OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

MOTIVATION: Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes in bioactivity. In response, several DGL models have been developed to enhance ligand bioactivity prediction in the presence of ACs. Yet, there remains a largely unexplored opportunity within ACs for optimizing ligand bioactivity, making it an area ripe for further investigation. RESULTS: We present a novel approach to simultaneously predict and optimize ligand bioactivities through DGL and ACs (OLB-AC). OLB-AC possesses the capability to optimize ligand molecules located near ACs, providing a direct reference for optimizing ligand bioactivities with the matching of original ligands. To accomplish this, a novel attentive graph reconstruction neural network and ligand optimization scheme are proposed. Attentive graph reconstruction neural network reconstructs original ligands and optimizes them through adversarial representations derived from their bioactivity prediction process. Experimental results on nine drug targets reveal that out of the 667 molecules generated through OLB-AC optimization on datasets comprising 974 low-activity, noninhibitor, or highly toxic ligands, 49 are recognized as known highly active, inhibitor, or nontoxic ligands beyond the datasets' scope. The 27 out of 49 matched molecular pairs generated by OLB-AC reveal novel transformations not present in their training sets. The adversarial representations employed for ligand optimization originate from the gradients of bioactivity predictions. Therefore, we also assess OLB-AC's prediction accuracy across 33 different bioactivity datasets. Results show that OLB-AC achieves the best Pearson correlation coefficient (r2) on 27/33 datasets, with an average improvement of 7.2%-22.9% against the state-of-the-art bioactivity prediction methods. AVAILABILITY AND IMPLEMENTATION: The code and dataset developed in this work are available at github.com/Yueming-Yin/OLB-AC.

Hyaluronic acid-modified mesoporous silica nanoprobes for target identification of atherosclerosis.

Rupture of vulnerable plaque and secondary thrombosis caused by atherosclerosis are one of the main causes of acute cardiovascular and cerebrovascular events, and it is urgent to develop an in-situ, noninvasive, sensitive and targeted detection method at molecular level. We chose CD44, a specific receptor highly expressed on the surface of macrophages, as the target of the molecular probe, and modified the CD44 ligand HA onto the surface of Gd2O3@MSN, constructing the MRI imaging nanoprobe HA-Gd2O3@MSN for targeted recognition of atherosclerosis. The fundamental properties of HA-Gd2O3@MSN were initially investigated. The CCK-8, hemolysis, hematoxylin-eosin staining tests and blood biochemical assays confirmed that HA-Gd2O3@MSN possessed excellent biocompatibility. Laser confocal microscopy, cellular magnetic resonance imaging, flow cytometry and immunohistochemistry were used to verify that the nanoprobes had good targeting properties. The in vivo targeting performance of the nanoprobes was further validated by employing a rabbit atherosclerosis animal model. In summary, the synthesized HA-Gd2O3@MSN nanoprobes have excellent biocompatibility properties as well as good targeting properties. It could provide a new technical tool for early identification of atherosclerosis.

NMI promotes tumor progression and gemcitabine resistance in pancreatic cancer via STAT3-IFIT3 axis.

N-myc and STAT interactor (NMI) has been reported to interact with several transcription factors, including STATs family, c-Myc, N-Myc, and BRCA1, to indirectly affect transcription events and participate in multiple cellular processes. However, its function in pancreatic ductal adenocarcinoma (PDAC) has seldom been studied. In this study, we investigated the regulation of NMI on PDAC progression and uncovered the underlying molecular mechanisms. We found that NMI expression was significantly upregulated in PDAC and high NMI expression was related to a worse patient survival. Cell proliferation and migration assay, including cell viability, transwell assay, wound healing, and subcutaneous mouse model were utilized to confirm the function of NMI in PDAC progression. Downregulation of NMI abrogates tumor progression of PDAC both in vitro and in vivo. RNA sequencing was utilized to identify the downstream molecules of NMI and interferon-induced protein with tetratricopeptide repeats 3 (IFIT3) was confirmed to be regulated by NMI in both mRNA and protein level. The binding function of NMI to STAT3 was essential in regulating the IFIT3 expression. Moreover, the NMI/STAT3-IFIT3 axis was identified to markedly facilitate the gemcitabine resistance in PDAC cells.

AFSE: towards improving model generalization of deep graph learning of ligand bioactivities targeting GPCR proteins.

Ligand molecules naturally constitute a graph structure. Recently, many excellent deep graph learning (DGL) methods have been proposed and used to model ligand bioactivities, which is critical for the virtual screening of drug hits from compound databases in interest. However, pharmacists can find that these well-trained DGL models usually are hard to achieve satisfying performance in real scenarios for virtual screening of drug candidates. The main challenges involve that the datasets for training models were small-sized and biased, and the inner active cliff cases would worsen model performance. These challenges would cause predictors to overfit the training data and have poor generalization in real virtual screening scenarios. Thus, we proposed a novel algorithm named adversarial feature subspace enhancement (AFSE). AFSE dynamically generates abundant representations in new feature subspace via bi-directional adversarial learning, and then minimizes the maximum loss of molecular divergence and bioactivity to ensure local smoothness of model outputs and significantly enhance the generalization of DGL models in predicting ligand bioactivities. Benchmark tests were implemented on seven state-of-the-art open-source DGL models with the potential of modeling ligand bioactivities, and precisely evaluated by multiple criteria. The results indicate that, on almost all 33 GPCRs datasets and seven DGL models, AFSE greatly improved their enhancement factor (top-10%, 20% and 30%), which is the most important evaluation in virtual screening of hits from compound databases, while ensuring the superior performance on RMSE and $r^2$. The web server of AFSE is freely available at http://noveldelta.com/AFSE for academic purposes.

Levels of peripheral blood routine, biochemical and coagulation parameters in patients with hemorrhagic fever with renal syndrome and their relationship with prognosis: an observational cohort study.

BACKGROUND: Hantaan virus (HTNV), Seoul virus (SEOV) and Puumala virus (PUUV) are major serotypes of the Hantavirus, which can cause hemorrhagic fever with renal syndrome (HFRS). The pathophysiology of HFRS in humans is complex and the determinants associated with mortality, especially the coagulation and fibrinolysis disorders, are still not been fully elucidated. Severe patients usually manifest multiple complications except for acute kidney injury (AKI). The aim of this study was to observe the levels of peripheral blood routine, biochemical and coagulation parameters during the early stage, so as to find independent risk factors closely related to the prognosis, which may provide theoretical basis for targeted treatment and evaluation. METHODS: A total of 395 HFRS patients from December 2015 to December 2018 were retrospectively enrolled. According to prognosis, they were divided into a survival group (n = 368) and a death group (n = 27). The peripheral blood routine, biochemical and coagulation parameters were compared between the two groups on admission. The relationship between the parameters mentioned above and prognosis was analyzed, and the dynamic changes of the coagulation and fibrinolysis parameters during the first week after admission were further observed. RESULTS: In addition to AKI, liver injury was also common among the enrolled patients. Patients in the death group manifested higher levels of white blood cell counts (WBC) on admission. 27.30% (107/392) of the patients enrolled presented with disseminated intravascular coagulation (DIC) on admission and DIC is more common in the death group; The death patients manifested longer prothrombin time (PT) and activated partial thromboplastin time (APTT), higher D-dimer and fibrinogen degradation product (FDP), and lower levels of platelets (PLT) and fibrinogen (Fib) compared with those of the survival patients. The proportion of D-dimer and FDP abnormalities are higher than PT, APTT and Fib. Prolonged PT, low level of Fib and elevated total bilirubin (TBIL) on admission were considered as independent risk factors for prognosis (death). CONCLUSIONS: Detection of PT, Fib and TBIL on admission is necessary, which might be benefit to early predicting prognosis. It is also important to pay attention to the dynamic coagulation disorders and hyperfibrinolysis during the early stage in the severe HFRS patients.

SMAD4 endows TGF-ß1-induced highly invasive tumor cells with ferroptosis vulnerability in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive malignancy prone to recurrence and metastasis. Studies show that tumor cells with increased invasive and metastatic potential are more likely to undergo ferroptosis. SMAD4 is a critical molecule in the transforming growth factor ß (TGF-ß) pathway, which affects the TGF-ß-induced epithelial-mesenchymal transition (EMT) status. SMAD4 loss is observed in more than half of patients with PDAC. In this study, we investigated whether SMAD4-positive PDAC cells were prone to ferroptosis because of their high invasiveness. We showed that SMAD4 status almost determined the orientation of transforming growth factor ß1 (TGF-ß1)-induced EMT via the SMAD4-dependent canonical pathway in PDAC, which altered ferroptosis vulnerability. We identified glutathione peroxidase 4 (GPX4), which inhibited ferroptosis, as a SMAD4 down-regulated gene by RNA sequencing. We found that SMAD4 bound to the promoter of GPX4 and decreased GPX4 transcription in PDAC. Furthermore, TGF-ß1-induced high invasiveness enhanced sensitivity of SMAD4-positive organoids and pancreas xenograft models to the ferroptosis inducer RAS-selective lethal 3 (RSL3). Moreover, SMAD4 enhanced the cytotoxic effect of gemcitabine combined with RSL3 in highly invasive PDAC cells. This study provides new ideas for the treatment of PDAC, especially SMAD4-positive PDAC.

Semi-Supervised Building Extraction with Optical Flow Correction Based on Satellite Video Data in a Tsunami-Induced Disaster Scene.

Data and reports indicate an increasing frequency and intensity of natural disasters worldwide. Buildings play a crucial role in disaster responses and damage assessments, aiding in planning rescue efforts and evaluating losses. Despite advances in applying deep learning to building extraction, challenges remain in handling complex natural disaster scenes and reducing reliance on labeled datasets. Recent advances in satellite video are opening a new avenue for efficient and accurate building extraction research. By thoroughly mining the characteristics of disaster video data, this work provides a new semantic segmentation model for accurate and efficient building extraction based on a limited number of training data, which consists of two parts: the prediction module and the automatic correction module. The prediction module, based on a base encoder-decoder structure, initially extracts buildings using a limited amount of training data that are obtained instantly. Then, the automatic correction module takes the output of the prediction module as input, constructs a criterion for identifying pixels with erroneous semantic information, and uses optical flow values to extract the accurate corresponding semantic information on the corrected frame. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and computational complexity in complicated natural disaster scenes.

Monocular Depth Estimation via Self-Supervised Self-Distillation.

Self-supervised monocular depth estimation can exhibit excellent performance in static environments due to the multi-view consistency assumption during the training process. However, it is hard to maintain depth consistency in dynamic scenes when considering the occlusion problem caused by moving objects. For this reason, we propose a method of self-supervised self-distillation for monocular depth estimation (SS-MDE) in dynamic scenes, where a deep network with a multi-scale decoder and a lightweight pose network are designed to predict depth in a self-supervised manner via the disparity, motion information, and the association between two adjacent frames in the image sequence. Meanwhile, in order to improve the depth estimation accuracy of static areas, the pseudo-depth images generated by the LeReS network are used to provide the pseudo-supervision information, enhancing the effect of depth refinement in static areas. Furthermore, a forgetting factor is leveraged to alleviate the dependency on the pseudo-supervision. In addition, a teacher model is introduced to generate depth prior information, and a multi-view mask filter module is designed to implement feature extraction and noise filtering. This can enable the student model to better learn the deep structure of dynamic scenes, enhancing the generalization and robustness of the entire model in a self-distillation manner. Finally, on four public data datasets, the performance of the proposed SS-MDE method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy (δ1) of 89% while minimizing the error (AbsRel) by 0.102 in NYU-Depth V2 and achieving an accuracy (δ1) of 87% while minimizing the error (AbsRel) by 0.111 in KITTI.

[Biological function of bladder smooth muscle cells regulated by multi-modal biomimetic stress].

Previous studies have shown that growth arrest, dedifferentiation, and loss of original function occur in cells after multiple generations of culture, which are attributed to the lack of stress stimulation. To investigate the effects of multi-modal biomimetic stress (MMBS) on the biological function of human bladder smooth muscle cells (HBSMCs), a MMBS culture system was established to simulate the stress environment suffered by the bladder, and HBSMCs were loaded with different biomimetic stress for 24 h. Then, cell growth, proliferation and functional differentiation were detected. The results showed that MMBS promoted the growth and proliferation of HBSMCs, and 80 cm H 2O pressure with 4% stretch stress were the most effective in promoting the growth and proliferation of HBSMCs and the expression level of α-smooth muscle actin and smooth muscle protein 22-α. These results suggest that the MMBS culture system will be beneficial in regulating the growth and functional differentiation of HBSMCs in the construction of tissue engineered bladder.

HTNV infection induces activation and deficiency of CD8+MAIT cells in HFRS patients.

Hantaan virus (HTNV) infection causes an epidemic of hemorrhagic fever with renal syndrome (HFRS) mainly in Asia. Mucosal-associated invariant T (MAIT) cells are innate-like T lymphocytes known to play an important role in innate host defense during virus infection. However, their roles and phenotypes during HTNV infection have not yet been explored. We characterized CD8+MAIT cells from HFRS patients based on scRNA-seq data combined with flow cytometry data. We showed that HTNV infection caused the loss and activation of CD8+MAIT cells in the peripheral blood, which were correlated with disease severity. The production of granzyme B and IFN-γ from CD8+MAIT cells and the limitation of HTNV replication in endothelia cells indicated the anti-viral property of CD8+MAIT cells. In addition, in vitro infection of MAIT cells by HTNV or HTNV-exposed monocytes showed that the activation of MAIT cells was IL-18 mediated. In conclusion, this study identified, for the first time, gene expression profiles of MAIT cells, provided underlying molecular mechanisms for activation of MAIT cells during HTNV infection, and suggested a potential anti-viral role of MAIT cells in HFRS.

Spatially-resolved bending recognition based on a learning-empowered fiber specklegram sensor.

Fiber specklegram sensors do not rely on complex fabrication processes and expensive sensor interrogation schemes and provide an alternative to routinely used fiber sensing technologies. Most of the reported specklegram demodulation schemes focus on correlation calculation based on statistical properties or classification according to features, resulting in limited measurement range and resolution. In this work, we propose and demonstrate a learning-empowered spatially resolved method for fiber specklegram bending sensors. This method can learn the evolution process of speckle patterns through a hybrid framework constructed by a data dimension reduction algorithm and regression neural network, which can simultaneously identify the curvature and perturbed position according to the specklegram, even for the unlearned curvature configuration. Rigorous experiments are performed to verify the feasibility and robustness of the proposed scheme, and the results show that the prediction accuracy for the perturbed position is 100%, and the average prediction errors for the curvature of the learned and unlearned configurations are 7.79 × 10-4 m-1 and 7.02 × 10-2 m-1, respectively. The proposed method promotes the application of fiber specklegram sensors in the practical scene and provides insights for the interrogation of sensing signals by deep learning.

Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units.

Modern drug discovery typically faces large virtual screens from huge compound databases where multiple docking tools are involved for meeting various real scenes or improving the precision of virtual screens. Among these tools, AutoDock Vina and its numerous derivatives are the most popular and have become the standard pipeline for molecular docking in modern drug discovery. Our recent Vina-GPU method realized 14-fold acceleration against AutoDock Vina on a piece of NVIDIA RTX 3090 GPU in one virtual screening case. Further speedup of AutoDock Vina and its derivatives with graphics processing units (GPUs) is beneficial to systematically push their popularization in large-scale virtual screens due to their high benefit-cost ratio and easy operation for users. Thus, we proposed the Vina-GPU 2.0 method to further accelerate AutoDock Vina and the most common derivatives with new docking algorithms (QuickVina 2 and QuickVina-W) with GPUs. Caused by the discrepancy in their docking algorithms, our Vina-GPU 2.0 adopts different GPU acceleration strategies. In virtual screening for two hot protein kinase targets, RIPK1 and RIPK3, from the DrugBank database, our Vina-GPU 2.0 reaches an average of 65.6-fold, 1.4-fold, and 3.6-fold docking acceleration against the original AutoDock Vina, QuickVina 2, and QuickVina-W while ensuring their comparable docking accuracy. In addition, we develop a friendly and installation-free graphical user interface tool for their convenient usage. The codes and tools of Vina-GPU 2.0 are freely available at https://github.com/DeltaGroupNJUPT/Vina-GPU-2.0, coupled with explicit instructions and examples.

Engineered Graphene Quantum Dots as a Magnetic Resonance Signal Amplifier for Biomedical Imaging.

The application of magnetic resonance imaging (MRI) nano-contrast agents (nano-CAs) has increasingly attracted scholarly interest owing to their size, surface chemistry, and stability. Herein, a novel T1 nano-CA (Gd(DTPA)-GQDs) was successfully prepared through the functionalization of graphene quantum dots with poly(ethylene glycol) bis(amine) and their subsequent incorporation into Gd-DTPA. Remarkably, the resultant as-prepared nano-CA displayed an exceptionally high longitudinal proton relaxivity (r1) of 10.90 mM-1 s-1 (R2 = 0.998), which was significantly higher than that of commercial Gd-DTPA (4.18 mM-1 s-1, R2 = 0.996). The cytotoxicity studies indicated that the Gd(DTPA)-GQDs were not cytotoxic by themselves. The results of the hemolysis assay and the in vivo safety evaluation demonstrate the outstanding biocompatibility of Gd(DTPA)-GQDs. The in vivo MRI study provides evidence that Gd(DTPA)-GQDs exhibit exceptional performance as T1-CAs. This research constitutes a viable approach for the development of multiple potential nano-CAs with high-performance MR imaging capabilities.

cRGD-Conjugated GdIO Nanoclusters for the Theranostics of Pancreatic Cancer through the Combination of T1-T2 Dual-Modal MRI and DTX Delivery.

Clinically, magnetic resonance imaging (MRI) often uses contrast agents (CAs) to improve image contrast, but single-signal MRI CAs are often susceptible to calcification, hemorrhage, and magnetic sensitivity. Herein, iron acetylacetone and gadolinium acetylacetone were used as raw materials to synthesize a T1-T2 dual-mode imaging gadolinium-doped iron oxide (GdIO) nanocluster. Moreover, to endow the nanoclusters with targeting properties and achieve antitumor effects, the cyclic Arg-Gly-Asp (cRGD) peptide and docetaxel (DTX) were attached to the nanocluster surface, and the efficacy of the decorated nanoclusters against pancreatic cancer was evaluated. The final synthesized material cRGD-GdIO-DTX actively targeted αvß3 on the surface of Panc-1 pancreatic cancer cells. Compared with conventional passive targeting, the enrichment of cRGD-GdIO-DTX in tumor tissues improved, and the diagnostic accuracy was significantly enhanced. Moreover, the acidic tumor microenvironment triggered the release of DTX from cRGD-GdIO-DTX, thus achieving tumor treatment. The inhibition of the proliferation of SW1990 and Panc-1 pancreatic cancer cells by cRGD-GdIO-DTX was much stronger than that by the untargeted GdIO-DTX and free DTX in vitro. In addition, in a human pancreatic cancer xenograft model, cRGD-GdIO-DTX considerably slowed tumor development and demonstrated excellent magnetic resonance enhancement. Our results suggest that cRGD-GdIO-DTX has potential applications for the precise diagnosis and efficient treatment of pancreatic cancer.

Comparison of the Effects of Metformin and Thiazolidinediones on Bone Metabolism: A Systematic Review and Meta-Analysis.

OBJECTIVES: Studies have shown that people with diabetes have a high risk of osteoporosis and fractures. The effect of diabetic medications on bone disease cannot be ignored. This meta-analysis aimed to compare the effects of two types of glucose-lowering drugs, metformin and thiazolidinediones (TZD), on bone mineral density and bone metabolism in patients with diabetes mellitus. METHODS: This systematic review and meta-analysis were prospectively registered on PROSPERO, and the registration number is CRD42022320884. Embase, PubMed, and Cochrane Library databases were searched to identify clinical trials comparing the effects of metformin and thiazolidinediones on bone metabolism in patients with diabetes. The literature was screened by inclusion and exclusion criteria. Two assessors independently assessed the quality of the identified studies and extracted relevant data. RESULTS: Seven studies involving 1656 patients were finally included. Our results showed that the metformin group had a 2.77% (SMD = 2.77, 95%CI [2.11, 3.43]; p < 0.00001) higher bone mineral density (BMD) than the thiazolidinedione group until 52 weeks; however, between 52 and 76 weeks, the metformin group had a 0.83% (SMD = -0.83, 95%CI: [-3.56, -0.45]; p = 0.01) lower BMD. The C-terminal telopeptide of type I collagen (CTX) and procollagen type I N-terminal propeptide (PINP) were decreased by 18.46% (MD = -18.46, 95%CI: [-27.98, -8.94], p = 0.0001) and 9.94% (MD = -9.94, 95%CI: [-16.92, -2.96], p = 0.005) in the metformin group compared with the TZD group.

Superoscillation focusing with suppressed sidebands by destructive interference.

Optical superoscillation, a phenomenon that the local optical field can oscillate much faster than that allowed by its highest harmonic, can significantly overcome the Abbe diffraction limit. However, as the spot size is compressed below the superoscillation criteria of 0.38λ/NA, huge sidebands will inevitably appear around the central lobe with intensity hundreds of times higher than that of the central lobe. Here, we propose an approach to realize superoscillation by using destructive interference. The central lobe size can be compressed beyond the superoscillation criteria without formation of strong sidebands by destructive interference between focused fields. Such a super-resolution metalens can find its application in label-free far-field super-resolution microscopy.

Manipulating Cation Exchange Reactions in Cu2-xS Nanoparticles via Crystal Structure Transformation.

Copper-deficient Cu2-xS nanoparticles (NPs) are extensively exploited as a superior cation exchange (CE) template to yield sophisticated nanostructures. Recently, it has been discovered that their CE reactions can be facilely manipulated by copper vacancy density, morphology, and NP size. However, the structural similarity of usually utilized Cu2-xS somewhat limits the manipulation of the CE reactions through the factor of crystal structure because it can strongly influence the process of the reaction. Herein, we report a methodology of crystal structure transformation to manipulate the CE reactions. Particularly, roxbyite Cu1.8S nanodisks (NDs) were converted into solid wurtzite CdS NDs and Janus-type Cu1.94S/CdS NDs by a "full"/partial CE reaction with Cd2+. Afterward, the roxbyite Cu1.8S were pseudomorphically transformed into covellite CuS NDs. Unlike Cu1.8S, the CuS was scarcely exchanged because of the unique disulfide (S-S) bonds and converted into hollow wurtzite CdS under a more reactive condition. The S-S bonds were gradually split and CuS@CdS core@shell-type NDs were generated. Therefore, our findings in the present study provide not only a versatile technique to manipulate CE reactions in Cu2-xS NPs but also a better comprehension of their reaction dynamics and pathways.

Synthesis and biological evaluation of glucagon-like peptide-1 analogs with the C-terminal helix 3 of albumin-binding domain 3.

Based on peptide 6 (Ser8-GLP-1 [7-35]-GVKALIDEILAA-NH2; GVKALI-DEILAA is the C-terminal helix 3 of albumin-binding domain 3 of protein G from bacterial Streptococcal G strain 148 [G148-ABD3]), a series of its analogs (compounds 0-VI: Aib8-GLP-1 [7-35]-linkers-GVKALIDEILAA-NH2) were designed and synthesized using microwave-assisted solid-phase synthesis. First, to monitor the reaction process and reduce potential risks, the synthesis process of compounds 0-VI was divided into three stages. Next, to explore the effect of these linkers on their albumin-binding rates, albumin-binding assays were performed. Finally, to evaluate their biological activities in vitro and in vivo, receptor potency, surface plasmon resonance (SPR), weight-loss, and glucose-lowering assays were carried out. These results indicated the linkers of different polarities between Aib8-GLP-1 (7-35) and the C-terminal helix 3 of ABD3 can significantly affect the albumin-binding rate of the C-terminal helix 3 of ABD3. And compound IV had the highest albumin-binding rates, weight-loss, and glucose-lowering effects among them.

Efficient synthesis of Aib8 -Arg34 -GLP-1 (7-37) by liquid-phase fragment condensation.

Glucagon-like peptide-1 (GLP-1) is a potential therapeutic agent for treating Type 2 diabetes, owing to its glucose-dependent capability to stimulate insulin secretion. Semaglutide is currently the best GLP-1 analogue; however, the Aib8 -Arg34 -GLP-1 (7-37) of semaglutide contains an unnatural amino acid at the eighth position (Aib: 2-aminoisobutyric acid), which hinders its fermentation process. Aib8 -Arg34 -GLP-1 (7-37) is mainly synthesised by solid-phase synthesis. However, solid-phase synthesis of Aib8 -Arg34 -GLP-1 (7-37) has many shortcomings: (i) The synthesis requires many organic solvents, (ii) the existence of deletion peptides impedes the subsequent purification process, (iii) the yield is low (approximately 16%), and (iv) it is not suitable for large-scale synthesis. However, the synthesis of Aib8 -Arg34 -GLP-1 (7-37) by liquid-phase fragment condensation of expressed and synthetic peptides (Arg34 -GLP-1 (9-37) and Boc-His (Boc)-Aib) has many advantages: (i) The synthesis process only requires a few organic reagents, (ii) the yield is high (approximately 60%), (iii) the purification conditions are simple and Aib8 -Arg34 -GLP-1 (7-37) with a purity of over 98% is obtained through one-step reverse-phase purification, and (iv) the raw materials are inexpensive and large-scale synthesis is possible. In conclusion, here, we developed an efficient method for synthesising Aib8 -Arg34 -GLP-1 (7-37).

Effect of CTP-mediated PTEN on 5637 bladder cancer cells and the underlying molecular mechanism.

OBJECTIVE: The aim of the present study was to explore the effect of cytoplasmic transduction peptide (CTP)-phosphatase and tensin homolog (PTEN) on the proliferation, cell cycle, apoptosis, migration and invasion of bladder cancer cells and the underlying molecular mechanism. METHODS: A eukaryotic expression vector, pTT5-CTP-PTEN, was constructed. The constructed vector was transfected into HEK 293-6E cells to express a fusion protein, CTP-PTEN. The fusion protein was purified. 5637 bladder cancer cells were cocultured with purified CTP-PTEN fusion protein. Target gene expression, protein expression, cell proliferation, cell cycle, apoptosis, cell invasion and cell migration were examined by reverse transcription polymerase chain reaction (RT-PCR), western blot, MTT assay, flow cytometry, Transwell assay, and cell scratch assay, respectively. RESULTS: Both PTEN and CTP-PTEN fusion protein inhibited the proliferation, cell cycle, invasion and migration of bladder cancer cells and promoted the apoptosis of bladder cancer cells. The effect of CTP-PTEN was more significant. CONCLUSIONS: The fused expression of CTP and PTEN significantly increased the penetrability of the tumor suppressor gene PTEN into cancer cells. The CTP-PTEN fusion protein exhibited a significant carcinostatic effect on 5637 bladder cancer cells.