DataSheet_2_Gene expression variations and allele-specific expression of two rice and their hybrid in caryopses at single-nucleus resolution.xlsx

Seeds are an indispensable part of the flowering plant life cycle and a critical determinant of agricultural production. Distinct differences in the anatomy and morphology of seeds separate monocots and dicots. Although some progress has been made with respect to understanding seed development in Arabidopsis, the transcriptomic features of monocotyledon seeds at the cellular level are much less understood. Since most important cereal crops, such as rice, maize, and wheat, are monocots, it is essential to study transcriptional differentiation and heterogeneity during seed development at a finer scale. Here, we present single-nucleus RNA sequencing (snRNA-seq) results of over three thousand nuclei from caryopses of the rice cultivars Nipponbare and 9311 and their intersubspecies F1 hybrid. A transcriptomics atlas that covers most of the cell types present during the early developmental stage of rice caryopses was successfully constructed. Additionally, novel specific marker genes were identified for each nuclear cluster in the rice caryopsis. Moreover, with a focus on rice endosperm, the differentiation trajectory of endosperm subclusters was reconstructed to reveal the developmental process. Allele-specific expression (ASE) profiling in endosperm revealed 345 genes with ASE (ASEGs). Further pairwise comparisons of the differentially expressed genes (DEGs) in each endosperm cluster among the three rice samples demonstrated transcriptional divergence. Our research reveals differentiation in rice caryopsis from the single-nucleus perspective and provides valuable resources to facilitate clarification of the molecular mechanism underlying caryopsis development in rice and other monocots.</p

DataSheet_1_Gene expression variations and allele-specific expression of two rice and their hybrid in caryopses at single-nucleus resolution.docx

Seeds are an indispensable part of the flowering plant life cycle and a critical determinant of agricultural production. Distinct differences in the anatomy and morphology of seeds separate monocots and dicots. Although some progress has been made with respect to understanding seed development in Arabidopsis, the transcriptomic features of monocotyledon seeds at the cellular level are much less understood. Since most important cereal crops, such as rice, maize, and wheat, are monocots, it is essential to study transcriptional differentiation and heterogeneity during seed development at a finer scale. Here, we present single-nucleus RNA sequencing (snRNA-seq) results of over three thousand nuclei from caryopses of the rice cultivars Nipponbare and 9311 and their intersubspecies F1 hybrid. A transcriptomics atlas that covers most of the cell types present during the early developmental stage of rice caryopses was successfully constructed. Additionally, novel specific marker genes were identified for each nuclear cluster in the rice caryopsis. Moreover, with a focus on rice endosperm, the differentiation trajectory of endosperm subclusters was reconstructed to reveal the developmental process. Allele-specific expression (ASE) profiling in endosperm revealed 345 genes with ASE (ASEGs). Further pairwise comparisons of the differentially expressed genes (DEGs) in each endosperm cluster among the three rice samples demonstrated transcriptional divergence. Our research reveals differentiation in rice caryopsis from the single-nucleus perspective and provides valuable resources to facilitate clarification of the molecular mechanism underlying caryopsis development in rice and other monocots.</p

High-throughput sequencing reveals biofluid exosomal miRNAs associated with immunity in pigs

Large numbers of miRNAs are found in biofluid exosomes. We isolated ~50–200 nm diameter exosomes from four types of porcine biofluid (urine, plasma, semen, and bile) using serial centrifugation and ultracentrifugation procedures. A total of 42.15 M raw data were generated from four small RNA libraries. This produced 40.17 M map-able sequences, of which we identified 204 conserved miRNAs, and 190 novel candidate miRNAs. Furthermore, we identified 34 miRNAs specifically expressed in only one library, all with well-characterized immune-related functions. A set of five universally abundant miRNAs (miR-148a-3p, miR-21-5p, let-7f-5p, let-7i-5p, and miR-99a-5p) across all four biofluids was also found. Function enrichment analysis revealed that the target genes of the five ubiquitous miRNAs are primarily involved in immune and RNA metabolic processes. In summary, our findings suggest that porcine biofluid exosomes contain a large number of miRNAs, many of which may be crucial regulators of the immune system. High-throughput sequencing evidence indicates that porcine biofluid exosomes contain a large number of miRNAs, many of which may be crucial regulators of the immune system.</p

Prediction of Semiconducting 2D Nanofilms of Janus WSi₂P₂As₂ for Applications in Sub‑5 nm Field-Effect Transistors

Searching for eligible two-dimensional (2D) semiconductors to fabricate high-performance (HP) short-channel field-effect transistors (FETs) at the nanoscale is essential toward the continuous miniaturization of devices. Herein, we predict the 2D Janus WSi2P2As2 semiconductor and propose it as a qualified channel material for sub-5 nm FETs by using first-principles calculations. The results demonstrate that the monolayer Janus WSi2P2As2 is a 2D semiconducting nanofilm with a band gap of 0.83 eV, a hole mobility of 490 cm2 V–1 s–1 in the armchair direction, and an out-of-plane polarization. Benefiting from these outstanding intrinsic characteristics, the performance of the 5 and 3 nm gate-length WSi2P2As2 FETs can fulfill the International Technology Roadmap for Semiconductors for HP standards after employing optimizing strategies, including underlap structure, dielectric project, and cold source. Our results promote the development of new 2D nanomaterials and device architectures for designing HP short-channel FETs

Differentially expressed genes in leaf, stem, and root based on RNA-seq data.

<p>(a). The growth phenotypes of ZD619 in the eight leaf stage under drought and well-water conditions. The well water-treated plant was shown on the left and drought-treated plant was shown on the right in the picture. (b). Venn diagram of DEGs in leaf, stem, and root. (c). Heat map of 5,866 DEGs in the three tissues. The genes in at least one of the subsets were analyzed. The bar represents the log2 of the drought/control ratio. (d). Heat map of 74 DEGs in the three tissues. The bar represents the log2 of the drought/control ratio.</p

RNA-Seq Analysis Reveals MAPKKK Family Members Related to Drought Tolerance in Maize

<div><p>The mitogen-activated protein kinase (MAPK) cascade is an evolutionarily conserved signal transduction pathway that is involved in plant development and stress responses. As the first component of this phosphorelay cascade, mitogen-activated protein kinase kinase kinases (MAPKKKs) act as adaptors linking upstream signaling steps to the core MAPK cascade to promote the appropriate cellular responses; however, the functions of MAPKKKs in maize are unclear. Here, we identified 71 MAPKKK genes, of which 14 were novel, based on a computational analysis of the maize (<i>Zea mays</i> L.) genome. Using an RNA-seq analysis in the leaf, stem and root of maize under well-watered and drought-stress conditions, we identified 5,866 differentially expressed genes (DEGs), including 8 MAPKKK genes responsive to drought stress. Many of the DEGs were enriched in processes such as drought stress, abiotic stimulus, oxidation-reduction, and metabolic processes. The other way round, DEGs involved in processes such as oxidation, photosynthesis, and starch, proline, ethylene, and salicylic acid metabolism were clearly co-expressed with the MAPKKK genes. Furthermore, a quantitative real-time PCR (qRT-PCR) analysis was performed to assess the relative expression levels of MAPKKKs. Correlation analysis revealed that there was a significant correlation between expression levels of two MAPKKKs and relative biomass responsive to drought in 8 inbred lines. Our results indicate that MAPKKKs may have important regulatory functions in drought tolerance in maize.</p></div

Differential expression of MAPKKKs identified by RNA-seq and qRT-PCR.

<p>(a, b and c). Expression patterns of 71 MAPKKK genes in the three tissues. (a). ZIK family genes. (b). MEKK family genes. (c). Raf family genes. (d). Relative expression levels of the MAPKKK genes in various tissues based on qRT-PCR analysis. The expression in leaf is shown on the left. The expression in stem is shown on the right.</p

Co-expression analysis of tissue-specific DEGs and MAPKKK genes.

<p>The weight value obtained from the WGCNA package was used as a parameter for the parametric analysis of gene co-expression levels. A cutoff of 0.2 was used to select highly co-expressed genes in all three tissues. The left side of the heat map represents the Z-scores obtained from a parametric analysis of gene co-expression. The lower left bar represents the degrees of the Z-score scale. The right side of the heat map represents the expression patterns of the DEGs co-expressed with MAPKKKs that were enriched in the corresponding pathways on the left. The lower right bar represents the log2 of the drought/control ratio.</p

Differentially expressed genes (drought vs. control).

<p>Differentially expressed genes (drought vs. control).</p

Relative gene expression of the 8 MAPKKK genes in various inbred lines based on qRT-PCR analysis.

<p>To determine whether the relative expression levels of the drought stress-responsive MAPKKK genes differed among varieties induced by drought, ZD619 and the 6 inbred maize lines J24, J853, X178, E28, C8605-2, 200B, Q319 and B73 were used. Lines X178, J24 are drought-resistant lines; 200B and E28 have poor drought tolerance. The eight MAPKKK genes are <i>GRMZM2G305066</i> (<i>ZmMAPKKK18</i>), <i>GRMZM2G165099</i> (<i>ZmMAPKKK19</i>), <i>GRMZM2G476477</i> (<i>ZmMAPKKK20</i>), <i>GRMZM2G173965</i> (<i>ZmMAPKKK21</i>), <i>GRMZM2G041774</i> (<i>ZmMAPKKK22</i>), <i>GRMZM2G021416</i> (<i>ZmMAPKKK26</i>), <i>GRMZM2G063069</i> (<i>ZmMAPKKK56</i>), <i>GRMZM2G474546</i> (<i>ZmMAPKKK73</i>). * indicates significant differences in comparison with the control at P < 0.05 respectively. Error bars indicate standard deviation for three replicates.</p