Monitoring the microbial diversity and functional activities in response to land-use changes and deforestation under soybean and sugarcane cultivations

Abstract

With the advent of new technologies and the optimization of the conventional methods, various approaches to studying gene expression are expanding the field of microbiology and molecular biology. Methods used for RNA extraction, DNA microarrays, Real-Time PCR, competitive RT-PCR, stable isotope probing and the use of reporter genes provide the strategies for detecting and quantifying gene expression. Through the use of these methods, one can study the influence of soil environmental factors such as nutrients, oxygen status, pH, pollutants, agro-chemicals, moisture and temperature on gene expression and some of the mechanisms involved in the responses of cells to their environment within our tropical ecosystems. The organisms being responsible for these ecosystem services are mainly Bacteria, Fungi and Archaea. However the relationships between the microbial community structure and the turnover processes (microbial community function) are poorly understood in tropical soils. The participants of this proposal will develop studies using biochemical and molecular tools to evaluate the impacts of the greenhouse gas emissions/sequestration due to land-use under soybean and sugarcane cultivations at three sites of agricultural expansion areas, which include Mato Grosso (MT, MS), Rondonia and Sao Paulo. Our purpose is to integrate researchers from different areas of agronomy, soil chemistry and microbiology, molecular biology and bioinformatics in an effort to detect, quantify and correlate the microbial processes involved in the C and N biogeochemical turnover in soybean and sugarcane cultivations, under undisturbed areas (forest) and under two agricultural system - conventional and sustainable agriculture (minimum tillage and mulching practices). Using three estimators of diversity (rarefaction, Chao1, ACE), it is proposed a high throughput DNA pyrosequencing and statistical inference to assess bacterial and Archaea diversity in the soils. (AU)