The innate immunity protein IFITM3 modulates γ-secretase in Alzheimer's disease.

Innate immunity is associated with Alzheimer's disease1, but the influence of immune activation on the production of amyloid-ß is unknown2,3. Here we identify interferon-induced transmembrane protein 3 (IFITM3) as a γ-secretase modulatory protein, and establish a mechanism by which inflammation affects the generation of amyloid-ß. Inflammatory cytokines induce the expression of IFITM3 in neurons and astrocytes, which binds to γ-secretase and upregulates its activity, thereby increasing the production of amyloid-ß. The expression of IFITM3 is increased with ageing and in mouse models that express familial Alzheimer's disease genes. Furthermore, knockout of IFITM3 reduces γ-secretase activity and the formation of amyloid plaques in a transgenic mouse model (5xFAD) of early amyloid deposition. IFITM3 protein is upregulated in tissue samples from a subset of patients with late-onset Alzheimer's disease that exhibit higher γ-secretase activity. The amount of IFITM3 in the γ-secretase complex has a strong and positive correlation with γ-secretase activity in samples from patients with late-onset Alzheimer's disease. These findings reveal a mechanism in which γ-secretase is modulated by neuroinflammation via IFITM3 and the risk of Alzheimer's disease is thereby increased.

Development of a Small Molecule Downmodulator for the Transcription Factor Brachyury.

Brachyury is an oncogenic transcription factor whose overexpression drives chordoma growth. The downmodulation of brachyury in chordoma cells has demonstrated therapeutic potential, however, as a transcription factor it is classically deemed "undruggable". Given that direct pharmacological intervention against brachyury has proven difficult, attempts at intervention have instead targeted upstream kinases. Recently, afatinib, an FDA-approved kinase inhibitor, has been shown to modulate brachyury levels in multiple chordoma cell lines. Herein, we use afatinib as a lead to undertake a structure-based drug design approach, aided by mass-spectrometry and X-ray crystallography, to develop DHC-156, a small molecule that more selectively binds brachyury and downmodulates it as potently as afatinib. We eliminated kinase-inhibition from this novel scaffold while demonstrating that DHC-156 induces the post-translational downmodulation of brachyury that results in an irreversible impairment of chordoma tumor cell growth. In doing so, we demonstrate the feasibility of direct brachyury modulation, which may further be developed into more potent tool compounds and therapies.

γ-Secretase inhibitors and modulators: Mechanistic insights into the function and regulation of γ-Secretase.

Over two decades, γ-secretase has been the target for extensive therapeutic development due to its pivotal role in pathogenesis of Alzheimer's disease and cancer. However, it has proven to be a challenging task owing to its large set of substrates and our limited understanding of the enzyme's structural and mechanistic features. The scientific community is taking bigger strides towards solving this puzzle with recent advancement in techniques like cryogenic electron microscopy (cryo-EM) and photo-affinity labelling (PAL). This review highlights the significance of the PAL technique with multiple examples of photo-probes developed from γ-secretase inhibitors and modulators. The binding of these probes into active and/or allosteric sites of the enzyme has provided crucial information on the γ-secretase complex and improved our mechanistic understanding of this protease. Combining the knowledge of function and regulation of γ-secretase will be a decisive factor in developing novel γ-secretase modulators and biological therapeutics.

Synthesis of indazoles and azaindazoles by intramolecular aerobic oxidative C-N coupling under transition-metal-free conditions.

A transition-metal-free oxidative C-N coupling method has been developed for the synthesis of 1H-azaindazoles and 1H-indazoles from easily accessible hydrazones. The procedure uses TEMPO, a basic additive, and dioxygen gas as the terminal oxidant. This reaction demonstrates better reactivity, functional group tolerance, and broader scope than comparable metal catalyzed reactions.

GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition.

PURPOSE: Body composition analysis (BCA) of the body torso plays a vital role in the study of physical health and pathology and provides biomarkers that facilitate the diagnosis and treatment of many diseases, such as type 2 diabetes mellitus, cardiovascular disease, obstructive sleep apnea, and osteoarthritis. In this work, we propose a body composition tissue segmentation method that can automatically delineate those key tissues, including subcutaneous adipose tissue, skeleton, skeletal muscle tissue, and visceral adipose tissue, on positron emission tomography/computed tomography scans of the body torso. METHODS: To provide appropriate and precise semantic and spatial information that is strongly related to body composition tissues for the deep neural network, first we introduce a new concept of the body area and integrate it into our proposed segmentation network called Geographical Attention Network (GA-Net). The body areas are defined following anatomical principles such that the whole body torso region is partitioned into three non-overlapping body areas. Each body composition tissue of interest is fully contained in exactly one specific minimal body area. Secondly, the proposed GA-Net has a novel dual-decoder schema that is composed of a tissue decoder and an area decoder. The tissue decoder segments the body composition tissues, while the area decoder segments the body areas as an auxiliary task. The features of body areas and body composition tissues are fused through a soft attention mechanism to gain geographical attention relevant to the body tissues. Thirdly, we propose a body composition tissue annotation approach that takes the body area labels as the region of interest, which significantly improves the reproducibility, precision, and efficiency of delineating body composition tissues. RESULTS: Our evaluations on 50 low-dose unenhanced CT images indicate that GA-Net outperforms other architectures statistically significantly based on the Dice metric. GA-Net also shows improvements for the 95% Hausdorff Distance metric in most comparisons. Notably, GA-Net exhibits more sensitivity to subtle boundary information and produces more reliable and robust predictions for such structures, which are the most challenging parts to manually mend in practice, with potentially significant time-savings in the post hoc correction of these subtle boundary placement errors. Due to the prior knowledge provided from body areas, GA-Net achieves competitive performance with less training data. Our extension of the dual-decoder schema to TransUNet and 3D U-Net demonstrates that the new schema significantly improves the performance of these classical neural networks as well. Heatmaps obtained from attention gate layers further illustrate the geographical guidance function of body areas for identifying body tissues. CONCLUSIONS: (i) Prior anatomic knowledge supplied in the form of appropriately designed anatomic container objects significantly improves the segmentation of bodily tissues. (ii) Of particular note are the improvements achieved in the delineation of subtle boundary features which otherwise would take much effort for manual correction. (iii) The method can be easily extended to existing networks to improve their accuracy for this application.

A Deep Learning Based Geographic Attention Model for Body Composition Tissue Segmentation.

Measurement of body composition, including multiple types of adipose tissue, skeletal tissue, and skeletal muscle, on computed tomography (CT) images is practical given the powerful anatomical structure visualization ability of CT, and is useful for clinical and research applications related to health care and underlying pathology. In recent years, deep learning-based methods have contributed significantly to the development of automatic body composition analysis (BCA). However, the unsatisfactory segmentation performance for indistinguishable boundaries of multiple body composition tissues and the need for large-scale datasets for training deep neural networks still need to be addressed. This paper proposes a deep learning-based approach, called Geographic Attention Network (GA-Net), for body composition tissue segmentation on body torso positron emission tomography/computed tomography (PET/CT) images which leverages the body area information. The representation ability of GA-Net is significantly enhanced with the body area information as it strongly correlates with the target body composition tissue. This method achieves precise segmentation performance for multiple body composition tissues, especially for boundaries that are hard to distinguish, and effectively reduces the data requirements for training the network. We evaluate the proposed model on a dataset that includes 50 body torso PET/CT scans for segmenting 4 key bodily tissues - subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), skeletal muscle tissue (SMT), and skeleton (Sk). Experiments show that our proposed method increases segmentation accuracy, especially with a limited training dataset, by providing geographic information of target body composition tissues.

Imaging of Cancer γ-Secretase Activity Using an Inhibitor-Based PET Probe.

PURPOSE: Abnormal Notch signaling promotes cancer cell growth and tumor progression in various cancers. Targeting γ-secretase, a pivotal regulator in the Notch pathway, has yielded numerous γ-secretase inhibitors (GSIs) for clinical investigation in the last 2 decades. However, GSIs have demonstrated minimal success in clinical trials in part due to the lack of specific and precise tools to assess γ-secretase activity and its inhibition in vivo. EXPERIMENTAL DESIGN: We designed an imaging probe based on GSI Semagacestat structure and synthesized the radioiodine-labeled analogues [131I]- or [124I]-PN67 from corresponding trimethyl-tin precursors. Both membrane- and cell-based ligand-binding assays were performed using [131I]-PN67 to determine the binding affinity and specificity for γ-secretase in vitro. Moreover, we evaluated [124I]-PN67 by PET imaging in mammary tumor and glioblastoma mouse models. RESULTS: The probe was synthesized through iodo-destannylation using chloramine-T as an oxidant with a high labeling yield and efficiency. In vitro binding results demonstrate the high specificity of this probe and its ability for target replacement study by clinical GSIs. PET imaging studies demonstrated a significant (P < 0.05) increased in the uptake of [124I]-PN67 in tumors versus blocking or sham control groups across multiple mouse models, including 4T1 allograft, MMTV-PyMT breast cancer, and U87 glioblastoma allograft. Ex vivo biodistribution and autoradiography corroborate these results, indicating γ-secretase specific tumor accumulation of [124I]-PN67. CONCLUSIONS: [124I]-PN67 is a novel PET imaging agent that enables assessment of γ-secretase activity and target engagement of clinical GSIs.