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:[email protected] / [email protected]
IntroductionIn 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.
Material and Methods
Ficoll and Percoll solution density gradient centrifugation
Monocytes Dendritic cells derived from monocytes (moDCs)
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.
Harvest and keep cells onto Trizol ® untill their extraction.
IL-4 (500U/ml)GMCSF (1000U/ml)
Stimulation for 4 hours
RNA extraction and cleaned up and Reverse Transcription
Peripheral blood from 6 healthy
donors
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).
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Results
Gene Symbol
Fold Regulation
CCL2 10,6119
CD80 2,9459
CSF2 1056,1719
CSF3 1266,2631
CXCL10 7,3387
IFNA1 3,4806
IFNB1 6,1087
IFNG 57,6998
IL10 31,3454
IL12A 2,7105
IL1A 193,463
IL1B 587,9418
IL2 29,0704
IL6 388,7104
IL8 40,6665
IRAK2 11,6471
IRF1 2,3088
Gene Symbol
Fold Regulation
MAP2K3 10,8515
NFKB1 5,0323
NFKBIA 5,5711
PELI1 5,1836
PTGS2 590,6094
REL 4,1434
RIPK2 10,8368
TLR2 2,4273
TLR7 5,2061
TNF 44,3334
2 Gene Symbol
Fold Regulation
CSF2 120,8161
CSF3 7,543
IFNG 52,9298
IL10 2,856
IL1A 5,6403
IL1B 24,8188
IL2 8,5137
IL6 4,3657
IL8 4,0329
MAP2K3 3,1299
PTGS2 33,215
REL 2,2535
TNF 28,1654
Gene Symbol
Fold Regulation
CXCL10 -21,8607
IFNB1 -3,0923
3
Gene Symbol
Fold Regulation
CCL2 3,0323
CSF2 50,6936
CSF3 4,5232
IL10 3,2107
IL1A 21,5333
IL1B 35,136
IL6 4,5012
IL8 6,5173
IRAK2 2,412
MAP2K3 4,2772
PTGS2 23,4233
REL 2,7255
TNF 7,8538
Gene Symbol
Fold Regulation
CXCL10 -6,8258
IFNA1 -3,5326
IFNB1 -28,9985
IL2 -3,045
IRF1 -2,9194
NFKB2 -2,4778
NFKBIL1 -2,5962
TICAM2 -2,5273
Gene Symbol
Fold Regulation
CCL2 3,5973
CSF2 68,8851
CSF3 46,295
IL10 5,6801
IL1A 13,0701
IL1B 46,0762
IL6 13,6141
IL8 5,6081
IRAK2 2,8214
MAP2K3 5,3533
PTGS2 31,9635
TNF 5,3719
Gene Symbol
Fold Regulation
CXCL10 -4,1539
IFNA1 -4,0655
IFNB1 -19,0858
IKBKB -2,2394
IL12A -2,1917
IL2 -2,1888
IRF1 -3,5744
MAPK8 -2,4137
NFKB2 -2,0968
NFKBIL1 -2,1841
TICAM2 -2,9914
TOLLIP -2,0092
Gene Symbol
Fold Regulation
CCL2 11,5807
CSF2 132,6047
CSF3 292,1396
CXCL10 2,1221
IL10 27,0685
IL1A 51,0403
IL1B 129,6176
IL6 100,027
IL8 17,5964
IRAK2 5,2768
MAP2K3 4,5248
NFKB1 2,1695
NFKBIA 3,0298
PTGS2 105,1594
RIPK2 5,529
TLR2 2,8932
TNF 11,9809
Gene Symbol
Fold Regulation
IFNB1 -5,424
4 5
6 Gene Symbol
Fold Regulation
CCL2 2,3257
CSF2 72,8291
CSF3 7,7066
IL10 6,9872
IL1A 22,2682
IL1B 53,6656
IL6 16,0568
IL8 4,6569
MAP2K3 3,5618
PTGS2 25,5277
TNF 18,3381
Gene Symbol
Fold Regulation
CXCL10 -10,5161
IFNB1 -9,8707
IL2 -2,1033
IRF1 -3,3142
MAPK8 -2,0913
NFKB2 -2,165
NFKBIL1 -2,3649
RELA -2,2775
TICAM2 -2,8438
7
Gene Symbol
Fold Regulation
CSF2 18,7006
IL1A 4,4368
IL1B 7,9621
IL8 2,5512
MAP2K3 2,5706
PTGS2 7,3443
TNF 6,4423
Gene Symbol
Fold Regulation
CXCL10 -222,6368
IFNA1 -3,343
IFNB1 -34,1568
IKBKB -2,578
IL12A -5,8398
IL2 -2,8297
IRF1 -3,2827
MAP2K4 -2,378
NFKB2 -3,313
NFKBIL1 -2,0203
RELA -2,0651
TICAM2 -2,0991
TOLLIP -2,0556
Gene Symbol
Fold Regulation
CSF2 17,9123
CSF3 2,3981
IL1A 4,92
IL1B 14,1869
IL6 2,2965
IL8 2,3493
MAP2K3 2,0338
PTGS2 10,218
TNF 6,5127
8
Gene Symbol
Fold Regulation
CXCL10 -89,3227
IFNB1 -41,2893
IKBKB -2,2312
IL12A -2,9637
IRF1 -3,5319
MAP2K4 -2,1041
RELA -2,202
TICAM2 -2,4988
TOLLIP -2,1489
Gene Symbol
Fold Regulation
CCL2 10,9988
CD80 4,2165
CSF2 91,1504
CSF3 107,6358
CXCL10 6,435
IFNB1 2,4435
IFNG 13,8084
IL10 10,4676
IL1A 106,0239
IL1B 235,5998
IL2 3,2814
IL6 196,1398
IL8 20,2413
IRAK2 11,5273
MAP2K3 12,649
NFKB1 4,1605
NFKBIA 4,6945
Gene Symbol
Fold Regulation
PELI1 4,8596
PTGS2 198,1842
REL 3,1259
RIPK2 9,3557
TICAM2 2,8365
TLR2 3,4999
TLR7 4,7245
TNF 15,3839
PATHOGENS
LACTOBACILLI
BIFIDOBACTERIA
S. typhimuriumE. coli C. perfringens
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.
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.
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
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