Xiang Zhang, PhD
Department of Chemistry
Center for Regulatory and Environmental Analytical Metabolomics
University of Louisville, Louisville, KY 40292 xiang.zhang@louisville.edu

is a field in biology aiming at systems level understanding of biological processes, where a bunch of parts that are connected to one another and work together. It attempts to create predictive models of cells, organs, biochemical processes and complete organisms.
• Integrative systems biology
Extracting biological knowledge from the ‘omics through integration
• Predictive systems biology
Predicting future of biosystem using
‘omics knowledge, e.g. in-silico biosystems
Davidov, E.; Clish, C. B.; Oresic, M.; Zhang, X; et al. Omics: A Journal of Integrative Biology. 2004, 8, 267-288.
Clish, C. B.; Davidov, E.; Oresic, M.; Zhang, X; et al. Omics: A Journal of Integrative Biology. 2004, 8, 3-13.

Differential omics is the beginning of
Systems Biology molecule cell tissue organism
…

1.
Differential proteomics and metabolomics are qualitative and quantitative comparison of proteome and metabolome under different conditions that should unravel complex biological processes
2.
It can be used to study any scientific phenomena that may change the proteome and/or metabolome of a living system.
NIH
 Cancer Biomarker Discovery

Nano-medicine
 Environment

Food and nutrition preventative medicine

Biomarkers are naturally occurring biomolecules useful for measuring the prognosis and/or progress of diseases and therapies.
 These substances may be normally present in small amounts in the blood or other tissues

When the amounts of these substances change, they may indicate disease.

Valid biomarkers should
 demonstrate drug activity sooner
 facilitate clinical trial design by defining patient populations
 optimize dosing for safety and efficacy
 be sensitive and easy to assay to speed drug development

Protein structure unchanged degradation
Protein structure is changed concentration posttranslational modification
•
Sensing structural change is a major element of comparative proteomics
•
Most of metabolomics works focus on concentration change only.
sequence
(mutation)

 Sample complexity
 About 25K types of protein coding-genes present in human. IPI human database (v3.25) has 67,250 entries, which could generate about 10 6-8 peptides
 More than one hundred post translational modifications
(PTMs) could happen in a proteome
 Large protein concentration difference
 10 7-8 in human cells, and at least 10 12 in human plasma
 Dynamic range of a LC-MS is about 10 4-6
 The top 12 high abundant proteins constitute approximately 95% of total protein mass of plasma/serum
 Albumin, IgG, Fibrinogen, Transferrin, IgA, IgM,
Haptoglobin, alpha 2-Macroglobulin, alpha 1-Acid
Glycoprotein, alpha 1-Antitrypsin and HDL (Apo A-I &
Apo A-II).

Dynamic system, large subject variation
Body Fluid profiling: biomarker platform
Generic
Sample prep.
Focused
Sample prep.
 g/ml ng/ml pg/ml
High concentration compounds
Low concentration compounds

•Metabolites have a wide range of molecular weights and large variations in concentration
•The metabolome is much more dynamic than proteome and genome, which makes the metabolome more time sensitive
•Metabolites can be either polar or nonpolar, as well as organic or inorganic molecules.
This makes the chemical separation a key step in metabolomics
•Metabolites have chemical structures , which makes the identification using MS an extreme challenge cholesta-3,5-diene

biomarker discovery
Diseased
A
B
C
D
…
Z
A
B
C
D
…
Z
S1 S2 S3
A
B
C
D
…
Z
A
B
C
D
…
Z
S4
Healthy
A
B
C
D
…
Z
A
B
C
D
…
Z
S5 S6
A
B
C
D
…
Z
S7
A
B
C
D
…
Z
S8

LIMS
Quality control data re-examination
Molecular networks
Protein
Function
Interaction
Correlation
Significance test
Regulated peaks
Molecular identification
Pattern recognition
Cluster loadings
Molecular validation
Pathway modeling
Regulated molecules
Unidentified molecules targeted tandem MS

Systems Biology Differential omics
1. Experimental design
2. Molecular identification
3. Data preprocessing
4. Statistical significance test
5. Pattern recognition
6. Molecular networks

• MudPIT, i.e. SCX followed by RP
• The proteome is split into 10-20X more fractions
• There is carry-over between fractions
• LC fractions generally still are too complex for MS
• Affinity Selection
• Avidin selection of Cys-containing peptides
• Cu-IMAC for His-containing peptides
• Ga-IMAC for phosphorylated peptides
• Lectins for glycosylated peptides
AP
Sample
APR
AP
Digestion
SCX
F1 F2 F2 F2
RPC-MS
Qiu, R.; Zhang, X. and Regnier, F. E. J. Chromatogr. B. 2007, 845, 143-150.
Wang, S.; Zhang, X.; and Regnier, F. E. J. Chromatogr. A 2002, 949, 153-162.
Regnier, F. E.; Amini, A.; Chakraborty, A.; Geng, M.; Ji, J.; Sioma, C.; Wang, S.; and Zhang, X. LC/GC 2001, 19(2), 200-213.
Geng, M.; Zhang, X.; Bina, M.; and Regnier, F. E. J. Chromatogr. B 2001, 752, 293-306.

a sample gel based platform
• Avoiding gel-to-gel variability
• Only labeling K-containing peptides
• Accurate quantification d)
Asara, J. M.; Zhang, X.; Zheng, B.; Christofk, H. H.; Wu, N.; Cantley, L. C. Nature Protocols, 2006, 1, 46-51. .
Asara, J. M.; Zhang, X.; Zheng, B.; Christofk, H. H.; Wu, N.; Cantley, L. C. J. Proteome Res., 2006, 5, 155-163.
Ji, J.; Chakraborty, A.; Geng M.; Zhang, X.; Amini, A.; Bina, M.; and Regnier, F. E. J. Chromatogr. A 2000, 745, 197-210.

Systems Biology Differential omics
1. Experimental design
2. Molecular identification protein identification metabolite identification
3. Data preprocessing
4. Statistical significance test
5. Pattern recognition
6. Molecular networks

database searching
The database searching approach uses a protein database to find a peptide for which a theoretically predicted spectrum best matches experimental data.
Protein
Peptide
Mass matched peptide

database searching
More than 20 algorithms have been developed.

Sequest

Spectrum Mill

Mascot

X! Tandem

OMSSA
1.
About 20% of tandem ms spectra could provide confident peptide identification
2.
< 50% of peptides can be identified by all algorithms
Zhang, X.; Oh, C.; Riley, C. P.; Buck, C. Current Proteomics 2007, 4, 121-130.

de novo sequencing de novo sequencing reconstructs the partial or complete sequence of a peptide directly from its MS/MS spectrum.
Performance of de novo method is limited by low mass accuracy, mass equivalence, and completeness of fragmentation.
Pevtsov, S.; Fedulova, I.; Mirzaei, H.; Buck, C.; Zhang, X. Journal of Proteome Research. 2006, 5, 3018-3028.
Fedulova, I.; Ouyang, Z.; Buck, C.; Zhang, X. The Open Spectroscopy Journal 2007, 1, 1-8.

structure of pattern classifier
Input layer
Hidden layer
Output layer
Oh, C.; Zak, S. H.; Mirzaei, H.; Regnier, F. E.; Zhang, X. Bioinformatics 2007, 23, 114-118.

h ji o kj t t h j j t t o k t t j j t t
Oh, C.; Zak, S. H.; Mirzaei, H.; Regnier, F. E.; Zhang, X. Bioinformatics 2007, 23, 114-118.

Protein Identification Using Multiple
Algorithms and Predicted Peptide
Separation in HPLC
PIUMA architecture
Raw LC/MS/MS data
2
Unmatched spectra
Unknown modification search
Protein List mzData or mzXML format
Processed MS/
MS data
3
1
Database seraching
Unmatched spectra
De novo sequencing
Mascot
Sequest
X! Tandem
Lutefisk novoHMM
Peaks
Peptide List
Peptide List Color legend existing algorithms algorithms to be developed method descriptions
Oh, C.; Zak, S. H.; Mirzaei, H.; Regnier, F. E. and Zhang, X. Bioinformatics, 2007, 23, 114-118.
Zhang, X.; Oh, C.; Riley, C. P.; Buck, C. Current Proteomics 2007, 4, 121-130.

Systems Biology Differential omics
1. Experimental design
2. Molecular identification
3. Data preprocessing
Spectrum deconvolution
Quality control
Alignment
Normalization
4. Statistical significance test
5. Pattern recognition
6. Molecular networks

GISTool, single sample analysis
1.
To differentiate signals arising from the real analytes as opposed to signals arising from contaminants or instrument noise
2.
To reduce data dimensionality, which will benefit down stream statistical analysis.
Functionality •Smoothing and centralization
•Peak cluster detection
• Charge recognition
• De-isotope
•Peak identification at LC level
•Doublet recognition
•Doublet quantification

Deconvoluting MS spectra
748.6354 3+
748.9694 2+
Single sample analysis
Zhang, X.; Hines, W.; Adamec, J.; Asara, J.; Naylor, S.; and Regnier, F. E. J. Am. Soc. Mass
Spectrom. 2005, 16, 1181-1191.

• Biological Sample QA/C
• protein assay
• Experimental Data QA/C
• 2D K-S test
• Percentile of detected peaks
• Percentile of aligned peaks
• Retention time variance vs. retention time
• m/z variance vs. retention time
• Frequency distribution of RT & m/z variance
0.08
0.06
0.04
0.02
0
1 2 3 4 5 6 sample ID
7 8 9 10
20 30 40 retention time (min)
50 60
Zhang, X.; Asara, J. M.; Adamec, J.; Ouzzani, M.; and Elmagarmid, A. K.
Bioinformatics, 2005, 21, 4054-4059.

To recognize peaks of the same molecule occurring in different samples from the thousands of peaks detected during the course of an experiment.
•Referenced alignment
•Blind alignment
•Quality depending on the information of peak detection
•Depends on experimental design

XAlign software for proteomics & metabolomics data
•Detecting median sample
M j
=

I i,j
M i,j
/

I i,j
T j
=

I i,j
T i,j
/

I i,j
D i s
=

|T i,j j=1
-
µ j
|
•Aligning samples to the median sample
0.8
0.4
0
-0.4
-0.8
10
10000
20 30 40 retention time (min)
50 60 70
Zhang, X.; Asara, J. M.; Adamec, J.; Ouzzani, M.; and Elmagarmid, A. K.
Bioinformatics, 2005, 21, 4054-4059.
1000
100
10
10 y = 1.3636x + 16.511
R
2
= 0.9475
100 1000 intensity of aligned peaks (sample 1)
10000

Chromatogram of Serum Analyzed on GC

GC/TOF-MS
metabolite component of human serum
• Four dimension
• 1535 peaks have been detected

MSort software for metabolomics
Oh, C.; Huang, X.; Buck, C.; Regnier, F. E. and Zhang, X. J. Chromatogr. A. 2008, 1179, 205-215
Criteria for alignment
• 1 st dim. rt
• 2 nd dim. rt
• spec. correlation
Features
*peak entry merging
*cont. exclusion

53 standard acids
1000
800
10 x 10
5
8
6 600
4
400
2
200
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
The number of peak entries in a row of alignment table
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
The number of peak entries in a row of alignment table
1.
8 [ OA + FA ] samples and 8 [ AA + FA ] samples
2.
derivatization reagent: (N-Methyl-N-t-butyldimethylsilyl)-trifluoroacetamide ( MTBSTFA )
Oh, C.; Huang, X.; Buck, C.; Regnier, F. E. and Zhang, X. J. Chromatogr. A. 2008, 1179, 205-215

To reduce concentration effect and experimental variance to make the data comparable.
1.
Log linear model x ij
= a i
 r j
 e ij
2.
Reference sample normalization
0 200
3.
Auto-scaling
4.
Constant mean / trimmed constant mean
5.
Constant median / trimmed constant median
400 600 peak index
800 1000

human serum sample
Before Normalization
20.7%
Intensity Variation
0.0
0.2
0.4
CV
0.6
0.8
1.0
After Normalization
0.0
0.2
0.4
CV
0.6
0.8
1.0
Intensity Variation
Log linear model: x ij
= a i
 r j
 e ij log(x ij
) = log(a i
) + log(r j
) + log(e ij
)
17.3%
0.2
0.4
CV
0.6
0.8
1.0
0.2
0.4
CV
0.6
0.8
1.0

Systems Biology Differential omics
1. Experimental design
2. Molecular identification
3. Data preprocessing
4. Statistical significance test
5. Pattern recognition
6. Molecular networks

To find individual peaks for which there are significant differences between groups.
Methods
1.
Pair-wise t-test (diff. mean?)
2.
Mann-Whitney U test (diff. median?)
3.
Kolmogorov-Smirnov test (diff. population?)
4.
Kruskal-Wallis analysis of variance

metabolome of great blue heron fertilized eggs contaminated by PCBs
PCBs: polychlorinated biphenyls down-regulated up-regulated fold change = I_c / I_n blue line: p=0.05
dashed line: fold change = 0
-3 -2 -1 0 fold change (log)
1 2 3

Systems Biology Differential omics
1. Experimental design
2. Molecular identification
3. Data preprocessing
4. Statistical significance test
5. Pattern recognition
6. Molecular networks

Resulting pattern recognition provides the first glimpse of improvement in understanding the underlying biology.
Principle component analysis (PCA)
Linear Discriminant Analysis (LDA)
Clustering objects on subsets of attributes (COSA)
Support vector machine (SVM)
Artificial neural network (ANN)

27 of the 28 control humans and all 8 control rats cluster to one group
11 of the 14 diseased human and all diseased rats cluster to second group

breast cancer samples vs. control samples

breast cancer samples vs. control samples

Systems Biology Differential omics
1. Experimental design
2. Protein identification
3. Data preprocessing
4. Statistical significance test
5. Pattern recognition
6. Molecular networks correlation network interaction network regulation network pathway analysis

pair wised correlation of proteins and metabolites
Diseased
A
B
C
D
…
Z
A
B
C
D
…
Z
S1 S2 S3
A
B
C
D
…
Z
A
B
C
D
…
Z
S4
Healthy
A
B
C
D
…
Z
A
B
C
D
…
Z
S5 S6
A
B
C
D
…
Z
S7
A
B
C
D
…
Z
S8

an example of drug effect on disease state
•Reveal important relationships among the various
GP-1a
L-6b
L-7a
L-7b
L-8a
L-12b
Emb
Tiss
ALP
L-24b
L-6a
GP-1b
L-20b
ApoA1_6
L-24a
L-19b
L-27b
L-15b
L-15a
L-14b
L-14a
L-1b
L-26b
L-20a
L-27a
L-13b
L-1a
L-16a
L-13a
ApoE_1
L-5a
L-11a
L-5b
SerPI_II_2
L-11b
L-18a
L-18b
L-28b
L-26a
L-12a
L-21b
L-21a
C18:1
LPC
L-9a
L-9b components
•Complimentary to abundance level information
•Provides information about the biochemical processes underlying the disease or drug response
Unkn1
C52:2 TG
L-10b
C33:1 PC
FBGB phenylalanine alanine
C18:2 LPC
C54:5 TG
C32:0 PC
C30:0 PC
C24:1 SPM
C24:0 SPM a-glucose
L-10a
L-8b
AMBP
L-17b
L-17a
ALB
TP
C52:1 TG
C50:4 TG
FetuinA_2
L-22a
L-23b
L-28a
A1MG_5
C52:5 TG alanine
L-23a
C54:5 TG leucine valine
L-22b
A1MG_2 valine
L-19a
K
C34:2 PC formate
C32:1 PC
L-16b
C52:3 TG glutamine glutamine tyrosine
C52:4 TG isoleucine glutamine
BUN
C58:5 TG
A1I3_3
C34:1 PC
C36:2 PC
C36:1 PC
GLUC
C54:3 TG
C54:2 TG
HDL
C20:4 CE
ApoA1_5
C54:1 TG
C54:6 TG
C52:6 TG
C58:3 TG
C56:3 TG
C58:4 TG
C56:2 TG
C22:5 CE lactate
ITIH3_1
Afamin_2 valine glutamine
TRIG leucine
NEFA
GLYC
C60:3 TG
C58:2 TG
C60:4 TG
C16:1 CE valine
C16:1 LPC tyrosine creatine lactate tyrosine lactate acetate tyrosine lactate alanine creatine
C38:4 PC tyrosine C46:1 TG isoleucine C48:1 TG lactate lactate phenylalanine
C20:5 CE C36:4 PC
C20:3 CE
Plasminogen
C19:0 LPC
C56:4 TG
C22:6 CE
C18:3 CE
C18:0 CE
C16:0 CE
C18:1 CE
C20:2 CE
C18:2 CE a-glucose
= positive correlation
= negative correlation phenylalanine phenylalanine
C18:1 LPC
PlasPre_2 leucine
A1I3_4 TT_2 phenylalanine
ApoA1_7 b-glucose b-glucose
TT_1
FG
ApoA1_3
C20:4 LPC
LD
A2GC
= higher in treated g roup
= lower i n treated g roup
Clish, C. B.; Davidov, E.; Oresic, M.; Plasterer, T.; Lavine, G.; Londo, T. R.; Meys, M.; Snell, P.; Stochaj, W.; Adourian, A.;
Zhang, X.; Morel, N.; Neumann, E.; Verheij, E.; Vogels, J, T.W.E.; Havekes, L. M.; Afeyan, N.; Regnier, F. E.; Greef, J.;
Naylor, S. Omics: A Journal of Integrative Biology 2004, 8, 3-13.

An interactive integration and visualization environment for molecular correlation of ‘omics data.
•Integrating molecular expression information generated in different ‘omics
•Visualizing molecular correlation in interactive mode
•Enabling time course data visualization and analysis
•Automatically organizing molecules based on their expression pattern in time course.
Zhang, M.; Ouyang, Q.; Stephenson, A.; Salt, D.; Kane, D. M.; Burgner J.; Buck, C. and Zhang, X. BMC
Systems Biology. Accepted by BMC Systems Biology.
a) b)


Wet-lab verification

AQUA

MRM
 Antibody

In-silico verification
 tracing lineage
 pathway analysis

•Interested in identifying the connections between input and output data for a program
•Tracing of fine-grained lineage through run-time analysis
•Developed based on dynamic slicing techniques used in debugging
• Applicable to any arbitrary function
Zhang, M.; Zhang, X.; Zhang, X.
and Prabhakar, S. 33rd International
Conference on Very Large Data Bases (VLDB 2007), 2007.

• Informatics platform developed in my group can be used to analyze protein and metabolite profiling data to differentiate disease and normal samples for biomarker discovery
• Groups identified using clustering analysis reflected the phenotypic categories of cancer and control samples, the animal and human subjects, etc. with high degree of accuracy
• The application of SysNet using an interactive visual data mining approach integrates omics data into a single environment, which enables biologists performing data mining
• Lineage tracing technology is an efficient and effective approach for in-silico biomarker verification. This technique will significantly reduce the false discovery rate (FDR) of biomarker discovery

Irina Fedulova
Dr. Hamid Mirzaei
Dr. Cheolhwan Oh
Sergey E. Pevtsov
Ouyang Qi
Alan Stephenson
Mingwu Zhang
Dr. John Burger
Dr. Michael D. Kane
Dr. Fred E. Regnier
Dr. David Salt
Dr. Mohammad Sulma
Dr. Daniel Raftery
Dr. Sunil Prabhakar
Dr. David Clemmer
Dr. John Asara
Dr. Mu Wang
Dr. Jake Chen
Dr. Steve Valentine
Dr. Steve Naylor

Posting Title: Industrial Postdoctoral Fellow - Bioinformatician
Work Location: University of Louisville, KY
Job Type: Full time
Starting Date: Position immediately available
Job Description: Predictive Physiology and Medicine (PPM) Inc. is an exciting health and life sciences company based in Bloomington, Indiana focused on developing analytical systems for the individualized health and wellness industry.
We have an immediate opening for a postdoctoral fellow. The successful candidate will develop bioinformatics systems for mass spectrometry based quantitative proteomics and metabolomics.
Requirements: The position requires a bioinformatician with strong computational background. Priority will be given to the candidate with a PhD in bioinformatics, computer science, statistics, engineer, or computational physics.
The successful candidate should have strong understanding of statistics and pattern recognition. Programming skills using Matlab, Microsoft .NET, or Java to accomplish analyses is required. Experience in analyzing biological data is not required; however, interest in multidisciplinary research is a must.