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Open Access Research article

High-resolution genome-wide scan of genes, gene-networks and cellular systems impacting the yeast ionome

Danni Yu1, John M C Danku2, Ivan Baxter3, Sungjin Kim4, Olena K Vatamaniuk4, Olga Vitek15, Mourad Ouzzani67 and David E Salt2*

Author Affiliations

1 Department of Statistics, Purdue University, West Lafayette, IN, USA

2 Institute of Biological and Environmental Science, University of Aberdeen, Scotland, UK

3 USDA-ARS Plant Genetics Research Unit, Donald Danforth Plant Science Center, St. Louis, MO, USA

4 Department of Crop and Soil Sciences, Cornell University, Ithaca, NY, USA

5 Department of Computer Science, Purdue University, West Lafayette, IN, USA

6 Cyber Center, Purdue University, West Lafayette, IN, USA

7 Current address: Qatar Computing Research Institute, Qatar Foundation, Doha, Qatar

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BMC Genomics 2012, 13:623  doi:10.1186/1471-2164-13-623

Published: 14 November 2012

Abstract

Background

To balance the demand for uptake of essential elements with their potential toxicity living cells have complex regulatory mechanisms. Here, we describe a genome-wide screen to identify genes that impact the elemental composition (‘ionome’) of yeast Saccharomyces cerevisiae. Using inductively coupled plasma – mass spectrometry (ICP-MS) we quantify Ca, Cd, Co, Cu, Fe, K, Mg, Mn, Mo, Na, Ni, P, S and Zn in 11890 mutant strains, including 4940 haploid and 1127 diploid deletion strains, and 5798 over expression strains.

Results

We identified 1065 strains with an altered ionome, including 584 haploid and 35 diploid deletion strains, and 446 over expression strains. Disruption of protein metabolism or trafficking has the highest likelihood of causing large ionomic changes, with gene dosage also being important. Gene over expression produced more extreme ionomic changes, but over expression and loss of function phenotypes are generally not related. Ionomic clustering revealed the existence of only a small number of possible ionomic profiles suggesting fitness tradeoffs that constrain the ionome. Clustering also identified important roles for the mitochondria, vacuole and ESCRT pathway in regulation of the ionome. Network analysis identified hub genes such as PMR1 in Mn homeostasis, novel members of ionomic networks such as SMF3 in vacuolar retrieval of Mn, and cross-talk between the mitochondria and the vacuole. All yeast ionomic data can be searched and downloaded at http://www.ionomicshub.org webcite.

Conclusions

Here, we demonstrate the power of high-throughput ICP-MS analysis to functionally dissect the ionome on a genome-wide scale. The information this reveals has the potential to benefit both human health and agriculture.

Keywords:
Ionome; Yeast; Clustering; Network analysis; Mitochondria; Vacuole; ESCRT; Genome-wide; ICP-MS; Ionomics