Comparative Analysis of Duckweed Cultivation with Sewage Water and SH Media for Production of Fuel Ethanol

<div><p>Energy crises and environmental pollution have caused considerable concerns; duckweed is considered to be a promising new energy plant that may relieve such problems. <i>Lemna aequinoctialis</i> strain 6000, which has a fast growth rate and the ability to accumulate high levels of starch was grown in both Schenk & Hildebrandt medium (SH) and in sewage water (SW). The maximum growth rates reached 10.0 g DW m⁻² day⁻¹ and 4.3 g DW m⁻² day⁻¹, respectively, for the SH and SW cultures, while the starch content reached 39% (w/w) and 34% (w/w). The nitrogen and phosphorus removal rate reached 80% (SH) and 90% (SW) during cultivation, and heavy metal ions assimilation was observed. About 95% (w/w) of glucose was released from duckweed biomass hydrolysates, and then fermented by Angel yeast with ethanol yield of 0.19 g g⁻¹ (SH) and 0.17 g g⁻¹ (SW). The amylose/amylopectin ratios of the cultures changed as starch content increased, from 0.252 to 0.155 (SH) and from 0.252 to 0.174 (SW). <i>Lemna aequinoctialis</i> strain 6000 could be considered as valuable feedstock for bioethanol production and water resources purification.</p></div

Ion concentrations in Schenk & Hildebrandt medium (SH) and sewage water (SW) before and after cultivation.

<p>Ion concentrations in Schenk & Hildebrandt medium (SH) and sewage water (SW) before and after cultivation.</p

Duckweed-mediated nitrogen removal from Schenk & Hildebrandt medium (SH) and sewage water (SW).

<p>Each data point represents the mean of triplicate values; error bars indicate the standard deviation.</p

Glucose released from the biomass of duckweed after enzymatic saccharification and ethanol yields of the fermentation in hydrolysates of duckweed biomass with Angel Yeast.

<p>Each data is the mean of three replicates ± standard deviation.</p><p>Glucose released from the biomass of duckweed after enzymatic saccharification and ethanol yields of the fermentation in hydrolysates of duckweed biomass with Angel Yeast.</p

Kinetics of starch content variation during duckweed cultivation in Schenk & Hildebrandt medium (SH) and sewage water (SW).

<p>Each data point represents the mean of triplicate values; error bars indicate the standard deviation.</p

Reducing sugar analysis of hydrolyzates of duckweed grown in Schenk & Hildebrandt medium (SH) and sewage water (SW). Glc: glucose, Gal: galactose, Man: mannose.

<p>Different letters indicate significant differences between different conditions (<i>p</i><0.05)</p><p>Reducing sugar analysis of hydrolyzates of duckweed grown in Schenk & Hildebrandt medium (SH) and sewage water (SW). Glc: glucose, Gal: galactose, Man: mannose.</p

Kinetics of duckweed growth in Schenk & Hildebrandt medium (SH) and sewage water (SW).

<p>Each data point represents the mean of triplicate values; error bars indicate the standard deviation.</p

Changes in the amylose, amylopectin, and starch content of <i>L. aequinoctialis</i> before and after cultivation in Schenk & Hildebrandt medium (SH) and sewage water (SW).

<p>All data are presented as the mean of triplicate measurements ± standard deviation. Different letters indicate significant differences between different conditions (<i>p</i><0.05).</p><p>Changes in the amylose, amylopectin, and starch content of <i>L. aequinoctialis</i> before and after cultivation in Schenk & Hildebrandt medium (SH) and sewage water (SW).</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

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