Everolimus exerts anticancer effects through inhibiting the interaction of matrix metalloproteinase-7 with syndecan-2 in colon cancer cells.

Previous work showed that matrix metalloproteinase-7 (MMP-7) regulates colon cancer activities through an interaction with syndecan-2 (SDC-2) and SDC-2-derived peptide that disrupts this interaction and exhibits anticancer activity in colon cancer. Here, to identify potential anticancer agents, a library of 1,379 Food and Drug Administration (FDA)-approved drugs that interact with the MMP-7 prodomain were virtually screened by protein-ligand docking score analysis using the GalaxyDock3 program. Among five candidates selected based on their structures and total energy values for interacting with the MMP-7 prodomain, the known mechanistic target of rapamycin kinase (mTOR) inhibitor, everolimus, showed the highest binding affinity and the strongest ability to disrupt the interaction of the MMP-7 prodomain with the SDC-2 extracellular domain in vitro. Everolimus treatment of the HCT116 human colon cancer cell line did not affect the mRNA expression levels of MMP-7 and SDC-2 but reduced the adhesion of cells to MMP-7 prodomain-coated plates and the cell-surface localization of MMP-7. Thus, everolimus appears to inhibit the interaction between MMP-7 and SDC-2. Everolimus treatment of HCT116 cells also reduced their gelatin-degradation activity and anticancer activities, including colony formation. Interestingly, cells treated with sirolimus, another mTOR inhibitor, triggered less gelatin-degradation activity, suggesting that this inhibitory effect of everolimus was not due to inhibition of the mTOR pathway. Consistently, everolimus inhibited the colony-forming ability of mTOR-resistant HT29 cells. Together, these data suggest that, in addition to inhibiting mTOR signaling, everolimus exerts anticancer activity by interfering with the interaction of MMP-7 and SDC-2, and could be a useful therapeutic anticancer drug for colon cancer.NEW & NOTEWORTHY The utility of cancer therapeutics targeting the proteolytic activities of MMPs is limited because MMPs are widely distributed throughout the body and involved in many different aspects of cell functions. This work specifically targets the activation of MMP-7 through its interaction with syndecan-2. Notably, everolimus, a known mTOR inhibitor, blocked this interaction, demonstrating a novel role for everolimus in inhibiting mTOR signaling and impairing the interaction of MMP-7 with syndecan-2 in colon cancer.

Evaluation of retrieval accuracy and visual similarity in content-based image retrieval of chest CT for obstructive lung disease.

The aim of our study was to assess the performance of content-based image retrieval (CBIR) for similar chest computed tomography (CT) in obstructive lung disease. This retrospective study included patients with obstructive lung disease who underwent volumetric chest CT scans. The CBIR database included 600 chest CT scans from 541 patients. To assess the system performance, follow-up chest CT scans of 50 patients were evaluated as query cases, which showed the stability of the CT findings between baseline and follow-up chest CT, as confirmed by thoracic radiologists. The CBIR system retrieved the top five similar CT scans for each query case from the database by quantifying and comparing emphysema extent and size, airway wall thickness, and peripheral pulmonary vasculatures in descending order from the database. The rates of retrieval of the same pairs of query CT scans in the top 1-5 retrievals were assessed. Two expert chest radiologists evaluated the visual similarities between the query and retrieved CT scans using a five-point scale grading system. The rates of retrieving the same pairs of query CTs were 60.0% (30/50) and 68.0% (34/50) for top-three and top-five retrievals. Radiologists rated 64.8% (95% confidence interval 58.8-70.4) of the retrieved CT scans with a visual similarity score of four or five and at least one case scored five points in 74% (74/100) of all query cases. The proposed CBIR system for obstructive lung disease integrating quantitative CT measures demonstrated potential for retrieving chest CT scans with similar imaging phenotypes. Further refinement and validation in this field would be valuable.

Machine learning-based clinical decision support system for treatment recommendation and overall survival prediction of hepatocellular carcinoma: a multi-center study.

The treatment decisions for patients with hepatocellular carcinoma are determined by a wide range of factors, and there is a significant difference between the recommendations of widely used staging systems and the actual initial treatment choices. Herein, we propose a machine learning-based clinical decision support system suitable for use in multi-center settings. We collected data from nine institutions in South Korea for training and validation datasets. The internal and external datasets included 935 and 1750 patients, respectively. We developed a model with 20 clinical variables consisting of two stages: the first stage which recommends initial treatment using an ensemble voting machine, and the second stage, which predicts post-treatment survival using a random survival forest algorithm. We derived the first and second treatment options from the results with the highest and the second-highest probabilities given by the ensemble model and predicted their post-treatment survival. When only the first treatment option was accepted, the mean accuracy of treatment recommendation in the internal and external datasets was 67.27% and 55.34%, respectively. The accuracy increased to 87.27% and 86.06%, respectively, when the second option was included as the correct answer. Harrell's C index, integrated time-dependent AUC curve, and integrated Brier score of survival prediction in the internal and external datasets were 0.8381 and 0.7767, 91.89 and 86.48, 0.12, and 0.14, respectively. The proposed system can assist physicians by providing data-driven predictions for reference from other larger institutions or other physicians within the same institution when making treatment decisions.

Deep Learning for Automated Triaging of Stable Chest Radiographs in a Follow-up Setting.

Background Most artificial intelligence algorithms that interpret chest radiographs are restricted to an image from a single time point. However, in clinical practice, multiple radiographs are used for longitudinal follow-up, especially in intensive care units (ICUs). Purpose To develop and validate a deep learning algorithm using thoracic cage registration and subtraction to triage pairs of chest radiographs showing no change by using longitudinal follow-up data. Materials and Methods A deep learning algorithm was retrospectively developed using baseline and follow-up chest radiographs in adults from January 2011 to December 2018 at a tertiary referral hospital. Two thoracic radiologists reviewed randomly selected pairs of "change" and "no change" images to establish the ground truth, including normal or abnormal status. Algorithm performance was evaluated using area under the receiver operating characteristic curve (AUC) analysis in a validation set and temporally separated internal test sets (January 2019 to August 2021) from the emergency department (ED) and ICU. Threshold calibration for the test sets was conducted, and performance with 40% and 60% triage thresholds was assessed. Results This study included 3 304 996 chest radiographs in 329 036 patients (mean age, 59 years ± 14 [SD]; 170 433 male patients). The training set included 550 779 pairs of radiographs. The validation set included 1620 pairs (810 no change, 810 change). The test sets included 533 pairs (ED; 265 no change, 268 change) and 600 pairs (ICU; 310 no change, 290 change). The algorithm had AUCs of 0.77 (validation), 0.80 (ED), and 0.80 (ICU). With a 40% triage threshold, specificity was 88.4% (237 of 268 pairs) and 90.0% (261 of 290 pairs) in the ED and ICU, respectively. With a 60% triage threshold, specificity was 79.9% (214 of 268 pairs) and 79.3% (230 of 290 pairs) in the ED and ICU, respectively. For urgent findings (consolidation, pleural effusion, pneumothorax), specificity was 78.6%-100% (ED) and 85.5%-93.9% (ICU) with a 40% triage threshold. Conclusion The deep learning algorithm could triage pairs of chest radiographs showing no change while detecting urgent interval changes during longitudinal follow-up. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Czum in this issue.

Technical Feasibility of Quantitative Measurement of Various Degrees of Small Bowel Motility Using Cine Magnetic Resonance Imaging.

OBJECTIVE: Cine magnetic resonance imaging (MRI) has emerged as a noninvasive method to quantitatively assess bowel motility. However, its accuracy in measuring various degrees of small bowel motility has not been extensively evaluated. We aimed to draw a quantitative small bowel motility score from cine MRI and evaluate its performance in a population with varying degrees of small bowel motility. MATERIALS AND METHODS: A total of 174 participants (28.5 ± 7.6 years; 135 males) underwent a 22-second-long cine MRI sequence (2-dimensional balanced turbo-field echo; 0.5 seconds per image) approximately 5 minutes after being intravenously administered 10 mg of scopolamine-N-butyl bromide to deliberately create diverse degrees of small bowel motility. In a manually segmented area of the small bowel, motility was automatically quantified using a nonrigid registration and calculated as a quantitative motility score. The mean value (MV) of motility grades visually assessed by two radiologists was used as a reference standard. The quantitative motility score's correlation (Spearman's ρ) with the reference standard and performance (area under the receiver operating characteristics curve [AUROC], sensitivity, and specificity) for diagnosing adynamic small bowel (MV of 1) were evaluated. RESULTS: For the MV of the quantitative motility scores at grades 1, 1.5, 2, 2.5, and 3, the mean ± standard deviation values were 0.019 ± 0.003, 0.027 ± 0.010, 0.033 ± 0.008, 0.032 ± 0.009, and 0.043 ± 0.013, respectively. There was a significant positive correlation between the quantitative motility score and the MV (ρ = 0.531, P < 0.001). The AUROC value for diagnosing a MV of 1 (i.e., adynamic small bowel) was 0.953 (95% confidence interval, 0.923-0.984). Moreover, the optimal cutoff for the quantitative motility score was 0.024, with a sensitivity of 100% (15/15) and specificity of 89.9% (143/159). CONCLUSION: The quantitative motility score calculated from a cine MRI enables diagnosis of an adynamic small bowel, and potentially discerns various degrees of bowel motility.

MuSiC-ViT: A multi-task Siamese convolutional vision transformer for differentiating change from no-change in follow-up chest radiographs.

A major responsibility of radiologists in routine clinical practice is to read follow-up chest radiographs (CXRs) to identify changes in a patient's condition. Diagnosing meaningful changes in follow-up CXRs is challenging because radiologists must differentiate disease changes from natural or benign variations. Here, we suggest using a multi-task Siamese convolutional vision transformer (MuSiC-ViT) with an anatomy-matching module (AMM) to mimic the radiologist's cognitive process for differentiating baseline change from no-change. MuSiC-ViT uses the convolutional neural networks (CNNs) meet vision transformers model that combines CNN and transformer architecture. It has three major components: a Siamese network architecture, an AMM, and multi-task learning. Because the input is a pair of CXRs, a Siamese network was adopted for the encoder. The AMM is an attention module that focuses on related regions in the CXR pairs. To mimic a radiologist's cognitive process, MuSiC-ViT was trained using multi-task learning, normal/abnormal and change/no-change classification, and anatomy-matching. Among 406 K CXRs studied, 88 K change and 115 K no-change pairs were acquired for the training dataset. The internal validation dataset consisted of 1,620 pairs. To demonstrate the robustness of MuSiC-ViT, we verified the results with two other validation datasets. MuSiC-ViT respectively achieved accuracies and area under the receiver operating characteristic curves of 0.728 and 0.797 on the internal validation dataset, 0.614 and 0.784 on the first external validation dataset, and 0.745 and 0.858 on a second temporally separated validation dataset. All code is available at https://github.com/chokyungjin/MuSiC-ViT.

Automated detection of intracranial aneurysms using skeleton-based 3D patches, semantic segmentation, and auxiliary classification for overcoming data imbalance in brain TOF-MRA.

Accurate and reliable detection of intracranial aneurysms is vital for subsequent treatment to prevent bleeding. However, the detection of intracranial aneurysms can be time-consuming and even challenging, and there is great variability among experts, especially in the case of small aneurysms. This study aimed to detect intracranial aneurysms accurately using a convolutional neural network (CNN) with 3D time-of-flight magnetic resonance angiography (TOF-MRA). A total of 154 3D TOF-MRA datasets with intracranial aneurysms were acquired, and the gold standards were manually drawn by neuroradiologists. We also obtained 113 subjects from a public dataset for external validation. These angiograms were pre-processed by using skull-stripping, signal intensity normalization, and N4 bias correction. The 3D patches along the vessel skeleton from MRA were extracted. Values of the ratio between the aneurysmal and the normal patches ranged from 1:1 to 1:5. The semantic segmentation on intracranial aneurysms was trained using a 3D U-Net with an auxiliary classifier to overcome the imbalance in patches. The proposed method achieved an accuracy of 0.910 in internal validation and external validation accuracy of 0.883 with a 2:1 ratio of normal to aneurysmal patches. This multi-task learning method showed that the aneurysm segmentation performance was sufficient to be helpful in an actual clinical setting.

Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease.

OBJECTIVE: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software. MATERIALS AND METHODS: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard- or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system. RESULTS: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 ± 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. CONCLUSION: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD.

Deep Learning-based Approach to Predict Pulmonary Function at Chest CT.

Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services. Materials and Methods In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV1) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV1 percent predicted (hereafter, FVC% and FEV1%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV1/FVC) less than 70% were used to classify participants at high risk. Results A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV1. For the prediction of the respiratory high-risk group, FVC%, FEV1%, and FEV1/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively. Conclusion A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance. © RSNA, 2023 Supplemental material is available for this article.

Interstitial lung abnormalities (ILA) on routine chest CT: Comparison of radiologists' visual evaluation and automated quantification.

PURPOSE: We aimed to evaluate the performance of a fully automated quantitative software in detecting interstitial lung abnormalities (ILA) according to the Fleischner Society guidelines on routine chest CT compared with radiologists' visual analysis. METHOD: This retrospective single-centre study included participants with ILA findings and 1:2 matched controls who underwent routine chest CT using various CT protocols for health screening. Two thoracic radiologists independently reviewed the CT images using the Fleischner Society guidelines. We developed a fully automated quantitative tool for detecting ILA by modifying deep learning-based quantification of interstitial lung disease and evaluated its performance using the radiologists' consensus for ILA as a reference standard. RESULTS: A total of 336 participants (mean age, 70.5 ± 6.1 years; M:F = 282:54) were included. Inter-reader agreements were substantial for the presence of ILA (weighted κ, 0.74) and fair for its subtypes (weighted κ, 0.38). The quantification system for identifying ILA using a threshold of 5 % in at least one zone showed 67.6 % sensitivity, 93.3 % specificity, and 90.5 % accuracy. Eight of 20 (40 %) false positives identified by the system were underestimated by readers for ILA extent. Contrast-enhancement in a certain vendor and suboptimal inspiration caused a true false-positive on the system (all P < 0.05). The best cut-off value of abnormality extent detecting ILA on the system was 3.6 % (sensitivity, 84.8 %; specificity 92.4 %). CONCLUSIONS: Inter-reader agreement was substantial for ILA but only fair for its subtypes. Applying an automated quantification system in routine clinical practice may aid the objective identification of ILA.

Development and Validation of a Model Using Radiomics Features from an Apparent Diffusion Coefficient Map to Diagnose Local Tumor Recurrence in Patients Treated for Head and Neck Squamous Cell Carcinoma.

OBJECTIVE: To develop and validate a model using radiomics features from apparent diffusion coefficient (ADC) map to diagnose local tumor recurrence in head and neck squamous cell carcinoma (HNSCC). MATERIALS AND METHODS: This retrospective study included 285 patients (mean age ± standard deviation, 62 ± 12 years; 220 male, 77.2%), including 215 for training (n = 161) and internal validation (n = 54) and 70 others for external validation, with newly developed contrast-enhancing lesions at the primary cancer site on the surveillance MRI following definitive treatment of HNSCC between January 2014 and October 2019. Of the 215 and 70 patients, 127 and 34, respectively, had local tumor recurrence. Radiomics models using radiomics scores were created separately for T2-weighted imaging (T2WI), contrast-enhanced T1-weighted imaging (CE-T1WI), and ADC maps using non-zero coefficients from the least absolute shrinkage and selection operator in the training set. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of each radiomics score and known clinical parameter (age, sex, and clinical stage) in the internal and external validation sets. RESULTS: Five radiomics features from T2WI, six from CE-T1WI, and nine from ADC maps were selected and used to develop the respective radiomics models. The area under ROC curve (AUROC) of ADC radiomics score was 0.76 (95% confidence interval [CI], 0.62-0.89) and 0.77 (95% CI, 0.65-0.88) in the internal and external validation sets, respectively. These were significantly higher than the AUROC values of T2WI (0.53 [95% CI, 0.40-0.67], p = 0.006), CE-T1WI (0.53 [95% CI, 0.40-0.67], p = 0.012), and clinical parameters (0.53 [95% CI, 0.39-0.67], p = 0.021) in the external validation set. CONCLUSION: The radiomics model using ADC maps exhibited higher diagnostic performance than those of the radiomics models using T2WI or CE-T1WI and clinical parameters in the diagnosis of local tumor recurrence in HNSCC following definitive treatment.

Artificial Intelligence in Lung Imaging.

Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.

Multi-domain CT translation by a routable translation network.

Objective.To unify the style of computed tomography (CT) images from multiple sources, we propose a novel multi-domain image translation network to convert CT images from different scan parameters and manufacturers by simply changing a routing vector.Approach.Unlike the existing multi-domain translation techniques, our method is based on a shared encoder and a routable decoder architecture to maximize the expressivity and conditioning power of the network.Main results.Experimental results show that the proposed CT image conversion can minimize the variation of image characteristics caused by imaging parameters, reconstruction algorithms, and hardware designs. Quantitative results and clinical evaluation from radiologists also show that our method can provide accurate translation results.Significance.Quantitative evaluation of CT images from multi-site or longitudinal studies has been a difficult problem due to the image variation depending on CT scan parameters and manufacturers. The proposed method can be utilized to address this for the quantitative analysis of multi-domain CT images.

Structure of the human galanin receptor 2 bound to galanin and Gq reveals the basis of ligand specificity and how binding affects the G-protein interface.

Galanin is a neuropeptide expressed in the central and peripheral nervous systems, where it regulates various processes including neuroendocrine release, cognition, and nerve regeneration. Three G-protein coupled receptors (GPCRs) for galanin have been discovered, which is the focus of efforts to treat diseases including Alzheimer's disease, anxiety, and addiction. To understand the basis of the ligand preferences of the receptors and to assist structure-based drug design, we used cryo-electron microscopy (cryo-EM) to solve the molecular structure of GALR2 bound to galanin and a cognate heterotrimeric G-protein, providing a molecular view of the neuropeptide binding site. Mutant proteins were assayed to help reveal the basis of ligand specificity, and structural comparison between the activated GALR2 and inactive hß2AR was used to relate galanin binding to the movements of transmembrane (TM) helices and the G-protein interface.

The influence of multiple frailty profiles on institutionalization and all-cause mortality in community-living older adults.

BACKGROUND: Frailty in older adults is associated with adverse geriatric outcomes. Physical frailty is often accompanied by problems in the cognitive, psychological, and social domains. This study investigated the ability of physical frailty combined with other health domains to predict institutionalization and mortality. METHODS: A national sample of 9171 Koreans aged 65 years or older were surveyed at baseline in 2008 and 3 year follow-up. Those who were prefrail or frail according to the Fried criteria were conceived to have physical frailty. Psychological frailty, cognitive frailty, and social frailty were defined as having depressive symptoms, cognitive impairment, and social vulnerabilities, respectively, in addition to physical frailty. Using Cox proportional hazards and competing-risks regression, the risk of mortality and institutionalization by the number and profiles of different frailty domains was analysed. RESULTS: At baseline, the 9171 participants were aged 73.1 (±6.8) years on average (median: 72, range: 65 to 103), and 59.2% were women. Multidomain frailty was highly prevalent (49.3%), with 6.1% concurrently displaying frailty in all four domains (mixed frailty). The risk of negative health outcomes increased with frailty in a higher number of domains with a subhazard ratio (SHR) of 3.48 (95% confidence interval [CI]: 1.83, 6.62; P < 0.001) for institutionalization and a hazard ratio (HR) of 3.95 (95% CI: 2.62, 5.93; P < 0.001) for mortality among those presenting mixed frailty. Psychological frailty (depressive symptoms combined with physical frailty) was strongly predictive of institutionalization (SHR = 2.85; 95% CI: 1.45, 5.59; P = 0.002) and mortality (HR = 2.47; 95% CI: 1.61, 3.78; P < 0.001). When combined with physical frailty and either depressive symptoms or social vulnerabilities, cognitive impairment also exhibited a significantly elevated risk of negative events. Physical frailty alone was not a strong predictor of adverse events, especially for mortality (HR = 1.13; 95% CI: 0.77, 1.67; P = 0.53). CONCLUSIONS: Co-occurrence of physical frailty with other domains is common in late life. The presence of frailty in multiple domains raises the risk of adverse outcomes, with the effects varying by multidimensional profiles.

Substituted Syndecan-2-Derived Mimetic Peptides Show Improved Antitumor Activity over the Parent Syndecan-2-Derived Peptide.

We previously showed that a synthetic peptide (S2-P) corresponding to a portion of the human syndecan-2 (SDC2) sequence can bind to the pro-domain of matrix metalloproteinase-7 (MMP-7) to inhibit colon cancer activities. Since S2-P had a relatively weak binding affinity for the MMP-7 pro-domain, we herein modified the amino acid sequence of S2-P to improve the anticancer potential. On the basis of the interaction structure of S2-P and MMP-7, four peptides were generated by replacing amino acids near Tyr 51, which is critical for the interaction. The SDC2-mimetic peptides harboring an Ala-to-Asp substitution at the C-terminal side of Tyr 51 (S2-D) or with an Ala-to-Phe substitution at the N-terminal side of Tyr 51 and an Ala-to-Asp substitution at the C-terminal side of Tyr 51 (S2-FE) showed improved interaction affinities for the MMP-7 pro-domain. Compared to S2-P, S2-FE was better able to inhibit the SDC2-MMP-7 interaction, the cell surface localization of MMP-7, the gelatin degradation activity of MMP-7, and the cancer activities (cell migration, invasion, and colony-forming activity) of human HCT116 colon cancer cells in vitro. In vivo, S2-FE inhibited the primary tumor growth and lung metastasis of CT26 mouse colon cancer cells in a xenograft mouse model. Together, these data suggest that S2-FE could be useful therapeutic anticancer peptides for colon cancer.

Structural insights into the HBV receptor and bile acid transporter NTCP.

Around 250 million people are infected with hepatitis B virus (HBV) worldwide1, and 15 million may also carry the satellite virus hepatitis D virus (HDV), which confers even greater risk of severe liver disease2. The HBV receptor has been identified as sodium taurocholate co-transporting polypeptide (NTCP), which interacts directly with the first 48 amino acid residues of the N-myristoylated N-terminal preS1 domain of the viral large protein3. Despite the pressing need for therapeutic agents to counter HBV, the structure of NTCP remains unsolved. This 349-residue protein is closely related to human apical sodium-dependent bile acid transporter (ASBT), another member of the solute carrier family SLC10. Crystal structures have been reported of similar bile acid transporters from bacteria4,5, and these models are believed to resemble closely both NTCP and ASBT. Here we have used cryo-electron microscopy to solve the structure of NTCP bound to an antibody, clearly showing that the transporter has no equivalent of the first transmembrane helix found in other SLC10 proteins, and that the N terminus is exposed on the extracellular face. Comparison of our structure with those of related proteins indicates a common mechanism of bile acid transport, but the NTCP structure displays an additional pocket formed by residues that are known to interact with preS1, presenting new opportunities for structure-based drug design.

Cryo-EM structure of the human somatostatin receptor 2 complex with its agonist somatostatin delineates the ligand-binding specificity.

Somatostatin is a peptide hormone that regulates endocrine systems by binding to G-protein-coupled somatostatin receptors. Somatostatin receptor 2 (SSTR2) is a human somatostatin receptor and is highly implicated in hormone disorders, cancers, and neurological diseases. Here, we report the high-resolution cryo-EM structure of full-length human SSTR2 bound to the agonist somatostatin (SST-14) in complex with inhibitory G (Gi) proteins. Our structural and mutagenesis analyses show that seven transmembrane helices form a deep pocket for ligand binding and that SSTR2 recognizes the highly conserved Trp-Lys motif of SST-14 at the bottom of the pocket. Furthermore, our sequence analysis combined with AlphaFold modeled structures of other SSTR isoforms provide a structural basis for the mechanism by which SSTR family proteins specifically interact with their cognate ligands. This work provides the first glimpse into the molecular recognition mechanism of somatostatin receptors and a crucial resource to develop therapeutics targeting somatostatin receptors.

Crystal Structures of the Plant Phospholipase A1 Proteins Reveal a Unique Dimerization Domain.

Phospholipase is an enzyme that hydrolyzes various phospholipid substrates at specific ester bonds and plays important roles such as membrane remodeling, as digestive enzymes, and the regulation of cellular mechanism. Phospholipase proteins are divided into following the four major groups according to the ester bonds they cleave off: phospholipase A1 (PLA1), phospholipase A2 (PLA2), phospholipase C (PLC), and phospholipase D (PLD). Among the four phospholipase groups, PLA1 has been less studied than the other phospholipases. Here, we report the first molecular structures of plant PLA1s: AtDSEL and CaPLA1 derived from Arabidopsis thaliana and Capsicum annuum, respectively. AtDSEL and CaPLA1 are novel PLA1s in that they form homodimers since PLAs are generally in the form of a monomer. The dimerization domain at the C-terminal of the AtDSEL and CaPLA1 makes hydrophobic interactions between each monomer, respectively. The C-terminal domain is also present in PLA1s of other plants, but not in PLAs of mammals and fungi. An activity assay of AtDSEL toward various lipid substrates demonstrates that AtDSEL is specialized for the cleavage of sn-1 acyl chains. This report reveals a new domain that exists only in plant PLA1s and suggests that the domain is essential for homodimerization.

Genetic diversity and population structure of Mongolian regional horses with 14 microsatellite markers.

OBJECTIVE: This study aimed to identify the genetic diversity and population structure of Mongolian horse populations according to the province of residence (Khentii, KTP; Uvs, USP; Omnogovi and Dundgovi, GOP; Khovsgol, KGP) using 14 microsatellite (MS) markers. METHODS: A total of 269 whole blood samples were obtained from the four populations (KTP, USP, GOP, KGP) geographically distinct provinces. Multiplex polymerase chain reaction (PCR) was conducted using 14 MS markers (AHT4, ASB2, ASB17, ASB23, CA425, HMS1, HMS2, HMS3, HMS6, HMS7, HTG4, HTG6, HTG7, and VHL20), as recommended by the International Society for Animal Genetics. Capillary electrophoresis was conducted using the amplified PCR products, alleles were determined. Alleles were used for statistical analysis of genetic variability, Nei's DA genetic distance, principal coordinate analysis (PCoA), factorial corresponding analysis (FCA), and population structure. RESULTS: On average, the number of alleles, expected heterozygosity (HExp), observed heterozygosity (HObs), and polymorphic information content among all populations were 11.43, 0.772, 0.757, and 0.737, respectively. In the PCoA and FCA, GOP, and KGP were genetically distinct from other populations, and the KTP and USP showed a close relationship. The two clusters identified using Nei's DA genetic distance analysis and population structure highlighted the presence of structurally clear genetic separation. CONCLUSION: Overall, the results of this study suggest that genetic diversity between KTP and USP was low, and that between GOP and KGP was high. It is thought that these results will help in the effective preservation and improvement of Mongolian horses through genetic diversity analysis and phylogenetic relationships.