Modulating MIOX2 expression in Nicotiana tabacum and impacts on genes involved in cell wall biosynthesis

Cell walls are essential structures for plant development and growth. Apart from its biological functions, the polysaccharides that make cell walls (cellulose, hemicellulose and pectins) are the principal natural fibrous materials, considered the most important renewable resource on earth, used as raw material for many industrial processes among them, for pulp and paper production, charcoal, and biofuels. For all these reasons, the study of molecular composition and biosynthesis of plant cell walls has been a matter of great interest for researchers over the past few years. In this context, a full-length cDNA fragment of Miox2 gene was cloned from Arabidopsis seedlings, using RT-PCR, with an open reading frame of 954 pb and a corresponding protein subunit molecular mass of 37 kDa. The deduced amino acid sequence of the cDNA showed a high degree of homology with myo-Inosytol oxygenases from other organisms. This cDNA was used for genetic transformation of model plants (tobacco), which expressed either antisense or sense RNA. Transgenic homozygous tobacco model plants with either repression or constitutively expressed Miox2 were obtained with the number of copies varying from 1 to 7. Neither, the repression of the endogenous tobacco Miox genes or the constitutive expression of Miox2 gene, caused major impact on plant development, leaf morphology or flowering time. There was however, statistically significant (P<0.05) changes in the arabinan and D-galacturonate contents. These results clearly indicate that the modulation of the myo-Inositol pathway caused no major impact on cell wall polysaccharide biosynthesis.As paredes celulares vegetais são estruturas essenciais para o crescimento e desenvolvimento das plantas. Além de suas diversas funções biológicas, os componentes polissacarídicos constituintes das paredes celulares (celulose, hemiceluloses e pectinas) são de vital importância como fonte natural de fibras, sendo consideradas as fontes principais de recursos renováveis do planeta, utilizados como matéria prima para diversos processos industriais, dentre eles, a produção de papel e celulose, carvão vegetal e biocombustíveis. Todos esses fatores têm despertado grande interesse no estudo da composição e biossíntese das paredes celulares. Neste contexto, um fragmento de cDNA do gene Miox2 foi clonado de plântulas de Arabidopsis, via RT-PCR, com uma região aberta de leitura de 954 pb e sua proteína com massa molecular de 37kDa. A sequência deduzida de aminoácidos do cDNA apresentou alto grau de identidade com mio-Inositol oxigenases de outros organismos. Este cDNA foi usado para transformação genética de plantas modelo (tabaco) que produziram RNA antisense ou sense. Plantas de tabaco homozigotas para o transgene com repressão ou expressão constitutiva do gene Miox2 foram obtidas com um número de cópias do transgene, variando de 1 a 7. A repressão do gene Miox de tabaco endógeno assim como a expressão constitutiva do gene Miox2 de Arabidopsis não causaram alterações no desenvolvimento, morfologia foliar ou tempo de florescimento das plantas. Entretanto, alterações estatisticamente significativas (P<0.05) ocorreram no conteúdo de arabinana e de D-galacturonato. Estes resultados indicam que a modulação do metabolismo do mio-Inositol não causou grandes impactos na biossíntese dos polissacarídeos da parede celular.Fil: Defávari Nascimento, D.. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Conti, Gabriela. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Labate, Mônica T. V.. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Gutmanis, Gunta. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Bertolo, Ana L. F.. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: de Andrade, Alexander. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Bragatto, Juliano. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Pagotto, Luís Otávio. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Damin, Plínio. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Moon, David H.. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; BrasilFil: Labate, Carlos A.. Universidade do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz; Brasi

Metaproteomic Analysis of the Anaerobic Community Involved in the Co-Digestion of Residues from Sugarcane Ethanol Production for Biogas Generation

The proteomics analysis could contribute to better understand about metabolic pathways in anaerobic digestion community because it still as a &ldquo;black-box&rdquo; process. This study aimed to analyze the proteins of the anaerobic co-digestion performed in reactors containing residues from the first and second generation ethanol production. Metaproteomics analysis was carried out for three types of samples: anaerobic sludge without substrate (SI), semi-continuous stirred reactor (s-CSTR) with co-digestion of filter cake, vinasse, and deacetylation liquor (R-CoAD) and s-CSTR with co-digestion of these aforementioned residues adding Fe3O4 nanoparticles (R-NP). The R-CoAD reactor achieved 234 NmLCH4 gVS&minus;1 and 65% of CH4 in the biogas, while the R-NP reactor reached 2800 NmLCH4 gVS&minus;1 and 80% of CH4. The main proteins found were enolase, xylose isomerase, pyruvate phosphate dikinase, with different proportion in each sample, indicating some change in pathways. However, according to those identified proteins, the main metabolic routes involved in the co-digestion was the syntrophic acetate oxidation coupled with hydrogenotrophic methanogenesis, with the CH4 production occurring preferentially via CO2 reduction. These findings contributed to unravel the anaerobic co-digestion at a micromolecular level, and may select a more appropriate inoculum for biogas production according to that residue, reducing reaction time and increasing productivity

Correlation between RNA-Seq and RT-qPCR data.

<p>Twelve up-regulated and five down-regulated genes were selected for validation and a Pearson's correlation was calculated base on the expression values generated for each AtUCP1 OE line (P07 and P32).</p

RT-qPCR validation of a set of differentially expressed genes.

<p>(A) Validation of the selected up-regulated genes. (B) Validation of the selected down-regulated genes. Assays were performed using the same RNA samples used for RNA-sequencing of both OE lines (P07 and P32) and WT expression was set to 1. Bars indicate standard errors of triplicate reactions (* means p<0.05).</p

ATP content in intact seedlings of wild-type (WT) and AtUCP1 OE lines (3-week-old).

<p>The data are representative of nine replicates. Means with the same letter are not significantly different (t test; p<0.05).</p

Enriched network based on GO terms in the up-regulated gene dataset associated with photosynthesis (A), carbon metabolism (B) and biotic and cold stress responses (C).

<p>Data were analyzed using the Biological Networks Gene Ontology tool (BiNGO). The area of a node is proportional to the number of up-regulated genes annotated to the corresponding GO category. Node color means enrichment significance <i>i</i>.<i>e</i>. darker the color on a color scale ranging from white (no significant) to orange (FDR-corrected p-value = 3 x 10⁻¹⁰), higher is the enrichment significance. White color nodes are not enriched but show the hierarchical relationship among the enriched ontology branches.</p

Assembling parameters.

<p>N50 = statistically weighted average such that 50% of the entire assembly is formed by contigs of equal size or larger than this value.</p><p>Assembling parameters.</p

R² linear regression of the RNA-Seq data from both OE lines (P07 and P32).

<p>The R² values were calculated using the Sigma Stat package based on the RPKM values derived from RNA-Seq data and after eliminating genes with zero count.</p

Gene Ontology (GO) analysis of the differentially expressed genes.

<p>Distribution histograms within the three main GO categories (in red “molecular function”, in blue “biological process” and in green “cellular component”) are shown for the up- (A) and down-regulated (B) genes. Only differentially expressed genes that were in common between the two AtUCP1 OE lines (P07 and P32) were included in the analysis.</p