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

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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