Early inflammatory reactions after receiving the marginal number of islets into the liver, beneath the kidney capsules, or into the spleen.

<p><b>(A–C)</b> Plasma MCP-1, G-CSF, and HMGB1 levels were measured 6 hours after islet transplantation into the portal vein (PV), beneath the kidney capsule (KC), or into the spleen (SP) (n = 7). Untreated naïve mice were used as a control (n = 7). Values are means±SD. *p<0.05. <b>(D)</b> Photomicrographs of islet cells after transplantation in the PV, KC, or SP. Sections were stained with anti-Gr-1 or F4/80 followed by staining with Haematoxylin. Scale bars: 100 μm.</p

Tlx1-related gene expression analysis.

<p><b>(A)</b> Tlx1 (Hox11)-related gene expression levels were significantly altered. A hierarchical clustering image reveals differences between Samples 1, 2, and 3. <b>(B)</b> Photomicrographs of transplanted islet cells in the spleen on days 0, 154, and 290. Sections were stained with anti-insulin antibody (green) and anti-Rrm2b antibody (red). Scale bars: 100 μm. The yellow boxed regions in the second column were enlarged, and the scale bars are 200 μm. <b>(C)</b> Photomicrographs of transplanted islet cells in the spleen on days 0, 154, and 290. Sections were stained with anti-insulin antibody (green) and anti-Pla2g2d antibody (red). Scale bars: 100 μm. The yellow boxed regions in the second column were enlarged, and the scale bars are 200 μm.</p

Long-term effects of intra-splenic islet transplantation.

<p><b>(A)</b> Non-fasting blood glucose levels in STZ-induced diabetic mice (C57BL/6) transplanted with 25 syngeneic islets in the spleen (SP) and 100 syngeneic islets under the kidney capsule (KC). Individual lines represent glucose levels in each animal. <b>(B)</b> Photomicrographs of transplanted islet cells in the SP on day 290. Sections were stained with anti-insulin antibody followed by hematoxylin. Scale bars: 100 μm. <b>(C)</b> Insulin content was measured after islet transplantation on days 0 (n = 6) and 280 (n = 6). Values are means±SD. *p<0.0001.</p

Simultaneous RNA-Seq Analysis of a Mixed Transcriptome of Rice and Blast Fungus Interaction

<div><p>A filamentous fungus, <em>Magnaporthe oryzae</em>, is a causal agent of rice blast disease, which is one of the most serious diseases affecting cultivated rice, <em>Oryza sativa</em>. However, the molecular mechanisms underlying both rice defense and fungal attack are not yet fully understood. Extensive past studies have characterized many infection-responsive genes in the pathogen and host plant, separately. To understand the plant-pathogen interaction comprehensively, it is valuable to monitor the gene expression profiles of both interacting organisms simultaneously in the same infected plant tissue. Although the host-pathogen interaction during the initial infection stage is important for the establishment of infection, the detection of fungal gene expression in infected leaves at the stage has been difficult because very few numbers of fungal cells are present. Using the emerging RNA-Seq technique, which has a wide dynamic range for expression analyses, we analyzed the mixed transcriptome of rice and blast fungus in infected leaves at 24 hours post-inoculation, which is the point when the primary infection hyphae penetrate leaf epidermal cells. We demonstrated that our method detected the gene expression of both the host plant and pathogen simultaneously in the same infected leaf blades in natural infection conditions without any artificial treatments. The upregulation of 240 fungal transcripts encoding putative secreted proteins was observed, suggesting that these candidates of fungal effector genes may play important roles in initial infection processes. The upregulation of transcripts encoding glycosyl hydrolases, cutinases and LysM domain-containing proteins were observed in the blast fungus, whereas pathogenesis-related and phytoalexin biosynthetic genes were upregulated in rice. Furthermore, more drastic changes in expression were observed in the incompatible interactions compared with the compatible ones in both rice and blast fungus at this stage. Our mixed transcriptome analysis is useful for the simultaneous elucidation of the tactics of host plant defense and pathogen attack.</p> </div

Confirmation of differential expression of known infection-responsive fungal genes by qRT-PCR and RNA-Seq.

<p>The expression profiles of known infection-responsive fungal genes, biotrophy-associated secreted (BAS) proteins (A) 1, (B) 2, (C) 3 and (D) 4, were examined by both qRT-PCR and RNA-Seq techniques. For the qRT-PCR data, the mean±standard errors for the three replicates are represented. The statistically significant differential gene expression between the control (Cont) and infected (Inf) samples for the compatible (COM) and incompatible (INC) interactions were tested by the Student's <i>t</i>-test and G-test for qRT-PRC and RNA-Seq, respectively. The asterisks show the statistical significances (*: <i>P</i><0.05, **:<i>P</i> <0.01 and ***: <i>P</i><0.001).</p

Distribution of fold-changes of rice and fungal significantly upregulated transcripts.

<p>The fold-changes (log₂) of the significantly upregulated transcripts in the compatible and incompatible interactions are plotted for (A) rice and (B) blast fungus. The colors represent the types of interactions in which the upregulations occurred (black: Common, blue: COM-specific, red: INC-specific).</p

Schematic representation of RNA-seq analysis of mixed transcriptome obtained from blast fungus-infected rice leaves.

<p>First, mRNA were extracted from the <i>Oryza sativa</i> ssp. <i>japonica</i> cv. Nipponbare (Pia) rice leaf blades 24 hours after water treatment (rice, control) and inoculation (rice+blast fungus, infected, 24hpi), and also from conidial suspensions of the compatible and incompatible blast strains (blast fungus, control). RNA-Seq were conducted for each sample using the illumine GAIIx sequencer. In the preprocessing of reads, low quality bases, adapter sequences, rRNA sequences and too short reads (<20 bp) were removed. For the rice analysis, all of the preprocessed reads were mapped to the fungal genome to filter out contaminated fungal reads. For the fungal analysis, contaminated rice reads were removed by mapping all of the reads against the rice genome. Finally, all of the filtered reads were mapped to the reference genomes by TopHat and transcript structures are predicted by Cufflinks. For each rice and fungal transcript, expression levels were estimated using the numbers of uniquely mapped reads to the transcript structures.</p

Additional file 2: Table S1. of The Nipponbare genome and the next-generation of rice genomics research in Japan

Rice genes reported by Japanese researchers in various scientific journals in 2005–2014. Literatures were searched and obtained from PubMed with ‘rice’ and ‘Oryza’ as keywords in either the title or abstract, and further selected by natural language processing and manual curation. The data can be accessed from Oryzabase ( http://shigen.nig.ac.jp/rice/oryzabase/download/reference ). (XLSX 215 kb

Highly upregulated (fold change≥150) rice genes.

<p>Highly upregulated (fold change≥150) rice genes.</p

Distributions of expression levels for rice and fungal annotated transcripts.

<p>The distributions of the expression levels (log₂ RPKMs) are shown for rice (A, B) and blast fungus (C, D). For rice, (A) the control (NPB) and infected (NPB+Ina86-137) samples in the compatible interaction and (B) the control (NPB) and infected (NPB+P91-15B) samples in the incompatible interaction are compared. For blast fungus, (C) the control (Ina86-137) and infected (NPB+Ina86-137) samples in the compatible interaction and (D) the control (P91-15B) and infected (NPB+P91-15B) samples in the incompatible interaction are compared. Significantly differentially expressed transcripts between the control and infected conditions are plotted in colors (red: upregulated, blue: downregulated). Pearson's correlation coefficients (<i>R</i>²) between replicates are presented.</p