poster - 31th german conference on bioinformatics 2016 ......title: poster - 31th german conference...

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Background and Objective: Materials and Methods: Results: Conclusion: CD14+ cells purified from bone marrow and blood of RA and OA patients undergoing hip replacement surgery were profiled with Affymetrix HG-U133 Plus 2.0 arrays. The BioRetis database was used for array analyses. For functional interpretation of array data Ingenuity Pathway Analysis and Gene Ontology were applied. 68 different reference transcriptomes of healthy bone marrow progenitors and various stages of activated and differentiated monocytes were used for more detailed functional interpretation. Flow cytometry was applied for profiling of monocyte subsets: CD14+CD16-, CD14+CD16+ and CD14dimCD16+ in bone marrow, blood and synovial fluid samples. immunoClust algorithm was applied for automated analyses of FACS data. Alteration of RA monocytes was evident already in bone marrow and was characterized by increased monocytopoiesis and/or premature release into circulation. Comprehensive analyses of Mo profiles by reference transcriptomes provided a very detailed insight into gene patterns related to maturation, differentiation and activation of Mo. Flow cytometry analyses of Mo subsets in RA bone marrow, blood and synovial fluid depicted increased expression of CD16 on Mo during their maturation and differentiation from bone marrow into blood and during their migration and activation from blood into synovium. The most obvious activation of Mo occurs in the joint and is depicted by a specific Mo subset that expresses the highest level of CD14, CD16 & CD163. Most biologics for rheumatoid arthritis (RA) target processes involved in monocyte activation. To determine when, where and how monocytes become involved in pathogenesis of RA, we analysed monocytes from bone marrow, blood and synovial fluid by gene-expression profiling and cytometry, and compared their activation patterns with osteoarthritis (OA). ArthroMark grant no 01EC1009A Gene expression profiling and cytometry analysis from bone marrow monocytes, blood and synovial fluid from rheumatoid and osteoarthritis patients Biljana Smiljanovic 1 , Till Sörensen 1 , Marc Bonin-Andresen 1 , Bruno Stuhlmüller 1 , Gerd R. Burmester 1 ,Andreas Grützkau 2 , Thomas Häupl 1 1 Department of Rheumatology and Clinical Immunology, Charité University Hospital, Berlin, Germany, 2 German Arthritis Research Center, Berlin, Germany Marc Bonin Department of Rheumatology and Clinical Immunology Charité University Hospital Charitéplatz 1 D-10117 Berlin Germany Tel: +49(0) 30 450 513 296 Fax: +49(0) 30 450 513 968 E-Mail: [email protected] Web: www.charite-bioinformatik.de Contacts: www.charite-bioinformatik.de 1. Transcriptomes of RA bone marrow and blood monocytes showed a minor overlap and more prominent alterations in blood 2. Functional analysis of RA-BM & RA-PB transcriptomes emphasised genes involved in inflammation and hemopoiesis 3. Functional analyses of RA-BM & RA-PB profiles by 68 reference transcriptomes showed the shifts toward immature profiles (shift to the left) 4. Monocyte maturation during migration from bone marrow via blood to synovial fluid in RA patients analyzed by FACS RA bone marrow Mo profile analysed by Molecular network - Ingenuity (IPA) & Gene ontology (GO) RA blood Mo profile analysed by Molecular network - Ingenuity (IPA) & Gene ontology (GO) (1) RA transcriptome of bone marrow (BM) Mo (2) RA transcriptome of blood (PB) Mo (3) Principal component analysis (PCA) of BM & PB profiles from RA & OA Mo 221 differentially expressed probe-sets between RA & OA bone marrow Mo 379 differentially expressed probe-sets between RA & OA blood Mo 571 differentially expressed probe-sets in BM & PB Mo from RA & OA patients OA-BM OA-PB RA_BM RA_PB RA OA RA OA Biological processes Genes anti-apoptosis Up-reg: CLU, FAS, IL10, SOCS3, YWHAZ Down-reg: ANXA1, NOTCH2NL, VNN1 hemopoiesis Up-reg: FLT3, IL10, PICALM, ZBTB16 Down-reg: NOTCH2NL Inflammatory Up-reg: FPR2, IL10, IL8, TNFAIP6, TPST1 Down-reg: ANXA1, C3AR1, VNN1 responsecell adhesion Up-reg: ALCAM, ARF6, FPR2, ITGA4, ITGA6, ITGB1 Down-reg: CD36 Biological processes Genes inflammatory response Up-reg: CCR2, CD163, FPR2, LTB4R, NLRC4, PXK Down-reg: CAMK1D, CCL2, CSF1R, ITGAL, MIF, TNF anti-apoptosis Up-reg: BAG4, CLEC5A, NAIP, SERPINB2, THBS1, VNN1 Down-reg: CCL2, FOXO1, RIPK2, TCF7L2, TNF cell cycle arrest Up-reg: CDKN2B, GADD45A, MAP2K6, MYC, THBS1 Down-reg: CDKN1C, GAS2L1, TCF7L2 hemopoiesis Up-reg: PICALM, RUNX1 Down-reg: BCL11A, CSF1R, IKZF1, ROGDI RA-BM profile showed a more immature profile than OA- BM and is characterised by early myelopoietic genes and genes up-regulated by G-CSF that indicated faster egress of Mo from BM RA-PB profile exhibited also a more immature profile than OA-PB, characterized by late myelopoietic genes from BM, pronounced G-CSF effects and absence of genes specific for CD16+ Mo - Mo from bone marrow, blood and synovial fluid were analysed by FACS - Mo subsets: CD14+CD16- & CD14~CD16+ - Frequency, CD14, CD16 & CD163 expression of Mo were analysed - immunoClust software for automated analysis of FACS data - CD14+CD16- Mo are the dominant subset in BM from RA & OA patients. - Frequency of CD14~CD16+ Mo, which is the more differentiated Mo subset, increased in PB and expression of CD14 decreased while expression of CD16 increased on this subset. - CD14++CD16+ subset appears only in SF and represents the most differentiated (the highest CD16 expression) and the most activated (the highest level of CD14 & CD163) Mo subset. Analysis of RA-BM profile by reference transcriptomes 68 Reference transcritpomes of bone marrow & blood samples Analysis of RA-PB profile by reference transcriptomes Bone marrow (RA & OA) Blood (RA & OA) SF & Blood (RA) Mo subsets CD14+CD16- & CD14~CD16+ BM & PB from RA & OA Mo subsets CD14+CD16+ & CD14~CD16+ & CD14++CD16+ from PB & SF of RA RA OA RA OA RA-BM profile Up-reg in RA-BM Down-reg in RA-BM Frequency CD14 expression CD16 expression CD163 expression 1. Late myelopoiesis 2. Weak TNF/LPS effect 1. Early myelopoiesis 2. Weak TNF/LPS effect 3. G-CSF effect 1. Myelopoiesis 2. TNF/LPS effect 1. Early & Late myelopoiesis 2. TNF/LPS effect 3. G-CSF effect RA-PB profile Up-reg in RA-PB Down-reg in RA-PB 3. CD16 + pattern

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Page 1: Poster - 31th German Conference on Bioinformatics 2016 ......Title: Poster - 31th German Conference on Bioinformatics 2016 (Gene expression profiling and cytometry analysis from bone

Background and Objective:

Materials and Methods:

Results:

Conclusion:CD14+ cells purified from bone marrow and blood of RA and OA patients undergoinghip replacement surgery were profiled with Affymetrix HG-U133 Plus 2.0 arrays. TheBioRetis database was used for array analyses. For functional interpretation of arraydata Ingenuity Pathway Analysis and Gene Ontology were applied. 68 differentreference transcriptomes of healthy bone marrow progenitors and various stages ofactivated and differentiated monocytes were used for more detailed functionalinterpretation. Flow cytometry was applied for profiling of monocyte subsets:CD14+CD16-, CD14+CD16+ and CD14dimCD16+ in bone marrow, blood and synovialfluid samples. immunoClust algorithm was applied for automated analyses of FACSdata.

Alteration of RA monocytes was evident already in bone marrow and wascharacterized by increased monocytopoiesis and/or premature release intocirculation. Comprehensive analyses of Mo profiles by reference transcriptomesprovided a very detailed insight into gene patterns related to maturation,differentiation and activation of Mo. Flow cytometry analyses of Mo subsets in RAbone marrow, blood and synovial fluid depicted increased expression of CD16 on Moduring their maturation and differentiation from bone marrow into blood and duringtheir migration and activation from blood into synovium. The most obvious activationof Mo occurs in the joint and is depicted by a specific Mo subset that expresses thehighest level of CD14, CD16 & CD163.

Most biologics for rheumatoid arthritis (RA) target processes involved in monocyte activation. To determine when, where and how monocytes become involved in pathogenesisof RA, we analysed monocytes from bone marrow, blood and synovial fluid by gene-expression profiling and cytometry, and compared their activation patterns withosteoarthritis (OA).

ArthroMarkgrantno01EC1009A

Geneexpressionprofilingandcytometryanalysisfrombonemarrowmonocytes,blood

andsynovialfluidfromrheumatoidandosteoarthritispatients

BiljanaSmiljanovic1,TillSorensen1,MarcBonin-Andresen1,BrunoStuhlmuller1,GerdR.Burmester1,AndreasGrutzkau2,ThomasHaupl1

1DepartmentofRheumatologyandClinicalImmunology,CharitéUniversityHospital,Berlin,Germany,2GermanArthritisResearchCenter,Berlin,Germany

MarcBoninDepartmentofRheumatologyandClinicalImmunologyCharitéUniversityHospitalCharitéplatz1D-10117BerlinGermany

Tel:+49(0)30450513296Fax:+49(0)30450513968E-Mail: [email protected]:www.charite-bioinformatik.de

Contacts:

www.charite-bioinformatik.de

1.TranscriptomesofRAbonemarrowandbloodmonocytesshowedaminoroverlapandmoreprominentalterationsinblood

2.FunctionalanalysisofRA-BM&RA-PBtranscriptomesemphasisedgenesinvolvedininflammationandhemopoiesis

3.FunctionalanalysesofRA-BM&RA-PBprofilesby68referencetranscriptomesshowedtheshiftstowardimmatureprofiles(shifttotheleft)

4.MonocytematurationduringmigrationfrombonemarrowviabloodtosynovialfluidinRApatientsanalyzedbyFACS

RAbonemarrowMoprofileanalysedbyMolecularnetwork- Ingenuity(IPA)&Geneontology(GO)

RAbloodMoprofileanalysedbyMolecularnetwork- Ingenuity(IPA)&Geneontology(GO)

(1)RAtranscriptomeofbonemarrow(BM)Mo (2)RAtranscriptomeofblood(PB)Mo (3)Principalcomponentanalysis(PCA)ofBM&PBprofilesfromRA&OAMo

221differentiallyexpressedprobe-setsbetweenRA&OAbonemarrowMo

379differentiallyexpressedprobe-setsbetweenRA&OAbloodMo

571differentiallyexpressedprobe-setsinBM&PBMofromRA&OApatients

OA-BMOA-PBRA_BMRA_PB

RA

OA

RA

OA

Biologicalprocesses Genesanti-apoptosis Up-reg:CLU,FAS,IL10,SOCS3,YWHAZ

Down-reg:ANXA1,NOTCH2NL,VNN1hemopoiesis Up-reg:FLT3,IL10,PICALM,ZBTB16

Down-reg:NOTCH2NLInflammatory Up-reg:FPR2,IL10,IL8,TNFAIP6,TPST1

Down-reg:ANXA1,C3AR1,VNN1responsecelladhesion Up-reg:ALCAM,ARF6,FPR2,ITGA4,ITGA6,ITGB1

Down-reg:CD36

Biologicalprocesses Genesinflammatoryresponse Up-reg:CCR2,CD163,FPR2,LTB4R,NLRC4,PXK

Down-reg:CAMK1D,CCL2,CSF1R,ITGAL,MIF,TNFanti-apoptosis Up-reg:BAG4,CLEC5A,NAIP,SERPINB2,THBS1,VNN1

Down-reg:CCL2,FOXO1,RIPK2,TCF7L2,TNFcellcyclearrest Up-reg:CDKN2B,GADD45A,MAP2K6,MYC,THBS1

Down-reg:CDKN1C,GAS2L1,TCF7L2hemopoiesis Up-reg:PICALM,RUNX1

Down-reg:BCL11A,CSF1R,IKZF1,ROGDI

• RA-BMprofileshowedamoreimmatureprofilethanOA-BMandischaracterisedbyearlymyelopoieticgenesandgenesup-regulatedbyG-CSFthatindicatedfasteregressofMofromBM

• RA-PBprofileexhibitedalsoamoreimmatureprofilethanOA-PB,characterizedbylatemyelopoieticgenesfromBM,pronouncedG-CSFeffectsandabsenceofgenesspecificforCD16+Mo

- Mofrombonemarrow,bloodandsynovialfluidwereanalysedbyFACS

- Mosubsets:CD14+CD16- &CD14~CD16+- Frequency,CD14,CD16&CD163expressionofMowereanalysed

- immunoClustsoftwareforautomatedanalysisofFACSdata- CD14+CD16- MoarethedominantsubsetinBMfromRA&OApatients.

- FrequencyofCD14~CD16+Mo,whichisthemoredifferentiatedMosubset,increasedinPBandexpressionofCD14decreasedwhileexpressionofCD16increasedonthissubset.

- CD14++CD16+subsetappearsonlyinSFandrepresentsthemostdifferentiated(thehighestCD16expression)andthemostactivated(thehighestlevelofCD14&CD163)Mosubset.

AnalysisofRA-BMprofilebyreferencetranscriptomes68Referencetranscritpomesofbonemarrow&bloodsamples AnalysisofRA-PBprofilebyreferencetranscriptomes

Bonemarrow(RA&OA) Blood(RA&OA) SF&Blood(RA)Mosubsets

CD14+CD16- &CD14~CD16+BM&PBfromRA&OA

MosubsetsCD14+CD16+&CD14~CD16+

&CD14++CD16+fromPB&SFofRA

RA

OA

RA

OA

RA-BMprofileUp-reginRA-BM

Down-reginRA-BM

Frequency CD14expression CD16expression CD163expression

1.Latemyelopoiesis

2.WeakTNF/LPSeffect

1.Earlymyelopoiesis

2.WeakTNF/LPSeffect

3. G-CSF effect

1.Myelopoiesis

2.TNF/LPSeffect

1. Early&Latemyelopoiesis

2.TNF/LPSeffect

3. G-CSF effect

RA-PBprofile

Up-reginRA-PB

Down-reginRA-PB

3.CD16+pattern