NEW STRATEGIES IN AIT BIOMARKERS BASED ON METABOLOMICS Dr. Domingo Barber Director IMMA Universidad CEU San Pablo
Disclosure In relation to this presentation, I declare the following, real or perceived conflicts of interest: Type Employment full time / part time Spouse / Family member employment / engagement Research Grant (P.I., collaborator or consultant; pending and received grants) Other research support Speakers Bureau / Honoraria Ownership interest (stock, stockoptions, patent or intellectual property) Consultant / advisory board Company None None None None None None ALK, AIMMUNE A conflict of interest is any situation in which a speaker or immediate family members have interests, and those may cause a conflict with the current presentation. Conflicts of interest do not preclude the delivery of the talk, but should be explicitly declared. These may include financial interests (e.g. owning stocks of a related company, having received honoraria, consultancy fees), research interests (research support by grants or otherwise), organisational interests and gifts. EAACI dedicated to Allergy Science, committed to your Health 2
Translational Research Allergy 2018 Submitted Clarification of pathophysiology 1 Clinical knowledge Electronic health records Clinical research 2 Epigenetics Exposomics Allergomics Genomics Epidemiology Transcriptomics Immunology Metabolomics Clinical signs and symptoms Microbiomics Prediction of prognosis Identification of biomarkers Facilitation of diagnosis 3 Individualized treatment
Allergy 2018 Submitted
COST 100 Therapeutic options in Allergy 10 1 Disease Severity Symptomatics Specific Immunotherapy Biologics EAACI dedicated to Allergy Science, committed to your Health
Why we need new biomarker strategies? -Biomarkers more closed to disease expression -Biomarkers that can be used to compare different intervention strategies -Biomarkers that provided evidence of the value of specific allergy intervention EAACI dedicated to Allergy Science, committed to your Health 6
HOW SIT WORKS?
Immunological response to SLIT Results at 2 years
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Sistemic intevention effects are expected afer 3 years Varona et al, Allergy 2018
Duration of IT is critical for sustained effect
December 08, 2003 Glaxo Chief On Drug Efficacy Allen Roses comments MOLECULAR in public on the statistics regarding pharmaceutical response rates. PROFILES "The vast majority of drugs - more than 90 per cent - only work in 30 or 50 per cent of the people," Dr Roses said. "I wouldn't say that most drugs don't work. I would say that most drugs work in 30 to 50 per cent of people. Drugs out there on the market work, but they don't work in everybody."
Metabolomics Definition. The study of the metabolites present within an organism, cell, tissue Metabolites are the ending / intermediate molecules from the metabolism Our phenotype could be described by the collection of metabolites and their levels in our organism Any pathology will result in specific alterations of some metabolites These can be potential biomarkers of diagnosis, prognosis or targets for pharmaceutical treatment EAACI dedicated to Allergy Science, committed to your Health 15
Work-Flow Hipothesis generation Analytical Validation Clinical Validation Sample Size: 20 s 100 s 1000 s Metabolites: 10.000 s >100 4-10 Metabolic profiling analysis (non-target): Relative abundance ~100s biomarker candidates Target analysis: Accurate quantification ~10 confirmed biomarkers Other platforms (immunoassays/ microchips/ routine lab-test) Biomarkers panel Potential targets for new drug treatments
Metabolomics in a model of Grass Pollen Allergy Severity EAACI dedicated to Allergy Science, committed to your Health 17
2018
A Claudin1 Occludin E-cadherin IL33 R elative copy num ber Non-allergic Mild Severe P O S T N R e la tiv e c o p y n u m b e r B % a r e a % a r e a % a r e a 8 0 6 0 * 4 0 2 0 0 C D N o n -a lle r g ic M ild S e v e r e 8 0 * 6 0 4 0 2 0 0 E F N o n -a lle r g ic M ild S e v e r e 8 0 * 6 0 4 0 2 0 0 G 1 5 * * 2 5 * N o n -a lle r g ic M ild S e v e r e 2 0 1 0 5 1 5 1 0 5 Rosace D et al, J Allergy Clin Immunol 2018 0 M ild S e v e r e 0 M ild S e v e r e
C la u d in 1 O c c lu d in e -c a d h e rin E C 5 0 to p ro filin A 1 * * * BAT correlates with Occludin loss 0.1 0.0 1 0.0 0 1 0.0 0 0 1 B N o n -a lle r g ic M ild S e v e r e Non-allergic Mild Severe 8 0 8 0 8 0 6 0 6 0 6 0 4 0 4 0 4 0 2 0 2 0 2 0 0 0.0 0 0 1 0.0 0 1 0.0 1 0.1 1 E C 5 0 Spearman r = 0.2302 P-value= 0.2580 Rosace D et al, J Allergy Clin Immunol 2018 (In press) 0 0.0 0 0 1 0.0 0 1 0.0 1 0.1 1 E C 5 0 Spearman r = 0.6471 P-value= 0.0037 0 0.0 0 0 1 0.0 0 1 0.0 1 0.1 1 E C 5 0 Spearman r = 0.1937 P-value= 0.3759
VEGFa % a r e a Masson s trichrome C o lla g e n fib e r s (% ) DAPI epithelial cells / area (pixels ) Hematoxilin & Eosin E p ith e lia l th ic k n e s s ( m ) B 1 8 0 * * A Non-allergic Mild Severe 1 6 0 1 4 0 1 2 0 1 0 0 8 0 N o n -a lle r g ic M ild S e v e r e D 1 0 0 8 0 * * * C 6 0 4 0 2 0 0 N o n -a lle r g ic M ild S e v e r e F 1 0 0 * E 9 0 8 0 7 0 6 0 5 0 4 0 N o n -a lle r g ic M ild S e v e r e G H 5 0 4 0 * * 3 0 2 0 1 0 0 Rosace D et al, J Allergy Clin Immunol 2018 N o n -a lle r g ic M ild S e v e r e
CD11c/ CD4 CD3 C D 3 + c e lls /a re a (p ix e ls ) CD11c C D 1 1 c + c e lls /a re a (p ix e ls ) A CD3/CD11c/CD4 Non-allergic Mild Severe B 8 * ** * *** 6 4 2 C D 0 2 0 1 5 E p C T E p C T E p * * ** C T N o n -a lle r g ic M ild S e v e r e 1 0 5 E 0 E p C T E p C T E p C T N o n -a lle r g ic M ild S e v e r e Rosace D et al, J Allergy Clin Immunol 2018
Allergy 2018. In press
Background results. LC-MS (ESI +) OPLS- predicted model R 2 = 0.99 Q 2 = 0.75 Classes were assigned based on the prediction ecuation: Controls Mild Moderate Severe n=6 n=5 n=6 n=8 SEVERITY
Results ROC curve using multivariate metabolites Mild vs Moderate Using 7 metabolites EAACI dedicated to Allergy Science, committed to your Health 25
KEY TO UNDERSTAND UNDERLYING PROCESSES Pyruvic acid Warburg metabolism activation Lactic acid Healthy Subjects METABOLOMICS Sphingolipid metabolism stress Multiple augmented lysophospholipids Sphingosine 1-P LPC 16:0 TRANSCRIPTOMICS Altered platelet functions Adhesion Aggregation Shape Change Increasing Severity of Food Allergy Granule Secretion Vs δg αg Obeso et al Allergy. In press Severe Food Allergic Phenotype
SPHINGOLIPID METABOLISM ** * * Sphinganine-C17 Sphinganine-C17 analogue * ** * * Sphinganine-P Sphingosine Sphingosine-P EAACI dedicated to Allergy Science, committed to your Health 27
EAACI dedicated to Allergy Science, committed to your Health
Allergy 2018. In press
Role of local CpG DNA methylation in mediating the 17q21 asthma susceptibility gasdermin B (GSDMB)/ ORMDL sphingolipid biosynthesis regulator 3 (ORMDL3) expression quantitative trait locus
Conclusions -Omics allow a better understanding of allergy disease progression -Is possible to define a combination of biomarkers to classify patients in base to severity -This classifiction could be used to decide intervention strategies and to monitor effect -Metabolic alterations, in particular sphingosine metabolism and energy metabolism identify disease progression - Is possible to compare effect of different intervention strategies and to justify the value of specific intervention, that will be essential for ethiological management of the disease EAACI dedicated to Allergy Science, committed to your Health 31
Why do we need stratification? Mild -Focus on prevention Moderate: -SIT Severe: - IT poor risk/benefit - Biologics(stabilization/reverssion)
Acknowledgements Hospital Virgen del Puerto, Plasencia María Isabel Alvarado, MD María del Carmen Dominguez, MD Angel Vega, MD Hospital Universitario de la Princesa Carlo Blanco, MD, PhD Tania Ramon, MD Maria Teresa Belver, MD Francisco Vega, PhD Hospital Clínico San Carlos, Madrid Montserrat Fernandez-Rivas, MD, PhD Guadalupe Marco, MD, PhD Leticia Sanchez, MD Manuel de Pedro, MD HM Sanchinarro, Madrid Marcela Santaolalla, MD Mercedes Arnas, MD PI13/00477 PI15/02256 PI16/00249 ARADyAL RD16/0006/0015
San Pablo CEU University: IMMA Maria Escribese, PhD Domenico Rosace BSc Coral Barbas, PhD Tomas Chivato MD,PhD Juan Rodriguez-Coira BSc Alma Villaseñor, PhD David Obeso BSc Leticia Mera BSc Elisa Zubeldia BSc Marina Perez-Gordo, PhD Cristina Gomez-Casado,PhD Adoración Martin PhD Paloma Fernandez, PhD Javier Moratinos Ricardo Arroyo Virginia Garcia Tomas Barker BSc Marisa Delgado BSc