Role of Toll-Like Receptors in the recognition of probiotics
by monocyte-derived dendritic cells.
Martínez-Abad, Beatriz1; Garrote, Jose A.1,2; Vallejo-Díez, Sara1; Montalvillo, Enrique1; Escudero-Hernández, Celia1; Bernardo, David3; Vázquez, Enrique4;
Rueda, Ricardo4; Arranz, Eduardo1.
1. Mucosal Immunology Lab. Paediatrics and Immunology Department. University of Valladolid. IBGM-CSIC, Spain. 2. Research Unity. Hospital Clínico Universitario-IECSCYL, Valladolid, Spain. 3. Antigen Presentation Research Group. Imperial
College London, St. Mark’s and Northwick Park Hospital. UK. 4. Discovery Technology Department Abbott Nutrition R&D, Granada, Spain
e-mail:colli01@hotmail.com / bea_mar@ibgm.uva.es
Material and Methods
Introduction
Four probiotic strains from genus Lactobacillus (Group 4, 5, 6 and
7) and 2 from genus Bifidobacterium (Group 8 and 9). As pathogens
controls we used Escherichia coli 0111 CECT 727, Salmonella
typhimurium and Clostridium perfringens CECT 376 (Group 1, 2
and 3 respectively). As basal (Control Group) we used moDCs
unstimulated.
In this assay we have studied the effect of different probiotic and pathogen bacteria on
one group of pattern recognition receptors (PPRs), the Toll-like receptors (TLRs), present
in dendritic cells. TLRs are specific for pathogen-associated molecular patterns (PAMPs)
and trigger different responses depending on stimuli. TLR2 and TLR4 are the most
studied receptors for bacteria because of recognizing two majority compounds of bacterial
wall, peptidoglycan and LPS respectively.
To measure the way in which dendritic cells respond to different type of bacteria we have
measured the changes on gene expression of TLRs pathway and its down-stream
pathways using a RT-PCR array method.
Ficoll and Percoll solution
density gradient centrifugation
IL-4 (500U/ml)
GMCSF (1000U/ml)
Peripheral blood
from 6 healthy
donors
Monocytes
Dendritic cells derived
from monocytes (moDCs)
Stimulation
for 4 hours
The ACTB (β-actin) was selected as housekeeping.
Changes in the transcriptional expression were
estimated with the ∆∆CT method using basal
condition as reference (Livak and Schmittgen
2001).
RNA extraction and
cleaned up and
Reverse Transcription
Results
E. coli
1
P
A
T
H
O
G
E
N
S
Gene
Symbol
10,9988
PELI1
CD80
4,2165
PTGS2
CSF2
91,1504
CSF3
107,6358
6,435
Fold
Regulation
2
C. perfringens
Gene
Symbol
Fold
Regulation
Gene
Symbol
Fold
Regulation
MAP2K3
10,8515
NFKB1
3
Gene
Symbol
Fold
Regulation
Gene
Symbol
Fold
Regulation
CSF2
120,8161
CXCL10
-21,8607
5,0323
CSF3
7,543
52,9298
4,8596
CCL2
10,6119
198,1842
CD80
2,9459
REL
3,1259
CSF2
1056,1719
NFKBIA
5,5711
IFNG
RIPK2
9,3557
CSF3
1266,2631
PELI1
5,1836
IL10
2,856
TICAM2
2,8365
CXCL10
7,3387
PTGS2
590,6094
IL1A
5,6403
4,1434
IL1B
24,8188
IFNB1
2,4435
TLR2
3,4999
IFNA1
3,4806
REL
IFNG
13,8084
TLR7
4,7245
IFNB1
6,1087
RIPK2
10,8368
IL2
8,5137
IL10
10,4676
TNF
15,3839
IFNG
57,6998
TLR2
2,4273
IL6
4,3657
IL1A
106,0239
IL10
31,3454
TLR7
5,2061
IL8
4,0329
IL1B
235,5998
IL12A
2,7105
TNF
44,3334
MAP2K3
3,1299
PTGS2
33,215
IL2
3,2814
IL1A
193,463
IL6
196,1398
IL1B
587,9418
REL
2,2535
IL8
20,2413
IL2
29,0704
TNF
28,1654
IRAK2
11,5273
IL6
388,7104
MAP2K3
12,649
IL8
40,6665
NFKB1
4,1605
IRAK2
11,6471
NFKBIA
4,6945
IRF1
Gene
Symbol
Fold
Regulation
Gene
Symbol
Fold
Regulation
CCL2
3,0323
CXCL10
-6,8258
CSF2
50,6936
IFNA1
CSF3
4,5232
IFNB1
IL10
3,2107
IL2
IL1A
21,5333
IL1B
5
Fold
Regulation
CSF2
18,7006
CXCL10
-222,6368
-4,0655
IL1A
4,4368
IFNA1
-3,343
IFNB1
-19,0858
IL1B
7,9621
IFNB1
-34,1568
5,6801
IKBKB
-2,2394
IL8
2,5512
IKBKB
-2,578
IL1A
13,0701
IL12A
-2,1917
MAP2K3
2,5706
IL12A
-5,8398
-2,4778
IL1B
46,0762
IL2
-2,1888
PTGS2
7,3443
IL2
-2,8297
NFKBIL1
-2,5962
IL6
13,6141
IRF1
-3,5744
TNF
6,4423
IRF1
-3,2827
TICAM2
-2,5273
IL8
5,6081
MAPK8
-2,4137
IRAK2
2,8214
NFKB2
-2,0968
MAP2K3
5,3533
NFKBIL1
-2,1841
31,9635
TICAM2
-2,9914
5,3719
TOLLIP
-2,0092
CCL2
3,5973
CXCL10
-4,1539
-3,5326
CSF2
68,8851
IFNA1
-28,9985
CSF3
46,295
-3,045
IL10
IRF1
-2,9194
35,136
NFKB2
IL6
4,5012
IL8
6,5173
4,2772
23,4233
2,7255
TNF
7,8538
Gene
Symbol
CCL2
CSF2
CSF3
CXCL10
Gene
Symbol
2,412
REL
Fold
Regulation
11,5807
132,6047
PTGS2
TNF
Gene
Symbol
IFNB1
Fold
Regulation
-5,424
7
Gene
Symbol
Fold
Regulation
Gene
Symbol
Fold
Regulation
CCL2
2,3257
CXCL10
-10,5161
CSF2
72,8291
CSF3
IFNB1
-9,8707
7,7066
IL2
-2,1033
IL10
6,9872
IRF1
-3,3142
IL1A
22,2682
MAPK8
-2,0913
IL1B
53,6656
NFKB2
-2,165
IL6
16,0568
NFKBIL1
-2,3649
IL8
4,6569
RELA
-2,2775
3,5618
TICAM2
-2,8438
292,1396
2,1221
IL10
27,0685
IL1A
51,0403
IL1B
129,6176
IL6
100,027
MAP2K3
IL8
17,5964
PTGS2
25,5277
TNF
18,3381
IRAK2
5,2768
MAP2K3
4,5248
NFKB1
2,1695
NFKBIA
3,0298
PTGS2
8
Gene
Symbol
Fold
Regulation
PTGS2
-3,0923
Fold
Regulation
Gene
Symbol
MAP2K3
IFNB1
2,3088
Fold
Regulation
IRAK2
6
Fold
Regulation
CCL2
CXCL10
4
L
A
C
T
O
B
A
C
I
L
L
I
S. typhimurium
Gene
Symbol
Harvest and keep cells
onto Trizol ® untill their
extraction.
B
I
F
I
D
O
B
A
C
T
E
R
I
A
9
Gene
Symbol
Gene
Symbol
MAP2K4
-2,378
NFKB2
-3,313
NFKBIL1
-2,0203
RELA
-2,0651
TICAM2
-2,0991
TOLLIP
-2,0556
Fold
Regulation
Gene
Symbol
Fold
Regulation
CSF2
17,9123
CXCL10
-89,3227
CSF3
2,3981
IFNB1
-41,2893
IL1A
4,92
IKBKB
-2,2312
IL1B
14,1869
IL12A
-2,9637
IL6
2,2965
IRF1
-3,5319
IL8
2,3493
MAP2K4
-2,1041
MAP2K3
2,0338
RELA
PTGS2
10,218
TICAM2
-2,4988
TNF
6,5127
TOLLIP
-2,1489
-2,202
105,1594
RIPK2
5,529
TLR2
2,8932
TNF
11,9809
Fig. 1-9: volcano plot graphs of each stimulus (bacterium) compared with control condition (unstimulated moDCs) in which we can observe in X-axis Log2 (Fold Change (FC) of Group “bacterium” / FC of Control Group) and in Y-axis –Log10 of p-value. Only
transcriptional changes with p ≤ 0.05 and ≥ 2 folds were included in the analysis. Values and plots in red represent up-regulation and values and plots in green represent down-regulation.
Fig. 10: clustergram and dendogram
analysis of genes whose expression
were modified ± 2 fold change
compared to the basal condition.
Rows represent genes and columns
represent condition.
Conclusions
Although, expression of TLR genes have hardly changed, we can observe differences in the NFκB, JNK/p38, JAK/STAT, Interferon Regulatory Factor (IRF) and Cytokine mediated signalling downstream
pathways. Pathogen bacteria induce a different expression pattern as regards probiotics. Gram- bacteria trigger a great amount of genes belong to these routes and Gram+ bacteria, include C.
perfringens, induce a down-regulation of TLR, adaptors and interacting proteins genes expression. We can observe that pathogens not present the same behaviour, C. perfringens down-regulates a great
amount of genes, and in the dendogram it is located nearer to bifidobacteria. This decrease in certain genes is common to observed in dendritic cells stimulated with bifidobacteria. However, C.
perfringens induces an increase on IFNG expression so high as the other pathogen controls. Regarding to probiotics, we observe that lactobacilli trigger lesser up-regulation and induce down-regulation
of several genes expression. Among lactobacilli, we can observe that Group 6 produce the biggest activation of the assayed genes in dendritic cells and is located together with E. coli and S. typhimurium
in the dendogram. Furthermore, bifidobacteria increase the expression of a few genes and down-regulate a great amount of genes, specially CXCL10 and IFNB1. This assay could help to understand the
probiotic’s actions not only because they trigger a weak response, but also they work in an active way down-regulating specific genes.
ACKNOWLEDGEMENTS: This work has been possible thanks to the financial support from Instituto de Salud Carlos III (PI10/01647), Junta de Castilla y León (Consejería de Educación, VA016A10-2), Beca FPI-Junta de Castilla y León, Beca FPI-UVA and Phadia España.