Hierarchical clustering and Tree View analysis.

<p> <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g006" target="_blank">Figure 6A</a>. The metabolism pathways analysis. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g006" target="_blank">Figure 6B</a>. The genetic information processing pathways analysis data. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g006" target="_blank">Figure 6C</a>. The Environmental information processing analysis. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g006" target="_blank">Figure 6D</a>. The cellular processes analysis <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g006" target="_blank">Figure 6E</a>. The Organismal systems analysis. The up regulated genes showed value over zero, while the down regulated genes showed value range from 0 to −2. Results showed that the most up regulated genes belongs to the following pathways: cellular process pathways, such as cell cycle and apoptosis; environmental information processing pathways, such as MAPK signaling pathway, TGF-beta signaling pathway; genetic information processing pathways such as ribosom; global pathways such as microbial metabolism in diverse environment; organismal systems pathways, such as T cell receptor signaling pathway, antigen processing and presentation molecules, while the most down regulated genes belongs to the following pathways: cellular process pathways, such as phagosome, endocytosis and autophagy; environmental information processing pathways, such as calcium signaling pathway; global pathway such as metabolic pathways; organismal systems pathways, such as Fc epsilon RI signaling pathway (as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g006" target="_blank">Figure 6</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone.0051988.s010" target="_blank">Table S3</a>, and <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone.0051988.s012" target="_blank">Table S5</a>). Results suggested that if we want to improve present tuberculosis therapy, we may pay more attention to the function of these DGEs to effectively control <i>Mycobacterium tuberculosis</i> infection.</p

DEGs of H37Rv-treated and BCG-treated libraries.

<p> The red and green bars represented the upregulated and downregulated DEGs in the H37Rv-treated library condition relative to the BCG-treated library, respectively. Results showed that the number of significantly DEGs between two samples was 1,917, where 1,135 genes and 782 genes were up-regulated and down-regulated, respectively.</p

Validations of DGE data via qPCR.

<p>The vertical axis indicated the fold change of the expressions of DEGs in the H37Rv-treated library compared with those in BCG-treated. DGE indicated the RNA samples from the pooling samples that were used. qPCR indicated the RNA samples from independent RNA extractions from biological replicates. The error bars represented SE. Pearson’s correlation coefficient (r) showed that both the DGE and qPCR data were highly correlated, where the DEGs had a high consistency and the r values ranging from 0.681 (TLR2) to 0.995 (MyD88) between the two methods suggesting that the DGE results are significant.</p

GO categories of the part unigenes.

<p>Red represented GO Component, blue represented GO Function; yellow represented GO Process. The DEGs from the selected 28 pathways were analyzed by the WEGO analysis. Results showed that about 468, 466, and 468 genes could be annotated in GO component, GO function, and GO process based on sequence homologies, respectively. In the three main categories (cellular component, molecular function, and biological process) of the GO classification, “cell,” “cell part,” “binding,” “catalytic,” “metabolic process,” and “cellular process” were dominant.</p

Major characteristic of DGE libraries and tag mapping to the UniGene transcript database.

<p>All Mapping represents the number of all tags mapped to the UniGene virtual tag database, clear-cut Mapping represents the number of clear-cut tags mapped to the UniGene virtual tag database, clear-cut tags indicate the tags matched only to one gene. Examination confirmed that the quality of the data were up to specification suggesting that DGE data are significant.</p

Different Transcriptional Profiles of RAW264.7 Infected with <em>Mycobacterium tuberculosis</em> H37Rv and <em>BCG</em> Identified via Deep Sequencing

<div><h3>Background</h3><p>The <em>Mycobacterium tuberculosis</em> H37Rv and BCG effects on the host cell transcriptional profile consider a main research point. In the present study the transcriptome profiling analysis of RAW264.7 either infected with <em>Mycobacterium tuberculosis</em> H37Rv or BCG have been reported using Solexa/Illumina digital gene expression (DGE).</p> <h3>Results</h3><p>The DGE analysis showed 1,917 different expressed genes between the BCG and H37Rv group. In addition, approximately 5% of the transcripts appeared to be predicted genes that have never been described before. KEGG Orthology (KO) annotations showed more than 71% of these transcripts are possibly involved in approximately 210 known metabolic or signaling pathways. The gene of the 28 pathways about pathogen recognition receptors and <em>Mycobacterium tuberculosis</em> interaction with macrophages were analyzed using the CLUSTER 3.0 available, the Tree View tool and Gene Orthology (GO). Some genes were randomly selected to confirm their altered expression levels by quantitative real-time PCR (qRT-PCR).</p> <h3>Conclusion</h3><p>The present study used DGE from pathogen recognition receptors and <em>Mycobacterium tuberculosis</em> interaction with macrophages to understand the interplay between <em>Mycobacterium tuberculosis</em> and RAW264.7. Meanwhile find some important host protein which was affected by <em>Mycobacterium tuberculosis</em> to provide evidence for the further improvement of the present efficacy of existing <em>Mycobacterium tuberculosis</em> therapy and vaccine.</p> </div

Categories of some DEGs based on KEGG.

<p>A) Global pathways; B) Metabolism pathways; C) Genetic information processing pathways; D) Environmental information processing pathways; E) Cellular processes pathways; F) Organismal Systems pathways. We from our point of view selected 28 pathways based on the potential role and interaction with <i>Mycobacterium tuberculosis</i>, where we tried to make the selection cover all the stages of infection and immune response, then made further study on the selected pathways, where we grouped it into six categories based on KEGG, and illustrate its distribution as shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g005" target="_blank">Figure 5</a>. Results suggested that in these 28 selected pathways the DGEs mainly distributed in global pathways, cellular processes pathways and organismal Systems pathways.</p

Distribution of gene expression (A) and tags (B) between experimental and control groups.

<p>Panel A represented the distribution of gene expression in the H37Rv-treated library (A1), BCG-treated library (A2), and control library (A3). Panel B represented the distribution of tags in the H37Rv-treated library (B1), BCG-treated library (B2), and uninfected library (B3). TPM represented tags per million. Results showed that the distribution of tags matched the distribution of genes for each group. Furthermore, the frequencies of tags or genes decreased with increasing number of tags or gene expression. Results suggested that these DGE dates are significant.</p

Categories of DEGs based on KEGG.

<p>The pathways distribution showed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone-0051988-g004" target="_blank">Figure 4:</a> among the 1,917 DEGs, 1,371 can be mapped into 210 pathways which be classified functionally into seven categories: global pathways, metabolism pathways, genetic information processing pathways, environmental information processing pathways, cellular processes pathways, organismal systems pathways, human diseases pathways based on the KEGG function classification, where the human diseases pathways showed highest amount of genes measured as 603 genes, while Organismal systems pathways were 562, Genetic information processing pathways were 385, metabolism pathways were 383, cellular processes were 361, environmental information processing pathways were 224 and Global pathways were 212. Results suggested that after H37Rv or BCG infecting macrophages or possibly DCs, the global pathway, metabolism, genetic information processing, environmental information processing, cellular processes, and organismal systems all adopt some changes as shown above.</p

<i>Mycobacterium tuberculosis</i>-influenced immune-relevant genes in RAW264.7.

<p>TPM represented tags per million. H37Rv (QD) represented the group in which RAW264.7 were treated with H37Rv; BCG (RD) represented the group in which RAW264.7 were treated with BCG. TPM represented tags per million. Symbol, Gene symbol (Brief gene description can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051988#pone.0051988.s009" target="_blank">Table S2</a>). Results showed that, the expression of some DEGs which involved in recognition and ingestion, complement receptor, cytokine and cytokine receptor, Endosome, Lysosome, lysosomal membrane protein, Lysosomal acid hydrolase and Fc gamma R-mediated phagocytosis, suggesting that these genes may be the involved in the main host protein which was affected by H37Rv or BCG.</p