User:Eun-Hae Kim

Eun-Hae "EK" Kim, Ph.D.

Eun-Hae "EK" Kim

Mailing Address:
Dr. Eun-Hae Kim
1177 E Fourth St
Tucson, AZ 85721-0038

or

P.O. Box 210038
Tucson, AZ 85721-0038

Physical Address:
Saguaro Hall Rm 315
1110 E. South Campus
Tucson, AZ 85721

Laboratory:
SWES-MEL (Soil, Water, and Environmental Science - Microbial Ecology Laboratory)
Saguaro Hall Rm 301

Email: eunhae.kim at arizona dot edu

Education

Ph.D., Environmental Science, Biochemistry

University of Arizona

Dept of Biochemistry and Molecular Biophysics and Soil, Water, and Environmental Science

Integrating an interdisciplinary approach of comparative genomics, molecular microbiology,

and biochemistry to better understand mechanisms of metal transport systems in bacteria.

M.S., Microbiology

University of Nevada, Las Vegas

School of Life Sciences

Elucidation of the roles and regulation of virulence factors in bacterial intracellular pathogens by

employing biochemical and genetic methods.

B.S., Biological Sciences

University of Southern California

Wrigley Institute of Environmental Studies

Characterization of microbial communities in aquatic and terrestrial environments on Santa Catalina

Island utilizing 16S rRNA genes as a phylogenetic marker.

Research interests

I graduated from the University of Southern California with a B.S. degree in Biological Sciences.

I was afforded the opportunity to do some really awesome field research at the USC Wrigley Institute for Environmental Studies where I studied phylogenetics and phylogeography of microbial populations around Catalina Island.

I then moved to the city that never sleeps, Las Vegas, NV where I obtained my Masters of Science degree in Microbiology. It was at UNLV where my work was extended from molecular biology to honing the skills necessary for employing biochemical methodologies. The focus of my research was analyzing virulence factors of the bacterial pathogen, Shigella.

I had a passion for creative and critical thinking and decided to continue my graduate career by obtaining a Ph.D. On an interview at the University of Arizona in the great Sonoran desert, I had arrived at an opportune time during monsoon season, which instantly made me fall in love with Tucson. I obtained my Doctorate degree at the University of Arizona in Environmental Science with a focus in Biochemistry. As a Ph.D. student, my research integrated a multidisciplinary approach of comparative genomics, molecular biology, and biochemistry to better understand mechanisms of metal homeostasis in microorganisms.

These acquired biochemical tools now have led me to the incredible field of proteomics, specifically community proteomics. My research focuses on how microbial communities impact biogeochemistry and global change.

I use the techniques of molecular microbial ecology and biochemistry via metagenomics and metaproteomics to examine microbial community interactions within populations and their environment, specifically in critical terrestial environments.

A driving question of my research is: What is the role microbes play in carbon gas emissions from thawing permafrost?

Publications

1. Kim EH and Rensing C. Genome of halomonas strain GFAJ-1, a blueprint for fame or business as usual. J Bacteriol. 2012 Apr;194(7):1643-5. DOI:10.1128/JB.00025-12 |
2. Liu G, Liu M, Kim EH, Maaty WS, Bothner B, Lei B, Rensing C, Wang G, and McDermott TR. A periplasmic arsenite-binding protein involved in regulating arsenite oxidation. Environ Microbiol. 2012 Jul;14(7):1624-34. DOI:10.1111/j.1462-2920.2011.02672.x |
3. Kim EH, Nies DH, McEvoy MM, and Rensing C. Switch or funnel: how RND-type transport systems control periplasmic metal homeostasis. J Bacteriol. 2011 May;193(10):2381-7. DOI:10.1128/JB.01323-10 |

   Selected as high impact publication by ASM Press and included in Journal Highlights section in Microbe Magazine, June 2011

4. Conroy O, Kim EH, McEvoy MM, and Rensing C. Differing ability to transport nonmetal substrates by two RND-type metal exporters. FEMS Microbiol Lett. 2010 Jul;308(2):115-22. DOI:10.1111/j.1574-6968.2010.02006.x |
5. Kim EH, Rensing C, and McEvoy MM. Chaperone-mediated copper handling in the periplasm. Nat Prod Rep. 2010 May;27(5):711-9. DOI:10.1039/b906681k |
6. Kim EH, Charpentier X, Torres-Urquidy O, McEvoy MM, and Rensing C. The metal efflux island of Legionella pneumophila is not required for survival in macrophages and amoebas. FEMS Microbiol Lett. 2009 Dec;301(2):164-70. DOI:10.1111/j.1574-6968.2009.01813.x |

Useful links

• Using the HPC
• OpenWetWare help pages

Proteomics Links

• Mass Spectrometry Analysis
• Mass Spectrometry Software
• Mass Spectrometry Data Format
• ORNL OBMS ASMS Posters
• What exactly is a 'virtual core' on Amazon EC2?
• Amazon Instance Types
• TPP on Amazon Cloud FAQ
• Pages on Protein

Proteomic Tools

• Ben's spectra divvying Script
• SequenceServer
• Trans-Proteomic Pipeline (TPP)
• Scaffold Download
• Proteome Software: Scaffold Download
• PNNL MS Tools
• Software from the Tabb Lab
• The Global Proteome Machine: Tutorial
• The Chorus Project
• ProteoWizard
• Venn Diagram Plotter
• SignalP
• ProtBase
• Fasta Converters
• Prediction Tools
• msconvert wine
• STRING known protein-protein interactions

Search Engines

• X!Tandem
• XTandem Parameters

Proteomic References

General Label-Free Statistics
(1) Detecting Differential and Correlated Protein Expression in Label-Free Shotgun Proteomics

1. Zhang B, VerBerkmoes NC, Langston MA, Uberbacher E, Hettich RL, and Samatova NF. Detecting differential and correlated protein expression in label-free shotgun proteomics. J Proteome Res. 2006 Nov;5(11):2909-18. DOI:10.1021/pr0600273 |

(2) Significance analysis of spectral count data in label-free shotgun proteomics

2. Choi H, Fermin D, and Nesvizhskii AI. Significance analysis of spectral count data in label-free shotgun proteomics. Mol Cell Proteomics. 2008 Dec;7(12):2373-85. DOI:10.1074/mcp.M800203-MCP200 |

(3) Mass spectrometry-based label-free quantitative proteomics

3. Zhu W, Smith JW, and Huang CM. Mass spectrometry-based label-free quantitative proteomics. J Biomed Biotechnol. 2010;2010:840518. DOI:10.1155/2010/840518 |

(4) Less label, more free: Approaches in label‐free quantitative mass spectrometry

4. Neilson KA, Ali NA, Muralidharan S, Mirzaei M, Mariani M, Assadourian G, Lee A, van Sluyter SC, and Haynes PA. Less label, more free: approaches in label-free quantitative mass spectrometry. Proteomics. 2011 Feb;11(4):535-53. DOI:10.1002/pmic.201000553 |

(5) Statistical similarities between transcriptomics and quantitative shotgun proteomics data

5. Pavelka N, Fournier ML, Swanson SK, Pelizzola M, Ricciardi-Castagnoli P, Florens L, and Washburn MP. Statistical similarities between transcriptomics and quantitative shotgun proteomics data. Mol Cell Proteomics. 2008 Apr;7(4):631-44. DOI:10.1074/mcp.M700240-MCP200 |

(6) PatternLab for proteomics: a tool for differential shotgun proteomics

6. Carvalho PC, Fischer JS, Chen EI, Yates JR 3rd, and Barbosa VC. PatternLab for proteomics: a tool for differential shotgun proteomics. BMC Bioinformatics. 2008 Jul 21;9:316. DOI:10.1186/1471-2105-9-316 |

(7) Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

7. Wong JW, Sullivan MJ, and Cagney G. Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments. Brief Bioinform. 2008 Mar;9(2):156-65. DOI:10.1093/bib/bbm046 |

BIG ONE (8) The effects of shared peptides on protein quantitation in label-free proteomics by LC/MS/MS

(9) Peek a peak: a glance at statistics for quantitative label-free proteomics

(10) Relative, label-free protein quantitation: spectral counting error statistics from nine replicate MudPIT samples

(11) An assessment of false discovery rates and statistical significance in label-free quantitative proteomics with combined filters

(12) PepC: proteomics software for identifying differentially expressed proteins based on spectral counting

(13) Quantitative mass spectrometry in proteomics: a critical review

(14) The Spectra Count Label-free Quantitation in Cancer Proteomics

PROBABLY VERY GOOD (15) Refinements to label free proteome quantitation: how to deal with peptides shared by multiple proteins

(16) Significance analysis of spectral count data in label-free shotgun proteomics

(17) Label-free, normalized quantification of complex mass spectrometry data for proteomic analysis

HERE IS FIRST PAPER ON SPECTRAL COUNTS!!! Must reference this (18) A model for random sampling and estimation of relative protein abundance in shotgun proteomics

other papers on Spectral Counting including NSAF (19) Role of spectral counting in quantitative proteomics

NSAF PAPER MUST REFERENCE: (20) Analyzing chromatin remodeling complexes using shotgun proteomics and normalized spectral abundance factors

the first very good paper comparing methods for label free quant (21) Comparison of label-free methods for quantifying human proteins by shotgun proteomics

APPLICATIONS: (22) Statistical Analysis of Membrane Proteome Expression Changes in Saccharomyces c erevisiae

(23) A label free quantitative proteomic analysis of the< i> Saccharomyces cerevisiae nucleus

(24) Differential quantitative proteomics of Porphyromonas gingivalis by linear ion trap mass spectrometry

(25) Community genomic and proteomic analyses of chemoautotrophic iron-oxidizing “Leptospirillum rubarum”(group II) and “Leptospirillum ferrodiazotrophum”(group III)

(26) Proteogenomic basis for ecological divergence of closely related bacteria in natural acidophilic microbial communities

(27) Shotgun metaproteomics of the human distal gut microbiota

(28) Metaproteomics of a gutless marine worm and its symbiotic microbial community reveal unusual pathways for carbon and energy use

Instrument concerns: (29) Effect of dynamic exclusion duration on spectral count based quantitative proteomics

Proteomic Papers Search Algorithms

(1) Kapp et al 2005, Search Algorithm Summary

Scripts

Extract_from_fasta