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PROteolysis TArgeting Chimeras (PROTACs) has been exploited to degrade putative protein targets. However, the antitumor performance of PROTACs is impaired by their insufficient tumour distribution. Herein, we present de novo designed polymeric PROTAC (POLY-PROTAC) nanotherapeutics for tumour-specific protein degradation. The POLY-PROTACs are engineered by covalently grafting small molecular PROTACs onto the backbone of an amphiphilic diblock copolymer via the disulfide bonds. The POLY-PROTACs self-assemble into micellar nanoparticles and sequentially respond to extracellular matrix metalloproteinase-2, intracellular acidic and reductive tumour microenvironment. The POLY-PROTAC NPs are further functionalized with azide groups for bioorthogonal click reaction-amplified PROTAC delivery to the tumour tissue. For proof-of-concept, we demonstrate that tumour-specific BRD4 degradation with the bioorthogonal POLY-PROTAC nanoplatform combine with photodynamic therapy efficiently regress tumour xenografts in a mouse model of MDA-MB-231 breast cancer. This study suggests the potential of the POLY-PROTACs for precise protein degradation and PROTAC-based cancer therapy. Proteolysis targeting chimeras (PROTACs) have emerged as promising cancer therapy agents but have suffered from systemic toxicity issues. Here, the authors report on the creation of polymeric PROTAC nanoparticles for tumour targeting delivery and demonstrate protein degradation in vivo, in combination with photodynamic therapy.

Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tasks like ligand-based virtual screening, drug response prediction, and phenotype-based drug repurposing. Notably, in vitro validation of TranSiGen’s application in pancreatic cancer drug discovery highlights its potential for identifying effective compounds. We envisage that integrating TranSiGen into the drug discovery and mechanism research holds significant promise for advancing biomedicine. While chemical-induced transcriptional profiles reveal drug mechanisms, inherent noise limits their utility. Here, authors present TranSiGen, a deep representation learning model that denoises and reconstructs these profiles, demonstrating its efficacy in downstream drug discovery tasks.

Background. Ischemic stroke is a severe acute cerebrovascular disease which can be improved with neuroprotective therapies at an early stage. However, due to the lack of effective neuroprotective drugs, most stroke patients have varying degrees of long-term disability. In the present study, we investigated the role of exosomes derived from CXCR4-overexpressing BMSCs in restoring vascular function and neural repair after ischemic cerebral infarction. Methods. BMSCs were transfected with lentivirus encoded by CXCR4 (BMSCCXCR4). Exosomes derived from BMSCCXCR4 (ExoCXCR4) were isolated and characterized by transmission electron microscopy and dynamic light scattering. Western blot and qPCR were used to analyze the expression of CXCR4 in BMSCs and exosomes. The acute middle cerebral artery occlusion (MCAO) model was prepared, ExoCXCR4 were injected into the rats, and behavioral changes were analyzed. The role of ExoCXCR4 in promoting the proliferation and tube formation for angiogenesis and protecting brain endothelial cells was determined in vitro. Results. Compared with the control groups, the ExoCXCR4 group showed a significantly lower mNSS score at 7 d, 14 d, and 21 d after ischemia/reperfusion (P<0.05). The bEnd.3 cells in the ExoCXCR4 group have stronger proliferation ability than other groups (P<0.05), while the CXCR4 inhibitor can reduce this effect. Exosomes control (ExoCon) can significantly promote the migration of bEnd.3 cells (P<0.05), while there was no significant difference between the ExoCXCR4 and ExoCon groups (P>0.05). ExoCXCR4 can further promote the proliferation and tube formation for the angiogenesis of the endothelium compared with ExoCon group (P<0.05). In addition, cobalt chloride (COCl2) can increase the expression of β-catenin and Wnt-3, while ExoCon can reduce the expression of these proteins (P<0.05). ExoCXCR4 can further attenuate the activation of Wnt-3a/β-catenin pathway (P<0.05). Conclusions. In ischemia/reperfusion injury, ExoCXCR4 promoted the proliferation and tube formation of microvascular endothelial cells and play an antiapoptotic role via the Wnt-3a/β-catenin pathway.

Regulatory T (T reg ) cells are critical for immune tolerance but also form a barrier to antitumor immunity. As therapeutic strategies involving T reg cell depletion are limited by concurrent autoimmune disorders, identification of intratumoral T reg cell-specific regulatory mechanisms is needed for selective targeting. Epigenetic modulators can be targeted with small compounds, but intratumoral T reg cell-specific epigenetic regulators have been unexplored. Here, we show that JMJD1C, a histone demethylase upregulated by cytokines in the tumor microenvironment, is essential for tumor T reg cell fitness but dispensable for systemic immune homeostasis. JMJD1C deletion enhanced AKT signals in a manner dependent on histone H3 lysine 9 dimethylation (H3K9me2) demethylase and STAT3 signals independently of H3K9me2 demethylase, leading to robust interferon-γ production and tumor T reg cell fragility. We have also developed an oral JMJD1C inhibitor that suppresses tumor growth by targeting intratumoral T reg cells. Overall, this study identifies JMJD1C as an epigenetic hub that can integrate signals to establish tumor T reg cell fitness, and we present a specific JMJD1C inhibitor that can target tumor T reg cells without affecting systemic immune homeostasis. Here, the authors target intratumoral T reg cells to enhance antitumor immunity without affecting systemic T reg cell function and identify JMJD1C as a critical epigenetic regulator of tumor T reg cell fitness.

Drought is a frequent global phenomenon. Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal, has been proven to be an efficient tool for monitoring and assessing gross primary productivity (GPP) and drought. To address the issue of the sparse resolution of satellite-based SIF, researchers have developed different downscaling algorithms. Recently, the most frequently used SIF products had a spatial resolution of 0.05 degrees. However, these spatial resolution SIF data are not conducive to regional agricultural drought monitoring. In this study, we utilized the global ‘OCO-2’ solar-induced fluorescence (GOSIF) products along with normalized difference vegetation index (NDVI) and land surface temperature (LST) products. With the powerful advantages offered by Google Earth Engine (GEE), we could conveniently acquire the necessary data. Additionally, employing the random forest (RF) method, we successfully acquired downscaled SIF data at an enhanced spatial resolution of 1 km. Using those downscaled SIF results with 1 km resolution, an SIF anomaly index was established and calculated to monitor drought. Results showed that the RF-based downscaled SIF result followed the same trend as the GOSIF value. Subsequently, correlation coefficients between SIF and GPP were calculated. The downscaled SIF demonstrated a higher correlation with GPP from MODIS compared to 0.05-degree GOSIF, with coefficients of 0.74 and 0.68 in May 2018, respectively. Moreover, the SIF anomaly index showed positive correlations with crop yield; the correlation coefficients were 0.93 for wheat and 0.89 for maize. The drought index had a negative correlation with areas affected by drought, with a correlation coefficient of −0.58. Finally, the SIF anomaly index was used to monitor drought from 2001 to 2020 in Henan Province. The 1 km SIF results obtained through the RF-based downscaled method were deemed reliable, thereby establishing the suitability of the SIF anomaly index for drought monitoring at a regional scale.

Background Colorectal cancer (CRC) is the second most common cause of cancer‐related death worldwide. Long noncoding RNA (lncRNA) is involved in many malignant tumors. This study aimed to clarify the role of the lncRNA plasmacytoma variant translocation 1 (PVT1) in CRC growth and metastasis. Methods Differentially expressed lncRNAs in CRC were analyzed using the Cancer Genome Atlas. Gene expression profiling interactive analysis and a comprehensive resource for lncRNAs from cancer arrays databases were used to analyze lncRNA PVT1 expression and CRC prognosis, respectively. Cell counting kit‐8, wound healing, colony formation, Transwell, and immunofluorescence assays were used to evaluate CRC cell proliferation, migration, invasion, and epithelial‐mesenchymal transition (EMT), respectively. Tumor growth and metastasis models were used to explore the PVT1 effect on the growth and metastasis of CRC in vivo. Results PVT1 was highly expressed in CRC, associated with a poor prognosis of CRC, and showed good diagnostic value. Transfection of sh‐PVT1 or pcDNA3.1‐PVT1 reduced or increased the proliferation, wound healing rate, colony formation, invasion, and EMT of CRC cells. PVT1 and miR‐3619‐5p were co‐expressed in CRC cytoplasm, and PVT1 acted as a competitive endogenous RNA (ceRNA) by sponging miR‐3619‐5p to up‐regulate tripartite motif containing 29 (TRIM29) expression. MiR‐3619‐5p overexpression and TRIM29 knockdown reduced proliferation, wound healing rate, invasion, and EMT of CRC cells. However, simultaneous PVT1 and miR‐3619‐5p overexpression or knockdown of miR‐3619‐5p and TRIM29 knockdown rescued the malignant phenotype of CRC cells. Conclusions We first clarified the ceRNA mechanism of PVT1 in CRC, which induced growth and metastasis by sponging with miR‐3619‐5p to regulate TRIM29.

Land-use and land-cover changes constitute pivotal components in global environmental change research. Through an examination of spatiotemporal variations in land cover, we can deepen our understanding of land-cover change dynamics, shape appropriate policy frameworks, and implement targeted environmental conservation strategies. The judicious management of land is a critical determinant in fostering the sustainable growth of urban economies and enhancing quality of life for residents. This study harnessed remote sensing data to analyze land-cover patterns in Tianjin over five distinct time points: 2000, 2005, 2010, 2015, and 2020. It focused on evaluating the evolving dynamics, transition velocities, and transformation processes across various land categories within the region. Utilizing dynamic analysis and a transition matrix, the study traced shifts among different land-use classes. The center-of-gravity migration model was employed to elucidate land-cover pattern evolution. This research also integrated pertinent land-cover statistics to offer a holistic perspective on Tianjin’s land-cover transformations. Employing the CA–Markov model, we projected the prospective spatial layout of land cover for the area. Our findings revealed the following. (1) From 2000 to 2020, Tianjin experienced a significant reduction in cropland, forest, grassland, and water areas, alongside a substantial increase in impervious. (2) The impervious surface’s center of gravity, initially in Beichen District, shifted 4.20 km northwestward at an average rate of 0.84 km per year. (3) Principal component analysis indicated that the growth in the output value of the secondary and forestry industries is a key driver in expanding Tianjin’s impervious-surface area. (4) Predictions for 2025 suggest an increase in Tianjin’s impervious-surface area to 4659.78 km2, with a concurrent reduction in cropland to 5656.18 km2. The insights gleaned from this study provide a solid theoretical foundation and empirical evidence, aiding in the formulation of informed land-use strategies, the preservation of urban land resources, and guiding principles for sustainable urban development.

Blood–brain barrier is a pivotal factor to be considered in the process of central nervous system (CNS) drug development, and it is of great significance to rapidly explore the blood–brain barrier permeability (BBBp) of compounds in silico in early drug discovery process. Here, we focus on whether and how uncertainty estimation methods improve in silico BBBp models. We briefly surveyed the current state of in silico BBBp prediction and uncertainty estimation methods of deep learning models, and curated an independent dataset to determine the reliability of the state-of-the-art algorithms. The results exhibit that, despite the comparable performance on BBBp prediction between graph neural networks-based deep learning models and conventional physicochemical-based machine learning models, the GROVER-BBBp model shows greatly improvement when using uncertainty estimations. In particular, the strategy combined Entropy and MC-dropout can increase the accuracy of distinguishing BBB + from BBB − to above 99% by extracting predictions with high confidence level (uncertainty score < 0.1). Case studies on preclinical/clinical drugs for Alzheimer’ s disease and marketed antitumor drugs that verified by literature proved the application value of uncertainty estimation enhanced BBBp prediction model, that may facilitate the drug discovery in the field of CNS diseases and metastatic brain tumors.

Lipophilicity is a fundamental physical property that significantly affects various aspects of drug behavior, including solubility, permeability, metabolism, distribution, protein binding, and toxicity. Accurate prediction of lipophilicity, measured by the logD7.4 value (the distribution coefficient between n-octanol and buffer at physiological pH 7.4), is crucial for successful drug discovery and design. However, the limited availability of data for logD modeling poses a significant challenge to achieving satisfactory generalization capability. To address this challenge, we have developed a novel logD7.4 prediction model called RTlogD, which leverages knowledge from multiple sources. RTlogD combines pre-training on a chromatographic retention time (RT) dataset since the RT is influenced by lipophilicity. Additionally, microscopic pKa values are incorporated as atomic features, providing valuable insights into ionizable sites and ionization capacity. Furthermore, logP is integrated as an auxiliary task within a multitask learning framework. We conducted ablation studies and presented a detailed analysis, showcasing the effectiveness and interpretability of RT, pKa, and logP in the RTlogD model. Notably, our RTlogD model demonstrated superior performance compared to commonly used algorithms and prediction tools. These results underscore the potential of the RTlogD model to improve the accuracy and generalization of logD prediction in drug discovery and design. In summary, the RTlogD model addresses the challenge of limited data availability in logD modeling by leveraging knowledge from RT, microscopic pKa, and logP. Incorporating these factors enhances the predictive capabilities of our model, and it holds promise for real-world applications in drug discovery and design scenarios. Graphical Abstract