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Esm-1, endothelial cell-specific molecule-1, is a susceptibility gene for diabetic kidney disease (DKD) and is a secreted proteoglycan, with notable expression in kidney, which attenuates inflammation and albuminuria. However, little is known about Esm1 expression in mature tissues in the presence or absence of diabetes. We utilized publicly available single-cell RNA sequencing data to characterize Esm1 expression in 27,786 renal endothelial cells (RECs) obtained from three mouse and four human databases. We validated our findings using bulk transcriptome data from 20 healthy subjects and 41 patients with DKD and using RNAscope. In both mice and humans, Esm1 is expressed in a subset of all REC types and represents a minority of glomerular RECs. In patients, Esm1(+) cells exhibit conserved enrichment for blood vessel development genes. With diabetes, these cells are fewer in number and shift expression toward chemotaxis pathways. Esm1 correlates with a majority of genes within these pathways, delineating a glomerular transcriptional polarization reflected by the magnitude of Esm1 deficiency. Diabetes correlates with lower Esm1 expression and with changes in the functional characterization of Esm1(+) cells. Thus, Esm1 appears to be a marker for glomerular transcriptional polarization in DKD. NEW & NOTEWORTHY: Esm-1 is primarily expressed in glomerular endothelium in humans. Cells expressing Esm1 exhibit a high degree of conservation in the enrichment of genes related to blood vessel development. In the context of diabetes, these cells are reduced in number and show a significant transcriptional shift toward the chemotaxis pathway. In diabetes, there is a transcriptional polarization in the glomerulus that is reflected by the degree of Esm1 deficiency. [ABSTRACT FROM AUTHOR]

Wheat is an important food crop in China. The quality of wheat affects the development of the agricultural economy. However, the high-quality wheat produced in China cannot meet the demand, so it would be an important direction for research to develop high-quality wheat. Grain protein content (GPC) is an important criterion for the quality of winter wheat and its content directly affects the quality of wheat. Studying the spatial heterogeneity of wheat grain proteins is beneficial to the prediction of wheat quality, and it plays a guiding role in the identification, grading, and processing of wheat quality. Due to the complexity and variability of wheat quality, conventional evaluation methods have shortcomings such as low accuracy and poor applicability. To better predict the GPC, geographically weighted regression (GWR) models, multiple linear regression, random forest (RF), BP neural networks, support vector machine, and long-and-short-term memory algorithms were used to analyze the meteorological data and soil data of Jiangsu Province from March to May in 2019–2022. It was found that the winter wheat GPC rises by 0.17% with every 0.1° increase in north latitude at the county level in Jiangsu. Comparison of the prediction accuracy of the coefficient of determination, mean deviation error, root mean square error, and mean absolute error by analyzing multiple algorithms showed that the GWR model was the most accurate, followed by the RF model. The regression coefficient of precipitation in April showed the smallest range of variation among all factors, indicating that precipitation in April had a more stable effect on GPC in the study area than the other meteorological factors. Therefore, consideration of spatial information might be beneficial in predicting county-level winter wheat GPC. GWR models based on meteorological and soil factors enrich the studies regarding the prediction of wheat GPC based on environmental data. It might be applied to predict winter wheat GPC and improve wheat quality to better guide large-scale production and processing. [ABSTRACT FROM AUTHOR]