 125 subjects from the Dementia Platform of Korea cohort, 234 subjects from the Premier cohort, and 139 subjects from the Gachon University Gil Medical Center. We further assessed classification performance across different vendors and protocols for each dataset. We achieved a mean AUC and classification accuracy of 0.9868 and 0.9482 in 5-fold cross-validation. In external validation, we obtained a comparable AUC of 0.9396 and classification accuracy of 0.8757 to other cross-validation studies in the ADNI cohorts. Furthermore, we observed the possibility of broad clinical application through LRMRI image validation by achieving a mean AUC and classification accuracy of 0.9404 and 0.8765 at cross-validation and AUC and classification accuracy of 0.8749 and 0.8281 at the ADNI cohort external validation.

Asymmetric Total Synthesis of Oxazolomycins B and C.

Efforts towards the first total synthesis of (-)-oxazolomycin B and (+)-oxazolomycin C from the intermediate of our previous synthesis of (+)-neoxazolomycin are reported. The syntheses were achieved in a longest linear sequence of 25 steps from the amino acid serine in 3.6 and 2.7 % overall yields, respectively. The efficiency of our approach is derived from silyl triflate-mediated reductive oxazolidine ring-opening and Fürstner's Ru-catalyzed hydrosilylation and protodesilylation reactions. The obtained spectra and optical rotations were in good agreement with those of natural products, thus confirming the structures.

Asymmetric Total Synthesis of Inthomycins A, B, and C.

Herein, we report the asymmetric total syntheses of inthomycin antibiotics containing a methylene-interrupted oxazolyl-triene motif. Utilizing the α,ß-unsaturated aldehyde as a common intermediate, all three inthomycins A-C were divergently synthesized. The asymmetric ynone reduction provided an R-configured secondary alcohol as in the natural products with high enantioselectivity. The geometrically different triene units for each inthomycin were stereoselectively established via methyl cuprate conjugate addition, isomerization of the α,ß-unsaturated aldehyde intermediate, and stereoretentive cross-coupling reactions.

Development of Oxadiazole-Based ODZ10117 as a Small-Molecule Inhibitor of STAT3 for Targeted Cancer Therapy.

Persistently activated STAT3 is a promising target for a new class of anticancer drug development and cancer therapy, as it is associated with tumor initiation, progression, malignancy, drug resistance, cancer stem cell properties, and recurrence. Here, we discovered 3-(2,4-dichloro-phenoxymethyl)-5-trichloromethyl-[1,2,4]oxadiazole (ODZ10117) as a small-molecule inhibitor of STAT3 to be used in STAT3-targeted cancer therapy. ODZ10117 targeted the SH2 domain of STAT3 regardless of other STAT family proteins and upstream regulators of STAT3, leading to inhibition of the tyrosine phosphorylation, dimerization, nuclear translocation, and transcriptional activity of STAT3. The inhibitory effect of ODZ10117 on STAT3 was stronger than the known STAT3 inhibitors such as S3I-201, STA-21, and nifuroxazide. ODZ10117 suppressed the migration and invasion, induced apoptosis, reduced tumor growth and lung metastasis, and extended the survival rate in both in vitro and in vivo models of breast cancer. Overall, we demonstrated that ODZ10117 is a novel STAT3 inhibitor and may be a promising agent for the development of anticancer drugs.

Asymmetric Total Synthesis of (+)-Neooxazolomycin Using a Chirality-Transfer Strategy.

The total synthesis of (+)-neooxazolomycin was achieved from the amino-acid d-serine. The efficiency of this approach is derived from the use of principles of memory of chirality and dynamic kinetic resolution in the intramolecular aldol reaction of a serine derivative to build the densely functionalized lactam framework and to install three contiguous stereocenters. The key intermediate was readily elaborated to the target natural product.