Gene regulatory code Alexander Kel BIOBASE GmbH Wolfenbüttel, Germany Beverly, USA Bangalor, India.
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Transcript of Gene regulatory code Alexander Kel BIOBASE GmbH Wolfenbüttel, Germany Beverly, USA Bangalor, India.
Gene regulatory code
Alexander Kel
BIOBASE GmbHWolfenbüttel, Germany
Beverly, USABangalor, India
George Gamow Vadim Ratner
+Frame-shift mutations
+
connectivity of the codon series
+
organ,tissue,cell
stage of development
cell cyclephase
extracellularsignals
Where ?
When ?
With whom?
How ?
gherllojunomd-bype Alexander fasltoiw
…cis
trans
Insulin pathway
TRANSPATH®TRANSPATH®
TRANSPATH Professional: MOLECULE table
TRANSPATH: TNF-alpha – 1 step downstream
TNF-alpha
TRANSPATH: TNF-alpha – 2 step downstream
TNF-alpha
TNF-alpha
TRANSPATH: TNF-alpha – 3 step downstream
TNF-alpha
TRANSPATH: TNF-alpha – 4 step downstream
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.
Example2: Growth hormone-deficient mice (Sma1)Example2: Growth hormone-deficient mice (Sma1)
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)
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)
Sma1Norm
-0.1 0.0 0.1 0.2 0.3 0.4 0.50
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No of
obs
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Sma1NormSma1Norm
-0.1 0.0 0.1 0.2 0.3 0.4 0.50
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No of
obs
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Composite module found in promoters of differentially expressed genes in liver of
growth hormone-deficient mice (Sma1).
differentially
expressed
genes
Non-changed
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 hormone-
deficient 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.
AView of loaded data set
Current dataset is highlighted by black in the project tree
CMatch output: single putative TF binding sites
Match outputs on the project tree, with different profiles applied
YES set: in this example genes upregulated 2-fold and more
NO set: in this example genes downregulated 2-fold and more
frequency of matches for given matrices in the YES and NO sets
Ratio of frequencies YES/NO
matrices
P-value for the calculated ratio
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
ECreate 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
Score of the suggested key nodes
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.
To create a subset of selected key nodes or of all molecules under the selected keynodes
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
TNF receptor associated factor 6disease: osteopetrosis
Example: human disease - Pseudoxanthoma Elasticum
Elastic fibers calcification
C
NEC
IC
ABC
32
32
23
7 1011
23
52
32 93
73
99
119
149
132
168
188
323
303
350
370
447
427
451
471
554
534
576
596 940
960998
1018
1082
1062
1084
1104
1196
1176
1199
12151
45
6
8
9
12
14151617
18
192021 22
2425262728
29 3031
3233
34
35
3637
38
3940
4142
434445 464748
49505152
5455
56
57. ABCC6 del
13. ABCC6del15 53. ABCC6del23-29
Mutations in ABCC6 transporter
0 500 1000
TFIII
DEC
TBP
NF-Y
SOX9
CAAT
p53Oct-1
Ap-2REP
DR1
DR1
SRY
Ap-2REPSRF MAZ
CP2
ELA2: human elastase 2 gene
Promoter evolution
TGAgTCA
AP-1
TGAGTCAHuman collagenase (-2013) *******
TGTGTAA** ** *
Mouse IL-2 (-143)
TTTCTCC* ** Mouse TNF-alpha (-82)
Consensus:
human TNF promoter
mast cells
T-cells + ?
dendritic cells
T-cells
-107 -74
NFAT
NFATAP-1
NF-kB
C/EBPAP-1
VDR
0
200
400
600
800
1000
1200
1400
Size of zip file = complexity
Time
„Molecular surrealism of 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
D EF
BC
BC
1
2
1
2
Fuzzy puzzle hypothesis of the multipurpose structure of the eukaryotic promoters
gherllojunomd-bype Alexander fasltoiw
Several regulatory messages could be written in thesame sequence. Reading of the messages depends on the
cellular context
1)gherllojunomd-bype Alexander fasltoiw
2)gherllojunomd-bype Alexander fasltoiw
3)gherllojunomd-bype Alexander fasltoiw
SHMALGAUSENIvan 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.
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).
LAW OF REQUISITE VARIETY
The Growth of Structural and Functional Complexity during Evolution
Cybernetics:
Fundamental evolutional limitations
Error catastrophe (Eigen M., 1971; Ratner V. and Samin V., 1982)
Haldane‘s Dilemma (Haldane J., 1957; Crow J. and Kimura M, 1970)
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...“
Losses due to Genetic Load
Fitness of population:
Solution: 0→s Neutrality (Kimura M.)
μ1
<LSequence length:
maxmax )ln4exp( wpNsww <<=
- replication errors
Prokaryotes
Genome lengthlimitations
Error catastrophe
Diploidity
Limitations onduplications
Instability of genomes
to repeats .
Chromatin
Limitations on multicellular organizationand differentiation
Flexibility of gene expression in differenttissues, cells, stages of development,
under induction and so on.
•Decrease of binding specificity•Fuzzy puzzle
• Induced fitting•Protein-protein interactions
Multiplicity of regulatory messagesencrypted in regulatory sequences
Unicelleukaryotes
Multicelleukaryotes
Stepwise breaking of the evolutional limitations in the course of progressive evolution to multicellular eukaryotic organisms
Single-celled
Gradual evolutionby fixation of multiple substitutions (Protein functional centres)
Edited bipolymerby fixation of a small number of substitutions (Protein folding)
Evolution at onceby fixation of single substitutions(Regulatory regions of eukaryoticgenes)
Three mechanisms of biopolymer evolution
gherllojunomd-bype Alexander fasltoiw
Even some messages which were not written
gherllojunomd-bype Alexander fasltoiw
gherllojunomd-zype Alexander fasltoiw
b
C21orf68_human CCAAGATATAGTTTAAATCCATTGTTTCTTTGTTGACTTTCTGGCTTGATGCCCTGTCTA 7124 <===============V$ELK1_01(0.87) C21orf68_chimp CCAAGATATAGTTTAAATCCATTGTTTCTTTGTTGACTTCCTGGCTTGATGCCCTGTCTA 7125 *************************************** ******************** <===========V$SRY_02(0.83) C21orf68_human GTGCTGTCACTGGAGTATTGATGTCCCCACTATTATTGTGTTGCTTTATATCTCATTTCC 7184 =======>V$CREB_01(1.00) C21orf68_chimp GTGCTGTCACTGGAGTATTGACGTCACCACTATTATTGTGTTGCTTTATATCTCATTTCC 7185 ********************* *** ********************************** C21orf68_human TAGGTCTATTAGTAATTGTTTTATAAATTTGGGAGCTCCAGTGTTAGGTGCATATATGTT 7244 C21orf68_chimp TAGGTCTATTAGTAATTGTTTTATAAATTTGGGAGCTCCAGTGTTAGGTGCATGTATGTT 7245 ***************************************************** ******
HMG14_chimp GCAGCAGCGAAGGTAGGCCTCGAAACGCGCATTGGGATGCAGCGGGGCCTTAGGCTACAC 10854 HMG14_human GCAGCAGCGAAGGTAAGCCTCGAAACGCGCATTGGGATGCAGCGGGGCCTTAGGCTACAC 9978 *************** ******************************************** 1 ===========>V$NFKB_C(1.00) HMG14_chimp TGCTTCTTAATGCGGGACTTTCCATTGTGATTAGCTATTTGAGCTTTCTTTATACTTTAA 10914 HMG14_human TGCTTCTTAATGCGGGGCTT-CCATTTTGATTAGCTATTGGAGCTTTATTTATACTTTAA 10037 **************** *** ***** ************ ******* ************ HMG14_chimp TAATTACGGTAAATAATTTTTCTAGTGGTCGAGGCAAAAATGTAATGGATATATTCATCC 10974 HMG14_human TAATTACGGTAAATAATTTTTCTAGTGGTCGAGGCAAAAATGTAATGGATATATTCATCC 10097 ************************************************************
Examples of anti-footprint (human/chimp) (minimized FP)
---------->V$CP2_01(2.767,0.504) <-----------V$EGR1_01(3.782,1.465) AKR1B1_-106_C GACCCTTGGGGAAGGCCGCCGCGGCACCCCCAGCGCAACCAATCAGAAGGCTCCTTCGCG <---------V$CEBP_Q3(2.903,0.921) AKR1B1_-106_T GACCCTTGGGGAAGGCCGCCGCGGCACCCCTAGCGCAACCAATCAGAAGGCTCCTTCGCG ****************************** *****************************
Diabetes mellitus, without diabetic complications
Polycystic ovary syndrome
CYP17A1_-34_T CCTAGAGTTGCCACAGCTCTTCTACTCCACTGCTGTCTATCTTGCCTGCCGGCACCCAGC <-----------V$EGR1_01(3.279,0.962) CYP17A1_-34_C CCTAGAGTTGCCACAGCTCTTCTACTCCACCGCTGTCTATCTTGCCTGCCGGCACCCAGC ****************************** *****************************
Diabetes mellitus
<=============V$COUP_01(6.373,2.182) ------------>V$DR1_Q3(4.842,1.447) TCF1_-58_A TGAGGCCTGCACTTTGCAGGGCTGAAGTCCAAAGTTCAGTCCCTTCGCTAAGCACACGGA TCF1_-58_C TGAGGCCTGCACTTTGCAGGGCTGAAGTCCCAAGTTCAGTCCCTTCGCTAAGCACACGGA ****************************** *****************************
Promoter is a white square
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