Gene regulatory code
Alexander Kel
BIOBASE GmbH
Wolfenbüttel, Germany
Beverly, USA
Bangalor, India
George Gamow
Vadim Ratner
+
Frame-shift mutations
+
connectivity of the
codon series
+
gherllojunomd-bype Alexander fasltoiw
Where ?
organ,
tissue,
cell
When ?
stage of
development
How ?
cell cycle
phase
extracellular
signals
With whom?
trans
cis
…
Insulin pathway
TRANSPATH®
TRANSPATH Professional: MOLECULE table
TRANSPATH: TNF-alpha – 1 step downstream
TNF-alpha
TRANSPATH: TNF-alpha – 2 step downstream
TNF-alpha
TRANSPATH: TNF-alpha – 3 step downstream
TNF-alpha
TRANSPATH: TNF-alpha – 4 step downstream
TNF-alpha
Example2: Growth hormone-deficient mice (Sma1)
Picture of WT mouse with hetero- and homozygous Sma1 mice.
Heterozygous Sma1 mice show 33% reduction of the body weight, whereas
homozygous mice exhibit a 56-58% reduction in body weight.
Composite module found in promoters of differentially
expressed genes in liver of
growth hormone-deficient mice (Sma1).
0.1040 * V$CETS1P54_02(0.949) -50- V$TCF4_Q5(0.908)
0.0751 * V$TCF1P_Q6(0.726)
-50- V$STAT6_01(0.861)
0.0728 * V$SF1_Q6(0.684)
-50- V$SMAD3_Q6(0.833)
0.0419 * V$ELK1_02(0.862)
-50- V$GRE_C(0.842)
450
40
400
35
350
30
300
25
250
20
N o of ob s
0.0983 * V$TCF11MAFG_01(0.821)
0.0471 * V$FOXO4_01(0.961)
0.0301 * V$IPF1_Q4(0.852)
0.0410 * V$AR_01(0.851)
0.0766 * V$GR_Q6(0.971)
0.0482 * V$STAT1_02(0.995)
0.0508 * V$CEBPB_01(0.98)
0.0281 * V$STAT5A_02(0.826)
200
15
150
10
100
5
50
Sm a1
0
0
-0.1
0.0
Non-changed
genes
0.1
0.2
0.3
0.4
Norm
0.5
differentially
expressed
genes
Results of the ArrayAnalyzer™ search upstream
TFs
Identifying growth hormone (GH) and receptor tyrosine kinases (RTK) as potential key
molecules involved in differential expression of the genes in liver of growth hormonedeficient mice (Sma1).
Data Sourse
Background
•Mice were infected by leukemia viruses, either by neurovirulent FrCasE or
by non- neurovirulent Fr75E;
•Aim was to find specific changes resulting from infection of microglia cells;
•Comparison of gene expression in FrCasE-infected versus Fr75E-infected
microglia cells is done in the following example.
A
Current dataset
is highlighted
by black in the
project tree
View of loaded data set
C
Match output: single putative TF binding sites
YES set: in this
example genes
upregulated 2fold and more
NO set: in this example
genes
downregulated
2-fold and more
matrices
Match outputs on the
project tree, with
different profiles applied
P-value for the
calculated
ratio
frequency of matches for given
matrices in the YES
and NO sets
Ratio of frequencies
YES/NO
Promoter model based on nerve-specific TFs
Increase of Fitness function with number of iterations
Composition of the promoter model
Sequences of YES and NO sets are well separated by the
selected promoter model
Vizualization of the promoter models for particular genes
E
Create a subset of TFs involved in the models
Subset of TFs involved
in the selected promoter
models on the project
tree, under the
corresponding models
F
Searching key nodes upstream of the selected TFs
To create a subset of selected key
nodes or of all molecules under
the selected keynodes
Key node analysis can be done at the fixed number of steps
upstream of the selected TFs, for example we can go one step
upstream, or two,...steps upstream and suggest molecules
(kinases, adaptors, receptors, ligands) that could provide
coordinated regulation of the selected TFs.
Score of the
suggested key nodes
F
Vizualization of the suggested key nodes
Suggested key node,
adaptor protein Hgs
Suggested key node Hgs is a known biomarker
for neurofibromatosis
F
Vizualization of the suggested key nodes
Suggested key
node, adaptor
protein TRAF2
Vizualization maps
can be saved on the
project tree
Suggested key node TRAF2 is important
for the induction of apoptosis
Example: human disease - Pseudoxanthoma Elasticum
TNF receptor associated factor 6
disease: osteopetrosis
Elastic fibers calcification
Mutations in ABCC6 transporter
6 del
1 3 . A B C C 6 d e l1 5
5 3 . A B C C 6 d e l2 3 -2 9
EC
9 149
132
12
168
188
323
350 447
5
6
4
303 370 427
471
960
554 576
998
1018
534 596
940
24
11
10
7
3
451
9
23
1082 1084
25
26
27
28
8
14
15
16
22
21
20
17
18
19
11 9 6
11 9 9
34
11 7 6 3 5
33 1215
36
32
37
29 30 31
38
1 0 6 2 11 0 4
39
40
41
42
43
44
45
55
54
52
51
50
49
4 6 4 74 8
IC
C
56
ABC
ELA2: human elastase 2 gene
Promoter evolution
AP-1
Consensus:
Human collagenase (-2013)
TGAgTCA
*******
TGAGTCA
Mouse IL-2 (-143)
** ** *
TGTGTAA
Mouse TNF-alpha (-82)
*
**
TTTCTCC
NFAT
human TNF promoter
-107
AP-1
mast cells
-74
NFAT
T-cells
NF-kB
dendritic cells
VDR
AP-1
C/EBP
T-cells + ?
Size of zip file = complexity
1400
1200
1000
800
600
400
200
0
Time
„Molecular surrealism of promoters“
Fuzzy puzzle hypothesis of the multipurpose structure of the eukaryotic promoters
coding multiple regulatory messages in the same DNA sequence.
A,B,C and D,E,F – two sets of TF; 1,2 – two sites in DNA; BC – basal
complex.
A
B C
B
C
1
2
D
E
B
C
F
1
2
Several regulatory messages could be written in the
same sequence. Reading of the messages depends on the
cellular context
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1)
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2)
3)
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gherllojunomd-bype Alexander fasltoiw
SHMALGAUSEN
Ivan Ivanovich
Born on 23.04.1884.
Died on 07.10.1963.
Evolutional morphology.
Academician of the Division of Mathematical
and Natural Sciences since 01.06.1935.
Evolution of mechanisms
of evolution
Cybernetics:
Cybernetics studies organization, communication and
control in complex systems by focusing on circular
(feedback) mechanisms.
Control or regulation is most fundamentally formulated as a
reduction of variety:
perturbations with high variety affect the system's internal state,
which should be kept as close as possible to the goal state, and
therefore exhibit a low variety.
Cybernetics:
LAW OF REQUISITE VARIETY
For appropriate regulation the variety in the regulator must
be equal to or greater than the variety in the system being
regulated.
Or, the greater the variety within a system, the greater its
ability to reduce variety in its environment through regulation.
Only variety (in the regulator) can destroy variety (in the
system being regulated).
The law was formulated by Ross Ashby (1962).
The Growth of Structural and Functional Complexity during
Evolution
Fundamental evolutional limitations
Error catastrophe (Eigen M., 1971; Ratner V. and Samin V., 1982)
Sequence length: L 
1
 - replication errors
Haldane‘s Dilemma (Haldane J., 1957; Crow J. and Kimura M, 1970)
Fitness of population:
w  w max exp( 4 Ns ln p )  w max Losses due to Genetic Load
Population cannot evolve quickly in many genes
simultaneously because losses are not redressed by fertility.
„... there has not been enough time for evolution to have
occurred - not even for human evolution...“
Solution:
s 0
Neutrality (Kimura M.)
Stepwise breaking of the evolutional limitations in the course of progressive
evolution to multicellular eukaryotic organisms
M u lt ic e ll
eukaryo tes
Single-celled
U nice ll
eukaryo tes
L im itation s on m u lticellu lar organ ization
an d differen tiation
P ro karyo tes
L im itation s on
du plication s
G en om e len gth
lim itation s
F le xib ilit y o f gene e xpressio n in d iffere nt
tissues, cells, stages of developm ent,
under induction and so o n .
Instabilit y o f ge no m es
to repeats .
E rror catastro phe
C h ro m atin
• D ec rease of b in d in g sp ecificity
• F u zzy p u zzle
• In d u ced fittin g
• P rotein -p rotein in teraction s
D ip loid ity
M ultiplicity of regulatory m essa ges
encrypted in regulatory sequences
Three mechanisms of biopolymer evolution
Gradual evolution
by fixation of multiple substitutions
(Protein functional centres)
Edited bipolymer
by fixation of a small number of
substitutions (Protein folding)
Evolution at once
by fixation of single substitutions
(Regulatory regions of eukaryotic
genes)
Even some messages which were not written
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b
Examples of anti-footprint (human/chimp) (minimized FP)
HMG14_chimp
HMG14_human
GCAGCAGCGAAGGTAGGCCTCGAAACGCGCATTGGGATGCAGCGGGGCCTTAGGCTACAC
GCAGCAGCGAAGGTAAGCCTCGAAACGCGCATTGGGATGCAGCGGGGCCTTAGGCTACAC
*************** ********************************************
1
HMG14_chimp
HMG14_human
HMG14_chimp
HMG14_human
C21orf68_human
C21orf68_chimp
C21orf68_human
C21orf68_chimp
C21orf68_human
C21orf68_chimp
===========>V$NFKB_C(1.00)
TGCTTCTTAATGCGGGACTTTCCATTGTGATTAGCTATTTGAGCTTTCTTTATACTTTAA
TGCTTCTTAATGCGGGGCTT-CCATTTTGATTAGCTATTGGAGCTTTATTTATACTTTAA
**************** *** ***** ************ ******* ************
TAATTACGGTAAATAATTTTTCTAGTGGTCGAGGCAAAAATGTAATGGATATATTCATCC
TAATTACGGTAAATAATTTTTCTAGTGGTCGAGGCAAAAATGTAATGGATATATTCATCC
************************************************************
10854
9978
10914
10037
10974
10097
CCAAGATATAGTTTAAATCCATTGTTTCTTTGTTGACTTTCTGGCTTGATGCCCTGTCTA
7124
<===============V$ELK1_01(0.87)
CCAAGATATAGTTTAAATCCATTGTTTCTTTGTTGACTTCCTGGCTTGATGCCCTGTCTA
7125
*************************************** ********************
<===========V$SRY_02(0.83)
GTGCTGTCACTGGAGTATTGATGTCCCCACTATTATTGTGTTGCTTTATATCTCATTTCC
=======>V$CREB_01(1.00)
GTGCTGTCACTGGAGTATTGACGTCACCACTATTATTGTGTTGCTTTATATCTCATTTCC
********************* *** **********************************
TAGGTCTATTAGTAATTGTTTTATAAATTTGGGAGCTCCAGTGTTAGGTGCATATATGTT
TAGGTCTATTAGTAATTGTTTTATAAATTTGGGAGCTCCAGTGTTAGGTGCATGTATGTT
***************************************************** ******
7184
7185
7244
7245
AKR1B1_-106_C
AKR1B1_-106_T
---------->V$CP2_01(2.767,0.504)
<-----------V$EGR1_01(3.782,1.465)
G A C C C T T G G G G A A G G C C G C C G C G G C A C C C CC A G C G C A A C C A A T C A G A A G G C T C C T T C G C G
<---------V$CEBP_Q3(2.903,0.921)
G A C C C T T G G G G A A G G C C G C C G C G G C A C C C CT A G C G C A A C C A A T C A G A A G G C T C C T T C G C G
****************************** *****************************
Diabetes mellitus, without diabetic complications
CYP17A1_-34_T
CYP17A1_-34_C
CCTAGAGTTGCCACAGCTCTTCTACTCCACTGCTGTCTATCTTGCCTGCCGGCACCCAGC
<-----------V$EGR1_01(3.279,0.962)
CCTAGAGTTGCCACAGCTCTTCTACTCCACCGCTGTCTATCTTGCCTGCCGGCACCCAGC
****************************** *****************************
Polycystic ovary syndrome
TCF1_-58_A
TCF1_-58_C
< = = = = = = = = = = = = =V $ C O U P _ 0 1 ( 6 . 3 7 3 , 2 . 1 8 2 )
------------>V$DR1_Q3(4.842,1.447)
T G A G G C C T G C A C T T T G C A G G G C T G A A G T C CA A A G T T C A G T C C C T T C G C T A A G C A C A C G G A
T G A G G C C T G C A C T T T G C A G G G C T G A A G T C CC A A G T T C A G T C C C T T C G C T A A G C A C A C G G A
****************************** *****************************
Diabetes mellitus
Promoter is a
white square
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