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:
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. To gain a systems-level understanding of these communities and processes, I collaborate with biogeochemists, modelers, and others.

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?
• TPP on Amazon Cloud FAQ

Proteomic Tools

• 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

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

Mass Spectrum Analysis

Basic peaks

Electron ionization mass spectra have several distinct sets of peaks: the molecular ion, isotope peaks, fragmentation peaks, and metastable peaks.

In the mass spectra the molecular ion peak is often most intense, but can be weak or missing. The molecular ion is a radical cation (M+.) as a result of removing one electron from the molecule. Identification of the molecular ion can be difficult. Examining organic compounds, the relative intensity of the molecular ion peak diminishes with branching and with increasing mass in a homologous series. In the spectrum for toluene for example, the molecular ion peak is located at 92 m/z corresponding to its molecular mass. Molecular ion peaks are also often preceded by a M-1 or M-2 peak resulting from loss of a hydrogen radical or dihydrogen.

The peak with the highest intensity is called the base peak which is not necessarily the molecular ion.

More peaks may be visible with m/z ratios larger than the molecular ion peak due to isotope distributions, called isotope peaks. The value of 92 in the toluene example corresponds to the monoisotopic mass of a molecule of toluene entirely composed of the most abundant isotopes (1H and 12C). The so-called M+1 peak corresponds to a fraction of the molecules with one higher isotope incorporated (2H or 13C) and the M+2 peak has two higher isotopes. The natural abundance of the higher isotopes is low for frequently encountered elements such as hydrogen, carbon and nitrogen and the intensity of isotope peaks subsequently low. In halogens on the other hand, higher isotopes have a large abundance which results in a specific mass signature in the mass spectrum of halogen containing compounds.

Peaks with mass less than the molecular ion are the result of fragmentation of the molecule. Many reaction pathways exist for fragmentation, but only newly formed cations will show up in the mass spectrum, not radical fragments or neutral fragments.

Metastable peaks are broad peaks with low intensity at non-integer mass values. These peaks result from ions with lifetimes shorter than the time needed to traverse the distance between ionization chamber and the detector.

Fragmentation

The fragmentation pattern of the spectra beside the determination of the molar weight of an unknown compound also suitable to give structural information, especially in combination with the calculation of the degree of unsaturation from the molecular formula (when available). Neutral fragments frequently lost are carbon monoxide, ethylene, water, ammonia, and hydrogen sulfide.

fragmentations arise from:

• homolysis (chemistry)|homolysis processes. An example is the cleavage of carbon-carbon bonds next to a heteroatom

fragmentation at heteroatom

In this depiction single-electron movements are indicated by a single-headed arrow.

• Rearrangement reactions, for example a retro Diels-Alder reaction extruding neutral ethylene:

Unsaturated ring fragmentation

or the McLafferty rearrangement. As it is not always obvious where a lone electron resides in a radical cation a square bracket notation is often used.

• Ion-neutal complex formation. This pathway involves bond homolysis or bond heterolysis, in which the fragments do not have enough kinetic energy to separate and, instead, reaction with one another like an ion-molecule reaction.

Some general rules:

• A useful aid is the nitrogen rule: if the m/z ratio is an even number, the compound contains no nitrogen or an even number of nitrogens.
• Cleavage occurs at alkyl substituted carbons reflecting the order generally observed in carbocations.
• Double bonds and arene compound|arene fragments tend to resist fragmentation.
• Allylic cations are stable and resist fragmentation.
• the even-electron rule stipulates that even-electron species (cations but not radical ions) will not fragment into two odd-electron species but rather to another cation and a neutral molecule.

Isotope effects

Isotope peaks within a spectra can help in structure elucidation. Compounds containing halogens (especially chlorine and bromine) can produce very distinct isotope peaks. The mass spectrum of methylbromide has two prominent peaks of equal intensity at m/z 94 (M) and 96 (M+2) and then two more at 79 and 81 belonging to the bromine fragment.

Even when compounds only contain elements with less intense isotope peaks (carbon or oxygen), the distribution of these peaks can be used to assign the spectrum to the correct compound. For example, two compounds with identical mass of 150 Da, C₈H₁₂N₃⁺ and C₉H₁₀O₂⁺, will have two different M+2 intensities which makes it possible to distinguish between them.

Natural abundance of some elements

The next table gives the isotope distributions for some elements. Some elements like phosphorus and fluorine only exist as a single isotope, with a natural abundance of 100%.

Natural abundance of some elements

Isotope	% nat. abundance	atomic mass
1H	99.985	1.007825
2H	0.015	2.0140
12C	98.89	12 (definition)
13C	1.11	13.00335
14N	99.64	14.00307
15N	0.36	15.00011
16O	99.76	15.99491
17O	0.04
18O	0.2	17.99916
28Si	92.23	27.97693
29Si	4.67	28.97649
30Si	3.10	29.97376
32S	95.0	31.97207
33S	0.76	32.97146
34S	4.22	33.96786
37Cl	24.23
35Cl	75.77	34.96885
79Br	50.69	78.9183
81Br	49.31	80.9163