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Monocyte recruitment in metastasis Macrophages are abundant in tumours, where they promote tumorigenesis and metastasis. Jeffrey Pollard and colleagues now identify inflammatory monocytes as a precursor population for the macrophages that promote breast cancer metastasis to the lung or bone. These cells are recruited through the cytokine CCL2 and promote tumour-cell extravasation (by producing the signal protein VEGF) and the seeding of metastases. These findings can explain the poor prognosis for breast cancer patients with elevated expression of CCL2. Macrophages, which are abundant in the tumour microenvironment, enhance malignancy 1 . At metastatic sites, a distinct population of metastasis-associated macrophages promotes the extravasation, seeding and persistent growth of tumour cells 2 . Here we define the origin of these macrophages by showing that Gr1-positive inflammatory monocytes are preferentially recruited to pulmonary metastases but not to primary mammary tumours in mice. This process also occurs for human inflammatory monocytes in pulmonary metastases of human breast cancer cells. The recruitment of these inflammatory monocytes, which express CCR2 (the receptor for chemokine CCL2), as well as the subsequent recruitment of metastasis-associated macrophages and their interaction with metastasizing tumour cells, is dependent on CCL2 synthesized by both the tumour and the stroma. Inhibition of CCL2–CCR2 signalling blocks the recruitment of inflammatory monocytes, inhibits metastasis in vivo and prolongs the survival of tumour-bearing mice. Depletion of tumour-cell-derived CCL2 also inhibits metastatic seeding. Inflammatory monocytes promote the extravasation of tumour cells in a process that requires monocyte-derived vascular endothelial growth factor. CCL2 expression and macrophage infiltration are correlated with poor prognosis and metastatic disease in human breast cancer 3 , 4 , 5 , 6 . Our data provide the mechanistic link between these two clinical associations and indicate new therapeutic targets for treating metastatic breast cancer.

A comprehensive comparison of the trends and drivers of global surface and canopy urban heat islands (termed Is and Ic trends, respectively) is critical for better designing urban heat mitigation strategies. However, such a global comparison remains largely absent. Using spatially continuous land surface temperatures and surface air temperatures (2003–2020), here we find that the magnitude of the global mean Is trend (0.19 ± 0.006°C/decade, mean ± SE) for 5,643 cities worldwide is nearly six‐times the corresponding Ic trend (0.03 ± 0.002°C/decade) during the day, while the former (0.06 ± 0.004°C/decade) is double the latter (0.03 ± 0.002°C/decade) at night. Variable importance scores indicate that global daytime Is trend is slightly more controlled by surface property, while background climate plays a more dominant role in regulating global daytime Ic trend. At night, both global Is and Ic trends are mainly controlled by background climate. Plain Language Summary Surface and canopy urban heat islands (surface and canopy UHIs, termed Is and Ic) are two major UHI types. These two counterparts are both related to urban population heat exposure and have long been a focus of urban climate research. However, the differences in the trends and major determinants of Is and Ic over global cities remain largely unclear. Based on spatially continuous land surface temperature and surface air temperature observations from 2003 to 2020, we find that the global mean Is trends are about 6.3 times and 2 times the Ic trends during the day and at night, respectively. During the day, the global Is trend is more regulated by surface property than by background climate, and vice versa for global Ic trend. At night, both the global Is and Ic trends are mainly regulated by background climate. These findings are important for better understanding global urban climate change and informing heat mitigation strategies. Key Points The global Is trend is six‐fold and twofold larger than the Ic trend during the day and at night, respectively During the day, global Is trend is slightly more controlled by surface property, yet background climate plays a dominant role in Ic trend At night, both global Is and Ic trends are more regulated by background climate

WNT/β-catenin signalling is crucial for intestinal homoeostasis. The intestinal epithelium and stroma are the major source of WNT ligands but their origin and role in intestinal stem cell (ISC) and epithelial repair remains unknown. Macrophages are a major constituent of the intestinal stroma. Here, we analyse the role of macrophage-derived WNT in intestinal repair in mice by inhibiting their release using a macrophage-restricted ablation of Porcupine, a gene essential for WNT synthesis. Such Porcn -depleted mice have normal intestinal morphology but are hypersensitive to radiation injury in the intestine compared with wild-type (WT) littermates. Porcn -null mice are rescued from radiation lethality by treatment with WT but not Porcn -null bone marrow macrophage-conditioned medium (CM). Depletion of extracellular vesicles (EV) from the macrophage CM removes WNT function and its ability to rescue ISCs from radiation lethality. Therefore macrophage-derived EV-packaged WNTs are essential for regenerative response of intestine against radiation. The intestinal stroma secretes WNT ligands but the role of WNT in intestinal repair is unclear. Here, the authors show that when WNT synthesis is ablated from stromal macrophages, the intestine morphology is normal but hypersensitive to radiation injury, implicating macrophage-derived WNT in intestinal repair.

The frequent occurrence of urban waterlogging seriously affects people’s lives and the national economy. The use of machine learning (ML) methods to spatially assess urban waterlogging susceptibility is critical for reducing the losses caused by such disasters. It is important to select an equal number of positive and negative samples to train binary ML classifiers for evaluation; in most cases, researchers are only able to obtain a relatively small number of historical waterlogging locations (positive samples), which leads to the selection of a limited number of negative samples, further affecting the trained classifiers’ performance. Facing this issue, we proposed an optimized seed spread algorithm (OSSA) that can estimate the potential inundated areas based on the spatial distribution of elevation and natural waters, thereby increasing the number of positive samples. The primary urban area of Guangzhou, China, was selected as the study region, and random forest was selected as the evaluation algorithm. We further employed two ML methods, support vector machine and logistic regression, to verify the quality of the increased positive samples. The results indicate that compared with the original positive samples, the OSSA-based positive samples achieve the highest area under the curve values among the three tested ML methods, indicating that the OSSA can be a suitable approach to increase the number of positive samples for such studies. We believe that this study advances the ML-based waterlogging susceptibility assessments, which could be valuable for developing countries where intensive hydrologic monitoring is lacking.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy that harbors mutations in homologous recombination-repair (HR-repair) proteins in 20%-25% of cases. Defects in HR impart a specific vulnerability to poly ADP ribose polymerase inhibitors and platinum-containing chemotherapy in tumor cells. However, not all patients who receive these therapies respond, and many who initially respond ultimately develop resistance. Inactivation of the HR pathway is associated with the overexpression of polymerase theta (Polθ, or POLQ). This key enzyme regulates the microhomology-mediated end-joining (MMEJ) pathway of double-strand break (DSB) repair. Using human and murine HR-deficient PDAC models, we found that POLQ knockdown is synthetically lethal in combination with mutations in HR genes such as BRCA1 and BRCA2 and the DNA damage repair gene ATM. Further, POLQ knockdown enhances cytosolic micronuclei formation and activates signaling of cyclic GMP-AMP synthase-stimulator of interferon genes (cGAS-STING), leading to enhanced infiltration of activated CD8+ T cells in BRCA2-deficient PDAC tumors in vivo. Overall, POLQ, a key mediator in the MMEJ pathway, is critical for DSB repair in BRCA2-deficient PDAC. Its inhibition represents a synthetic lethal approach to blocking tumor growth while concurrently activating the cGAS-STING signaling pathway to enhance tumor immune infiltration, highlighting what we believe to be a new role for POLQ in the tumor immune environment.

The stromal microenvironment and particularly the macrophage component of primary tumors influence their malignant potential. However, at the metastatic site the role of these cells and their mechanism of actions for establishment and growth of metastases remain largely unknown. Using animal models of breast cancer metastasis, we show that a population of host macrophages displaying a distinct phenotype is recruited to extravasating pulmonary metastatic cells regardless of species of origin. Ablation of this macrophage population through three independent means (genetic and chemical) showed that these macrophages are required for efficient metastatic seeding and growth. Importantly, even after metastatic growth is established, ablation of this macrophage population inhibited subsequent growth. Furthermore, imaging of intact lungs revealed that macrophages are required for efficient tumor cell extravasation. These data indicate a direct enhancement of metastatic growth by macrophages through their effects on tumor cell extravasation, survival and subsequent growth and identifies these cells as a new therapeutic target for treatment of metastatic disease.

The frequent occurrence of wildfires presents a serious threat to human livelihoods and local ecosystems. The use of machine learning (ML) methods to assess wildfire susceptibility can provide decision support for disaster prevention. However, most current ML‐based wildfire susceptibility assessments overly focus on spatially evaluating the disaster threat, while ignoring the potential threats of wildfires to local ecosystems. This situation makes it difficult to determine seasonal variations in wildfire susceptibility and limits the value of assessment results. We present a framework to assess wildfire susceptibility and wildfire threats seasonally to local ecosystems. The ecosystem service value (ESV) was used as a proxy for the economic value of an ecosystem, the random forest algorithm was used to evaluate wildfire susceptibility, and the Daxinganling region, the largest forested area in China, was selected as the study area, and the dynamic equivalent coefficient factor method was used to calculate the ESV of each cell. Our main findings were as follows: (a) wildfire susceptibility exhibited obvious disparities in terms of spatial distribution across the four seasons; (b) each ecosystem in the study area faced a different magnitude of wildfire disturbance; and (c) the expected ESV loss (USD 10.8 billion) due to wildfires was much higher than the region’s total GDP (USD 2 billion) in 2019. This study was repeatable, and all data required were obtained freely. The methodologies used can be applied directly to other regions. Our study will be of particular interest to developing counties where intensive wildfire monitoring is limited. Plain Language Summary The occurrence of wildfires presents a considerable threat to human livelihoods and ecosystems. Spatially assessing wildfire susceptibility will enable the identification of potential wildfire‐related hot spots, and can provide decision support for wildfire management and ecosystem protection. The use of machine learning (ML) methods to evaluate wildfire susceptibility is an effective way to achieve this. However, most of the current ML‐based wildfire susceptibility assessments merely focus on spatial susceptibility assessments and never consider wildfire consequences, making the seasonal variations of wildfire susceptibility difficult to determine and limiting, therefore, the value of the assessment results. We propose a framework that seasonally evaluates wildfire susceptibility and quantifies the economic loss to local ecosystems due to wildfires. The results demonstrated that wildfire susceptibility varied across the four seasons, and the potential loss due to wildfires in the study region was much higher than the region’s GDP in 2019. This study provides a link between wildfire susceptibility assessments and ecosystem service valuation. Key Points A framework was established to seasonally evaluate the wildfire susceptibility based on machine learning Seasonally economic values of ecosystems were computed Wildfire disturbances to each ecosystems across the four seasons were estimated

The frequent occurrence of urban floods (UFs) poses significant threats to public safety and the national economy. Accurate estimation of urban flood susceptibility (UFS) and the identification of potential hotspots are critical for effective UF management. However, existing UFS studies often fall short due to a limited understanding of UFs’ nature, frequently relying on disaster factors analogous to those used for natural floods while neglecting key urban characteristics, limiting the accuracy of UFS estimates. To address these challenges, we propose a novel framework for UFS assessment. Unlike those studies that focus primarily on topographic and surface characteristics, our approach integrates urban-specific factors that capture the distinctive attributes of the urban environment, including Urban Heat Island Intensity, Urban Rain Island Intensity, Urban Resilience Index, and Impervious Surface Percentage. Guangzhou was selected as the study area, where machine learning methods were employed to calculate UFS, and Shapley Additive Explanation was utilized to quantify the contributions of employed factors. We evaluated the significance of urban factors from three perspectives: classifier performance, map accuracy, and factor importance. The results indicate that (1) urban factors hold significantly greater importance compared to other factors, and (2) the incorporation of urban factors markedly enhances both the performance of the trained classifier and the accuracy of the UFS map. These findings underscore the value of integrating urban factors into UFS assessments, thereby contributing to more precise UF management and supporting sustainable urban development.

Theabrownin is one of the most bioactive compounds in Pu-erh tea. Our previous study revealed that the hypocholesterolemic effect of theabrownin was mediated by the modulation of bile salt hydrolase (BSH)-enriched gut microbiota and bile acid metabolism. In this study, we demonstrated that theabrownin ameliorated high-fat-diet (HFD)-induced obesity by modifying gut microbiota, especially those with 7α-dehydroxylation on the species level, and these changed microbes were positively correlated with secondary bile acid (BA) metabolism. Thus, altered intestinal BAs resulted in shifting bile acid biosynthesis from the classic to the alternative pathway. This shift changed the BA pool by increasing non-12α-hydroxylated-BAs (non-12OH-BAs) and decreasing 12α-hydroxylated BAs (12OH-BAs), which improved energy metabolism in white and brown adipose tissue. This study showed that theabrownin was a potential therapeutic modality for obesity and other metabolic disorders via gut microbiota-driven bile acid alternative synthesis.

Cephalosporin C acylase (CCA) is the key enzyme in the production of 7-aminocephalosporanic acid (7-ACA) via a one-step enzymatic process. To improve the soluble expression level of CCA in recombinant Escherichia coli at elevated temperatures, a library of T7 promoter mutants was created by site-saturation mutagenesis, and a series of mutated promoters were subsequently screened. Green fluorescent protein (GFP) was fused to the C-terminus of CCA to facilitate library screening, and the expression of the CCA and GFP fusion proteins was investigated under the control of the T7 promoter. Twenty-four mutants were selected by detecting the fluorescence intensity of colonies on agar plates to form a library with different expression levels. The enzyme activities of the mutants were positively correlated with their fluorescence intensities. The highest enzyme activity among these mutant promoters was 1.3-fold higher than the enzyme activity resulting from the wild-type promoter when the cells were cultured at 32 °C for 16 h. In addition, the transcription and expression levels of several typical promoters were discussed, and the effects of GFP fusion on the enzyme activity of CCA were investigated.