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Abstract In plants, the plant-specific RNA polymerase V (Pol V) transcripts non-coding RNAs and provides a docking platform for the association of accessory proteins in the RNA-directed DNA methylation (RdDM) pathway. Various components have been uncovered that are involved in the process of DNA methylation, but it is still not clear how the transcription of Pol V is regulated. Here, we report that the conserved RNA polymerase II (Pol II) elongator, SPT6L, binds to thousands of intergenic regions in a Pol II-independent manner. The intergenic enrichment of SPT6L, interestingly, co-occupies with the largest subunit of Pol V (NRPE1) and mutation of SPT6L leads to the reduction of DNA methylation but not Pol V enrichment. Furthermore, the association of SPT6L at Pol V loci is dependent on the Pol V associated factor, SPT5L, rather than the presence of Pol V, and the interaction between SPT6L and NRPE1 is compromised in spt5l. Finally, Pol V RIP-seq reveals that SPT6L is required to maintain the amount and length of Pol V transcripts. Our findings thus uncover the critical role of a Pol II conserved elongator in Pol V mediated DNA methylation and transcription, and shed light on the mutual regulation between Pol V and II in plants.

Summary: In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8+ T cell types and potential prognostic marker genes. Motivation: A fundamental problem in single-cell RNA sequencing (scRNA-seq) studies is identifying cell types and their associated marker genes using clustering and differential expression analysis between clusters. Many sequenced genes are cell-type irrelevant and significantly influence cell-type identification. Ideally, one should select marker genes for the best cell-type identification. However, because cell types are unknown, directly selecting marker genes seems impractical, and surrogate criteria, such as variance, deviance, and zero proportions, are used for gene selection. The surrogate criteria can miss important genes or select unimportant genes, leaving potentially relevant cell types unidentified. We aim to develop a method that can directly select marker genes with high accuracy and significantly improve cell-type identification.

The coastal zone is abundant in natural resources but has become increasingly fragile in recent years due to climate change and extensive, improper exploitation. Accurate land use and land cover (LULC) mapping of coastal zones using remotely sensed data is crucial for monitoring environmental changes. Traditional classification methods based on statistical learning require significant spectral differences between ground objects. However, state-of-the-art end-to-end deep learning methods can extract advanced features from remotely sensed data. In this study, we employed ResNet50 as the feature extraction network within the U-Net architecture to achieve accurate classification of coastal areas and assess the model’s performance. Experiments were conducted using Gaofen-2 (GF-2) high-resolution remote sensing data from Shuangyue Bay, a typical coastal area in Guangdong Province. We compared the classification results with those obtained from two popular deep learning models, SegNet and DeepLab v3+, as well as two advanced statistical learning models, Support Vector Machine (SVM) and Random Forest (RF). Additionally, this study further explored the significance of Gray Level Co-occurrence Matrix (GLCM) texture features, Histogram Contrast (HC) features, and Normalized Difference Vegetation Index (NDVI) features in the classification of coastal areas. The research findings indicated that under complex ground conditions, the U-Net model achieved the highest overall accuracy of 86.32% using only spectral channels from GF-2 remotely sensed data. When incorporating multiple features, including spectrum, texture, contrast, and vegetation index, the classification accuracy of the U-Net algorithm significantly improved to 93.65%. The major contributions of this study are twofold: (1) it demonstrates the advantages of deep learning approaches, particularly the U-Net model, for LULC classification in coastal zones using high-resolution remote sensing images, and (2) it analyzes the contributions of spectral and spatial features of GF-2 data for different land cover types through a spectral and spatial combination method.

Cadmium (Cd) is a toxic heavy metal that can accumulate in crop plants. We reported previously the engineering of a low cadmium-accumulating line (2B) of rice through overexpression of a truncated OsO3L2 gene. As expression of this transgene was highest in plant roots, amplicon and metatranscriptome sequencing were used to investigate the possibility that its expression affects root associated microbes. Based on amplicon sequencing of bacterial 16S rRNA, but less so from fungal ITS, the OTUs (operational taxonomic units) showed less diversity in soil tightly (rhizoplane) than loosely (rhizosphere) associated with plant roots. Significantly changed OTUs caused by the low-Cd accumulating plant 2B, Cd treatment or both were found, and 10 of the 13 OTUs (77%) that were enriched in Cd treated 2B samples over the wild type counterpart have been previously described as involved in tolerance to Cd or other heavy metals. Metatranscriptome sequencing of rhizosphere microbiome found that bacteria accounted for 70–75% of the microbial RNA. Photosynthesis-antenna proteins and nitrogen metabolism pathways were most active in soil microbes treated with Cd and grown with plant 2B. Correspondingly, the relative abundance of Cyanobacteria was enriched to

Cadmium pollution threatens food safety and security by causing health issues and reducing farmland availability. Engineering genetic changes in crop plants to lower Cd accumulation can be a cost-effective approach to address this problem. Previously, we reported that a rice line, 2B, which expresses a truncated version of OsO3L2 had reduced Cd accumulation throughout the plant, including in seed. However, downstream events caused by expression of this gene were not known. In this study, RNA-seq was used to identify differentially expressed genes between the wild type and 2B rice with or without Cd treatment, leading to the study of an ABC transporter gene, OsABCG48 (ATP-Binding Cassette transporter G family member 48). Heterologous expression of OsABCG48 conferred tolerance to Cd in Schizosaccharomyces pombe, Arabidopsis and rice. Moreover, overexpressing OsABCG48 in rice lowered root Cd accumulation that was associated with more extensive lateral root development. These data suggest that OsABCG48 might have applications for engineering low-Cd rice.