Advanced search
Start date
Betweenand

Energetic metabolism analysis along culture growth of ARCELLA intermedia

Grant number: 17/16077-3
Support type:Scholarships abroad - Research Internship - Master's degree
Effective date (Start): October 08, 2017
Effective date (End): November 07, 2017
Field of knowledge:Biological Sciences - Genetics
Principal Investigator:Daniel José Galafasse Lahr
Grantee:Giulia Magri Ribeiro
Supervisor abroad: Laura Aline Katz
Home Institution: Instituto de Biociências (IB). Universidade de São Paulo (USP). São Paulo , SP, Brazil
Local de pesquisa : Smith College, United States  
Associated to the scholarship:16/14402-1 - Analysis of Arcella vulgaris expression profile along your growth curve in laboratory, BP.MS

Abstract

Microbial growth is a complex process involving numerous metabolic reactions that result in cell division. Typical growth curves have 4 phases: lag, log, stationary and decline. Arcella intermedia doesn't seem to follow exactly this same pattern of growth. In vitro, cell growth is dependent on several factors, such as pH, temperature, osmolality, gas concentration, available surface substrata, and state ofthe cells at inoculation. During in vitro cultivation of animal cells, any change relativeto the optimal environmental conditions may result in a rapid decrease in cell viability. All living cells do "work", and to do these jobs the cell needs energy. Metabolic pathways are regulated at various levels within the cell. And it was already shown that these regulations are greatly variable in protists. With RNAseqexperiments we can identify genes and active transcripts in cells. That is useful toidentify differentially expressed genes, which should be responding to environmentalsignals. In this project we intend to investigate the modifications of the energeticmetabolism throughout that lineage of amoebas growth. I intend to see either if this organism has 'textbook' metabolism or if it differs like other microbial eukaryotes examples. We already have generated transcriptome (RNAseq) data. We are going to do computational analysis pipeline to extract metabolism related genes and understand their expression pattern along amoeba growth curve. I intend to do this job in a month. (AU)