Identification of miRNAs and their targets by high-throughput sequencing and degradome analysis in cytoplasmic male-sterile line NJCMS1A and its maintainer NJCMS1B of soybean

Table S1. Summary of small RNA annotations from NJCMS1A and NJCMS1B. Table S2. Known miRNAs identified in NJCMS1A and NJCMS1B. Table S3. Family member distribution in conserved miRNA families. Table S4. Summary of miRNA families found in NJCMS1A and NJCMS1B. Table S5. Novel miRNAs on the other arm of known pre-miRNAs. Table S6. Novel miRNAs identified in NJCMS1A and NJCMS1B. Table S7-1. High-confidence known miRNAs identified in NJCMS1A and NJCMS1B. Table S7-2. High-confidence novel miRNAs identified in NJCMS1A and NJCMS1B. Table S8-1. The up-regulated miRNAs identified in NJCMS1A and NJCMS1B. Table S8-2. The down-regulated miRNAs identified in NJCMS1A and NJCMS1B. Table S9. The targets of miRNAs identified in NJCMS1A and NJCMS1B. Table S10. Targets of novel miRNAs in NJCMS1A and NJCMS1B. Table S11. Primers used in this study. (ZIP 637 kb

Comparative Transcriptome Analysis between the Cytoplasmic Male Sterile Line NJCMS1A and Its Maintainer NJCMS1B in Soybean (<i>Glycine max</i> (L.) Merr.)

<div><p>Background</p><p>The utilization of soybean heterosis is probably one of the potential approaches in future yield breakthrough as was the situation in rice breeding in China. Cytoplasmic male sterility (CMS) plays an important role in the production of hybrid seeds. However, the molecular mechanism of CMS in soybean remains unclear.</p><p>Results</p><p>The comparative transcriptome analysis between cytoplasmic male sterile line NJCMS1A and its near-isogenic maintainer NJCMS1B in soybean was conducted using Illumina sequencing technology. A total of 88,643 transcripts were produced in Illumina sequencing. Then 56,044 genes were obtained matching soybean reference genome. Three hundred and sixty five differentially expressed genes (DEGs) between NJCMS1A and NJCMS1B were screened by threshold, among which, 339 down-regulated and 26 up-regulated in NJCMS1A compared to in NJCMS1B. Gene Ontology (GO) annotation showed that 242 DEGs were annotated to 19 functional categories. Clusters of Orthologous Groups of proteins (COG) annotation showed that 265 DEGs were classified into 19 categories. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that 46 DEGs were assigned to 33 metabolic pathways. According to functional and metabolic pathway analysis combined with reported literatures, the relations between some key DEGs and the male sterility of NJCMS1A were discussed. qRT-PCR analysis validated that the gene expression pattern in RNA-Seq was reliable. Finally, enzyme activity assay showed that energy supply was decreased in NJCMS1A compared to in NJCMS1B.</p><p>Conclusions</p><p>We concluded that the male sterility of NJCMS1A might be related to the disturbed functions and metabolism pathways of some key DEGs, such as DEGs involved in carbohydrate and energy metabolism, transcription factors, regulation of pollen development, elimination of reactive oxygen species (ROS), cellular signal transduction, and programmed cell death (PCD) etc. Future research will focus on cloning and transgenic function validation of possible candidate genes associated with soybean CMS.</p></div

Function classification in Clusters of Orthologous Groups of proteins (COG) of differentially expressed genes between NJCMS1A and NJCMS1B.

<p>Capital letters on X-axis indicated the COG categories as listed on the right of the histogram; Y-axis indicated the number of differentially expressed genes.</p

Enzyme activity assay in NJCMS1A and NJCMS1B.

<p>The black column represented NJCMS1A and gray column represented NJCMS1B on X-axis; Y-axis represented the enzyme activity. (A) Total ATPase activity and (B) Sucrose phosphate synthase (SPS) activity. The data were given as Mean ± SD from three biological replicates.</p

KEGG pathways enriched of differentially expressed gene (DEGs) between NJCMS1A and NJCMS1B (core DEGs).

<p>^aLog₂FC ≥ 1 represented up-regulated and Log₂FC ≤ -1 represented down-regulated.</p><p>^<b>b</b>Regulation direction of DEGs (NJCMS1B was the control).</p><p>^<b>c</b><i>P</i> value of ≤ 0.05 was considered statistically significant.</p><p>KEGG pathways enriched of differentially expressed gene (DEGs) between NJCMS1A and NJCMS1B (core DEGs).</p

DEGs confirmed by qRT-PCR using the same sample as that in RNA-seq.

<p>X-axis represented gene name, the blue column represented qRT-PCR results, the red column represented RNA-seq results, and gray column represented CK (NJCMS1B); Y-axis represented the relative level of gene expression. Gene-specific qRT-PCR primers and gene name were listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126771#pone.0126771.s005" target="_blank">S5</a>-1 Table. All qRT-PCR reactions were performed with three biological replicates.</p

Saturation analysis of sequencing data of NJCMS1A and NJCMS1B.

<p>X-axis represented the percentage of mapped reads to soybean genome (%); Y-axis represented the fraction of genes within 15% of quantitative deviation. Each color line represented the saturation curve of different gene expression level, and the gene number within different FPKM interval was displayed in the lower right corner.</p

DEGs confirmed by qRT-PCR using different sample from that in RNA-seq.

<p>X-axis represented gene name, the blue column represented qRT-PCR results, the red column represented RNA-seq results, and gray column represented CK (NJCMS1B); Y-axis represented the relative level of gene expression. Gene-specific qRT-PCR primers and gene name were listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0126771#pone.0126771.s005" target="_blank">S5</a>-2 Table. All qRT-PCR reactions were performed with three biological replicates.</p

Nebula-I: A General Framework for Collaboratively Training Deep Learning Models on Low-Bandwidth Cloud Clusters

The ever-growing model size and scale of compute have attracted increasing interests in training deep learning models over multiple nodes. However, when it comes to training on cloud clusters, especially across remote clusters, huge challenges are faced. In this work, we introduce a general framework, Nebula-I, for collaboratively training deep learning models over remote heterogeneous clusters, the connections between which are low-bandwidth wide area networks (WANs). We took natural language processing (NLP) as an example to show how Nebula-I works in different training phases that include: a) pre-training a multilingual language model using two remote clusters; and b) fine-tuning a machine translation model using knowledge distilled from pre-trained models, which run through the most popular paradigm of recent deep learning. To balance the accuracy and communication efficiency, in Nebula-I, parameter-efficient training strategies, hybrid parallel computing methods and adaptive communication acceleration techniques are jointly applied. Meanwhile, security strategies are employed to guarantee the safety, reliability and privacy in intra-cluster computation and inter-cluster communication. Nebula-I is implemented with the PaddlePaddle deep learning framework, which can support collaborative training over heterogeneous hardware, e.g. GPU and NPU. Experiments demonstrate that the proposed framework could substantially maximize the training efficiency while preserving satisfactory NLP performance. By using Nebula-I, users can run large-scale training tasks over cloud clusters with minimum developments, and the utility of existed large pre-trained models could be further promoted. We also introduced new state-of-the-art results on cross-lingual natural language inference tasks, which are generated based upon a novel learning framework and Nebula-I.Comment: 20 pages, 10 figures, technical repor