Gene regulatory code

geographertonguesΤεχνίτη Νοημοσύνη και Ρομποτική

30 Νοε 2013 (πριν από 3 χρόνια και 8 μήνες)

67 εμφανίσεις

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



+

organ,

tissue,

cell

stage of

development

cell cycle

phase

extracellular

signals

Where ?

When ?

With whom?

How ?

gherllojunomd
-
bype Alexander fasltoiw



cis

trans

Insulin pathway

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)

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)


Sma1
Norm
-
0.1
0.0
0.1
0.2
0.3
0.4
0.5
0
50
100
150
200
250
300
350
400
450
No of
obs
0
5
10
15
20
25
30
35
40
Sma1
Norm
Sma1
Norm
-
0.1
0.0
0.1
0.2
0.3
0.4
0.5
0
50
100
150
200
250
300
350
400
450
No of
obs
0
5
10
15
20
25
30
35
40
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.

A

View of loaded data set

Current dataset
is highlighted
by black in the
project tree

C

Match 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

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

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 6

disease: osteopetrosis

Example:
human disease
-

Pseudoxanthoma Elasticum

Elastic fibers calcification

C
N
EC
IC
ABC
32
32
2
3
7
10
11
23
52
32
93
73
99
119
149
132
168
188
323
303
350
370
447
427
451
471
554
534
576
596
940
960
998
1018
1082
1062
1084
1104
1196
1176
1199
1215
1
4
5
6
8
9
12
14
15
16
17
18
19
20
21
22
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
54
55
56
57. ABCC6 del
13. ABCC6del15
53. ABCC6del23-29

Mutations in ABCC6 transporter

ELA2: human elastase 2 gene

Promoter evolution

TGAgTCA

AP
-
1

TGAGTCA

Human 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

NFAT

AP
-
1

NF
-
kB

C/EBP

AP
-
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
E
F
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 the

same 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

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.

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.
)


1

L
Sequence length:


max
max
)
ln
4
exp(
w
p
Ns
w
w


-

replication errors

Prokaryotes
Genome length
limitations
Error catastrophe
Diploidity
Limitations on
duplications
Instability of genomes
to repeats
.
Chromatin
Limitations on
multicellular

organization
and differentiation
Flexibility of gene expression in different
tissues, cells, stages of development,
under induction

and so on
.

Decrease of binding specificity

Fuzzy puzzle

Induced fitting

Protein-protein interactions
Multiplicity of regulatory messages
encrypted in regulatory sequences
Unicell
eukaryotes
Multicell
eukaryotes
Stepwise breaking of the evolutional limitations in the course of progressive

evolution to multicellular eukaryotic organisms

Single
-
celled

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)


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
CCAAGATATAGTTTAAATCCATTGTTTCTTTGTTGACTT
T
CTGGCTTGATGCCCTGTCTA
7124





<===============V$ELK1_01(0.87)

C21orf68_chimp
CCAAGATATAGTTTAAATCCATTGTTTCTTTGTTGACTT
C
CTGGCTTGATGCCCTGTCTA
7125


*************************************** ********************






<===========
V$SRY_02(0.83)


C21orf68_human
GTGCTGTCACTGGAGTATTGA
T
GTC
C
CCACTATTATTGTGTTGCTTTATATCTCATTTCC
7184





=======>
V$CREB_01(1.00)


C21orf68_chimp
GTGCTGTCACTGGAGTAT
TGA
C
GTC
A
CCACTATTATTGTGTTGCTTTATATCTCATTTCC
7185

********************* *** **********************************


C21orf68_human
TAGGTCTATTAGTAATTGTTTTATAAATTTGGGAGCTCCAGTGTTAGGTGCAT
A
TATGTT
7244

C21orf68_chimp
TAGGTCTATTAGTAATTGTTTTATAAATTTGGGAGCTCCAGTGTTAGGTGCAT
G
TATGTT
7245

*****************************
************************ ******


HMG14_chimp
GCAGCAGCGAAGGTA
G
GCCTCGAAACGCGCATTGGGATGCAGCGGGGCCTTAGGCTACAC
10854

HMG14_human
GCAGCAGCGAAGGTA
A
GCCTCGAAACGCGCATTGGGATGCAGCGGGGCCTTAGGCTACAC
9978

*************** ********************************************



1

===========>
V$NFKB_C(1.00)


HMG14_chimp
TGCTTCTTAATGCGGG
A
CTT
T
CCATT
G
TGATTAGCTATT
T
GAGCTTT
C
TTTATACTTTAA
10914

HMG14_human
TGCT
TCTTAATGCGGG
G
CTT
-
CCATT
T
TGATTAGCTATT
G
GAGCTTT
A
TTTATACTTTAA
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
GACCCTTGGGGAAGGCCGCCGCGGCACCCC
C
AGCGCAACCAATCAGAAGGCTCCTTCGCG




<
---------
V$CEBP_Q3(2.903,0.921)

AKR1B1_
-
106_T
GACCCTTGGGGAAGGCCGCCGCGGCACCCC
T
AGCGCAACCAATCAGAAGGCTCCTTCGCG


*******************
*********** *****************************

Diabetes mellitus, without diabetic complications

Polycystic ovary syndrome

CYP17A1_
-
34_T
CCTAGAGTTGCCACAGCTCTTCTACTCCAC
T
GCTGTCT
ATCTTGCCTGCCGGCACCCAGC




<
-----------
V$EGR1_01(3.279,0.962)


CYP17A1_
-
34_C
CCTAGAGTTGCCACAGCTCTTCTACTCCAC
C
GCTGTCTATCTTGCCTGCCGGCACCCAGC

****************************** *****************************

Diabetes mellitus




<=============
V$COUP_01(6.373,2.182)




------------
>
V$DR1_Q3(4.842,1.447)


TCF1_
-
58_A
T
GAGGCCTGCACTTTGCAGGGCTGAAGTCC
A
AAGTTCAGTCCCTTCGCTAAGCACACGGA

TCF1_
-
58_C
TGAGGCCTGCACTTTGCAGGGCTGAAGTCC
C
AAGTTCAGTCCCTTCGCTAAGCACACGGA


****************************** *****************************

Promoter is a

white square


www.biobase
-
international.com



BIOBASE explains biology

Sampling of BIOBASE Customers