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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Sma1NormSma1Norm

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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

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52

32 93

73

99

119

149

132

168

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323

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447

427

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471

554

534

576

596 940

960998

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12151

45

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192021 22

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29 3031

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434445 464748

49505152

5455

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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

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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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