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Systemic Lupus ErythematosusEvolving concepts in the pathogenesis

Dimitrios T Boumpas, MD, FACP

University of Athens

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What causes lupus?

How does the disease start?

New cells and biologic processes:

Neutrophils, autophagy and NETosis

What are the main pathogeneticmechanisms?

Tissue injury

Exposome ( UV light and microbiotta)

High-throuput techniques in SLE:

What and why

Genomics and transcriptomics arecoming of age

Outline: Evolving concepts in the pathogenesis ..

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This Galtonian distinction is overly simplistic as they interact in myriad ways to modify biology of all living

organisms (genetic factors influnce the enviromental exposures on the organism and while the latter

influence the expression or function of genes ( epigenetics)

Environment

(30-70%)

Genetic background

(30-70%)Immune response genes

Tissue susceptibility genes

Phenotype

Severity-Outcome

Disease

RASLE

HSC ?

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SLE at the beginning of the last decade

Antigen-driven, T-cell dependent production of

auto-antibodies. Known antigens (histonic

proteins)

Cytokines: IL-10. IFN-a and IFN-g play a role in

the pathogenesis

Disease paradigms: Th2 response withproduction of auto-antibodies

Co-stimulation-T cell help: Role of co-stimulatory

molecules: CD40/CD40L, CD28/B7/CTLA4

Formation of immune complexes and activationof the complement resulting in tissue injury

Cytotoxicity-direct auto(ab)-mediated injury

The role of innate immunity limited at complement and IFNa. TLRs anddendritic cells were not in our vocabulary. Genes?

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SLE at the end of the last decade

Apoptotic material is a source of autoantigens and

molecules with adjuvant / cytokine-inducer activity

(IFNa)

Nucleic acids linked to apoptosis are recognized by the

innate immune system via sophisticated sensors (TLRs,

NLRs) and facilitated by danger signals

Pre-clinical phase of the disease. Prevention strategies?

Genome-wide scans have confirmed the importance

immune response genes as well as of genes involved in

endothelial function and tissue response to injury.

New growth-survival factors for B cells: TRL- agonists,

Blys, April

Crow. NEJM 2009

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The evolving pathogenesis of SLE

New concepts in the pathogenesis

Genetics, functional genetics, epigenetics

-Known genes account for only 30% of the genetic risk. Epigenetics (methylationand acetylation-identical discordant twin methylation paterns differs)

Apoptosis, NETosis, autophagy :-Source of autoantigens and alarmins/adjuvants

Innate immunity:

- TLRs ( 3, 7 and 9), neutrophils, antimicrobial peptides, alarmins, Ts, IFNa

Adaptive immunity: DCs, B and T lymphocytes, IL-21, IL-17

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How does lupus start?

General autoimmune phase: auto-

antibodies common to other autoimmune

diseases (Ro, La)

Eventually, more specific antibodies against

nucleic acid containing histones (anti-Sm;

anti-RNP; anti-DNA)

Questions

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Questions

What is the source of the auto-antigens in lupus? Apoptosis,NETosis, autophagy

How are they recognized by the immune system? TLRs, NLRs

How does the body (normally) prevent recognition of self-nucleicacids?

What facilitates their recognition? Alarmins (HMBP),

autoantibodies

Why there is a dominance of immune response against nuclear

antigens which is so characteristic for the disease?

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The key role of dendritic cells in lupus

Exogenous factors/antigens (ie viruses) or auto-antigens

recognized by innate immune system receptors

activate DCs and B cells

production of IFNaand auto-antibodies(respectively)

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Microbial/viral or cellular ligands for TLRs can costimulate B cells to produce

autoantibodies as well as stimulate the production of type 1 interferons by DCs

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TLR-mediated autoimmunity

Activate autoreactive B or T lymphocytes

Activate APCs (DCs, B cells).

B cells from active SLE patients have increased

TLR-9 expression

Combined BCR and TLR stimulation more potent

Chromatin containing immune complexes 100 fold

more efficacious because of the presence of nucleicacids

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DCs are at the center of the innate and adaptive immune response

Present in all tissues; located in strategic locations

surveying the area

Two major subtypes: pDC and mDC ( CD11c+).

pDCs. IFNa factories

mDCs.Initiate the immune responses and

determine its type (innate vs adaptive, humoral vs

cellular); OR promote tolerance (mature mDCs

promote reactivity,

immature mDCs promote tolerance)
http://content.nejm.org/content/vol343/issue1/images/large/07f1.jpeg

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In lupus, a variety of factors (IFNa, immune complexes, TLRs) induce maturation of

mDCs, therefore promoting auto-reactivity (instead of tolerance)

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Enhanced IFNa responses in SLE

Increased levels of IFNa

Increased expression of IFNa-related genes correlates with activity and severity

Haplotypes IRF5 STAT4 determine IFNa production and risk for SLE

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AUTOPHAGY AND APOPTOSIS

Neutrophils

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Neutrophils

Key cells of the innate immunity and inflammation.

In active SLE patients, in cDNA microarray analysis, a strong

neutrophil signaturewith upregulation of genes encoding for

proteins involved in granulopoiesis, apoptosis, and neutrophil

adhesion (integrins) ( Nakou et al, Arthritis Rheum 2006)

SLE neutrophils are activated intravascularly, tend to aggregate

as a result of increased expression of cell adhesion molecules,

produce high amounts of reactive oxygen species (ROS), and have

increased turnover due to increased apoptosis.

In human lupus, we also have identified several microRNAs

regulating genes related to autophagy as well as evidence for

hyper-expression of these genes in DNA microarrays.

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Autophagy

Autophagy is a catabolic pathway during which cells break down their owncomponents during periods of starvation or stress.

Stress signals ranging from nutrient deprivation to immune signaling induce the

degradation of cytoplasmic material ( remodeling of the house).

Involved in both pathogen recognition and elimination as well as antigen

presentation and production of inflammatory cytokines such as IL-1.

Autophagy in antigen-presenting cells results in presentation of citrullinatedpeptides to CD4 T cells.J Exp Med.2011 Dec 19;208(13):2625-32
http://www.ncbi.nlm.nih.gov/pubmed?term=Ireland%20JM%20AND%20J%20Exp%20Med.http://www.ncbi.nlm.nih.gov/pubmed?term=Ireland%20JM%20AND%20J%20Exp%20Med.http://www.ncbi.nlm.nih.gov/pubmed?term=Ireland%20JM%20AND%20J%20Exp%20Med.http://www.ncbi.nlm.nih.gov/pubmed?term=Ireland%20JM%20AND%20J%20Exp%20Med.http://www.ncbi.nlm.nih.gov/pubmed?term=Ireland%20JM%20AND%20J%20Exp%20Med.

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Is autophagy involved in SLE?

Chloroquine, a well-established therapy in SLE, is general inhibitor of autophagy.

Chloroquine, also inhibits TLR-dependent type I IFN production in pDCs, whereas itdoes not interfere with the cytoplasmic DNA sensing pathway.

We have identified several micro-RNAs (miRNAs) differentially expressed in

peripheral blood mononuclear cells (PBMCs) of SLE patients that are predicted to

target autophagy-related mRNAs: mir-196 regulating IRGM, mir-106b regulatingatg4b, Bec-1,and Ulk-1, mir-15a regulating atg9a, and let7g regulating atg4b and

Ulk-2 (Stagakis E et al, Ann Rheum Dis. 70:1496, 2011).

GWAS have also linked SNPs inATG5to SLE susceptibility (Han JW et al, Nat

Genet. 4:1234, 2009).

Autophagymay contributes to the hyperactivation of innate immunity cells other

than pDCs and the development of autoimmune responses in SLE, via aberrant

activation of the cytosolic nucleic acid sensing pathways

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Autophagy regulates IFNa production in human pDCs

stimulated with LL37-DNA complexes.

(A) LL37-DNA complexes trigger autophagy in

human pDCs.

(B) (B, C)Autophagy inhibitors (wortmanin)

selectively block the release of IFNa in pDCsstimulated with LLL37-DNA complexes (G.

Chamilos and M. Gilliet, unpublished data)

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Increased rates of Autophagy and NETosis in SLE

neutrophils: Proceses are linked

Spontaneous release of thrombogenic tissue factor and HMGB1 decorated

NETS in SLE-in an autophagy-dependended fashion- activates the

coagulation cascade and contributes to endothelial and organ injury.

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NETs promote inflammation, immune reactivity and coagulation

NETs represent scaffolds of intact chromatin fibers and antimicrobial peptides

allowing the neutrophils to trap and disarm bacteria extra-cellularly

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Neutrophil extracellular traps (NETs) in lupus

Neutrophils in SLE release NETs which arecomplexes made of DNA, histones, and neutrophil

proteins

NETs contain antimicrobila peptides which facilitate

recognition of self-nucleic acids by TLRs and induce

type I IFN responses (Science Transl Med 2011)

Timely removal of NETs may be crucial for tissue

homeostasis to avoid presentation of auto-antigens

A subset of lupus patients have impaired NET

degradationdue to antibodies against DNase and this is

associated with nephritis (PNAS 2010)

PNAS 2010

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SLE NETs are loaded with antimicrobial peptides ( LL37) and alarmins ( HMGB1)

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Neutrophils in SLE

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Pathogenesis

Tissue injury

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Tissue damage in lupus: renal, brain, vessels

Systemic effects

Immune complexes and complement-activation

Inflammatory cells (neutrophils, macrophages, mast-cells), reactive oxygen species,

inflammatory cytokines (TNF, MCP-1, IFN)

Antibody-mediated tissue injury in NPSLE, APS, thrombocytopenia, nephritis

Cross-reactive antibodies to anti-DNA and glutamate receptors on neuronal cells mediate

excitotoxic neuronal death or dysfunction (PNAS 2010)

Self-anti-IgE in lupus activate basophils, promote autoantibody production (Nat Med.

2010)

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Tissue damage in lupus: renal, brain, vessels

Systemic effects

Immune complexes and complement-activation

Inflammatory cells (neutrophils, macrophages, mast-cells), reactive oxygen species,

inflammatory cytokines (TNF, MCP-1, IFN)

Antibody-mediated tissue injury in NPSLE, APS, thrombocytopenia, nephritis

Cross-reactive antibodies to anti-DNA and glutamate receptors on neuronal cells mediate

excitotoxic neuronal death or dysfunction

Self-anti-IgE in lupus activate basophils, promote autoantibody production and give lupus

nephritis a booster shot. (Nat Med. 2010)

Local effects at the tissue level

Locally (ie, kidney) produced IFNa or TNF cause tissue injury. Auto-reactive B and Tcells in the kidney

Defects in kallikreinsmay jeopardize the ability of lupus kidneys to protect themselves-

from the injury (Krasoudaki abstract EULAR 2012)

PD-1-ligand expressed by kidneys of lupus patients down-regulate infiltrating

lymphocytes

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Vascular damage in SLE: Neutrophils, DNA degradation, IFN-a

Neutrophils.

A distinct subset of inflammatory neutrophils (low

density granulocytes) induces vascular damage and

produces IFNa

Pathogenic variants of ITGAM increase the binding to

ICAM and the adhesion leucocytes to activated

endothelial cells

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Neutrophils.



produces IFNa



endothelial cells

Endothelium Impaired DNA degradation as a result of defect in repair

endonucleases (TREX1) increase the accumulation of

ss-DNA derived from endogenous retro-elements in

endothelial cells and may activate production of IFNa

IFNa increases damage and impairs its repair

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Neutrophils.



produces IFNa



endothelial cells

Endothelium

Impaired DNA degradation as a result of defect in repair

endonucleases (TREX1) increase the accumulation of

ss-DNA derived from endogenous retro-elements in

endothelial cells and may activate production of IFNa

IFNa increases damage and impairs its repair

IFNa

IFNa promotes an anti-angiogenic signature in SLE

and control EPCs/CACs, characterized by repression of

IL-1a/IL-1b, IL-1R1. IL-1b abrogates the deleterious

effects of IFN-alpha (Kaplan. J. Immunol. 2010)

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Clinical implications

Lupus flares: stress, UV irradiation, viral infections

UV activates the NALP1 inflammasome in the keratinocytes and produces IL-1 EBV - a major risk for lupus - promotes IFNa production by pDCs. Aberrant chronic

viral infection may be a source of IFN-a (A @R, 2009)

TLRs

Acquired defect in TLR signaling in lupus correlates with remission

Increased TLR-9 copy number or increased number of risk alleles correlates with

earlier onset and more severe disease

TLR antagonists: plaquenil. Use it during the preclinical phase when more specific

antibodies for lupus develop for primary prevention?

TLR-7 and 9 stimulation in B and DC cells promotes steroid resistance (Nature

2010). HCQ facilitates steroid action

Novel therapeutic targets

Anti-IFN, BAFF, TACI, anti-IL-1, co-stimulation modulation (PD-1/PD-1L)?

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Better understanding of the biology of disease and success

of biologicsin RA have created an impetus for new Rx

Biologic therapies

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Novel therapies in SLE

A. Targeting B cells

B cell depleting Anti-CD20 mAb (rituximab, ocrelizumab)

Modulating B cells Anti-CD22 mAb (epratuzumab)

Inhibiting B cell growth factors

Targeting plasma cells

Anti-BAFF (anti-BLyS) (belimumab)

TACI-Ig (atacicept)

Bosertan

B. Targeting T cells

Inhibiting costimulation CTLA4-Ig (abatacept)

C. Targeting cytokines Anti-IL-6 (tocilizumab)

Anti-IL-10

Anti-TNF (infliximab)

Anti-IFN (MEDI-545)

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Inhibition of B-cell growth factors

cell

Anti-BLyS(belimumab)

TACI-Ig

(ataticept)

BR3-Fc

(briobacept)

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Candidate patients for belimumab; Give it a chance to show what it can do

Residual disease activity-unable to taper

steroids or contraindications to steroids

Patients with moderate to high disease activity (i.e. SLEDAI 6

in spite of maximum standard of care immunosuppressive

therapy. Higher disease activity ( i.e. SLEDAI 8) is

associated with more robust responses especially if associated

with serologic activity (anti-dsDNA

positivity, low complement)

Patients unable to taper steroids to acceptable doses for

chronic use.

Refractory disease Patients who have failed to respond or are intolerant to

available standard of care therapies.

High risk for flares Patients with high serological activity /residual disease despitemaximum standard of care.

Patients with a history of recurrent flares.

High risk for disease progression Long-standing disease with multiple organ involvement.

Predictors of response to belimumab Higher disease activity SELENA- SLEDAI10 andcorticosteroid treatment.

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Complex Pathogenesis in autoimmune/inflammatory diseases

Environment

(30-70%)

Genetic background

(30-70%)Immune response genes

Tissue susceptibility genes

Phenotype

Severity-Outcome

Disease

RASLE

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Four healthy persons , iv endotoxin and analysis of human leukocytes

Immune responses are complex!!!!!!!

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Prototypical inflammatory cell 292 genes, red up, blue down-regulation

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Temporal changes in human blood leukocytes after LPS in normal volunteers

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Network analysis

Transient deregulation of WBC bioenergetics and modulation of the translational machinery

f

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The complexity of the system

Cells and molecules interact with each other and with ECM. Complexity resembles that of the CNS

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Autoimmune rheumatic diseases are even more complex!!!!

Rheumatic diseases are of complex aetiology withenvironmental and genetic factors interacting witheach other

Patients vary with regard to disease

manifestations, age of onset, prognosis andtherapeutic response

Disease phenotype is a consequence of 100s-1000s gene expression changes in multiple

affected tissues and immune effector cells

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High throughput technologies are required

Thus far rheumatic disease research has been mainly focused in theinvestigation of specific molecules and inflammatory pathways

Understanding the complex nature of rheumatic diseases as well as theimplication of both genetic and environmental factors requires high

throughput technologies

High throughput technologies represent combinations of basic biologicalmethods with automated biochemical, biological, optical and imagingmethods

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Integrative Methodologies to Study a Complex Disease

Complex Disease

Gene profiling MicroRNA arrays Proteomics

Computational Analysis

Identification of Novel Gene Targets

High throughput technologies in

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High throughput technologies in

Rheumatology

Rheumatoid arthritisProteomics and DNA microarrays

Systemic lupus erythematosusmiRNA and genome-scans
http://content.nejm.org/content/vol357/issue10/images/large/05f1.jpeg

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High-throughput technologies for novel risk genes in SLE

Level 1: gene polymorphisms

-Genome-wide association studies and functional genomics

Level 2: Gene expression

-cDNA microarrays

Level 3: Regulation of gene expression

-post-transcriptional, translational, post-translational

Level 4: Proteomics

- serum and/or tissue

High throughput studies in SLE

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High-throughput studies in SLE

Previous work in the lab using expression microarrays:

Up-regulated genes in the bone marrow of patients with active disease

including genes involved in cell death and granulopoiesis

Gene network analysis: 19 central nodes as major gene regulators

including ERK, JNK, and p38 MAP kinases, insulin, Ca2+and STAT3

Miicro-RNA signature in SLE:

Genome-wide expression studies, which are not influenced by deductive assumptions, provide an

unbiased approach for investigating the pathogenesis of complex diseases like SLE

Nakou, et al.Arthritis Rheum. 2008

Nakou, et al. PLoS One. 2010

Stagakis, et al.Ann Rheum Dis. 2011

MicroRNAs in SLE and Lupus Nephritis

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Deregulation of immune response

in the periphery

27 differentially expressed miRNAs in

PBMCs of SLE patients: miR 21

(Stagakis et al, Ann Rheum Dis, 2011)

MicroRNAs in SLE and Lupus Nephritis

Identify novel genes within the kidney

Genes controlling susceptibility of

end organ to damage

Proliferative /membranous nephritis

MicroRNA Signature of Lupus Nephritis

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24 differentially expressed miRNAs in

renal biopsy samples

of LN patients vs healthy controls

9 up-regulated 15 down-regulated

RelativeK

LK4Lucactivity

RelativeKLK4Luc

activity

Expressionlevels

NZW mice

NZB/W F1 mice

2 months old 6 months old

miR-422a KLK4 miR-422a KLK4

MicroRNA Fold Change

Decreased expression of KLK4

Decreased KLK4 luciferase activity

CHIP-Seq: upstream of KLK4 binding sites for IRF1 and RXRA

Administration of miR-422a antagomir in lupus prone mice

SLE genetics: where we stand

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Heritability is 66% (concordance rates 2456% in

monozygotic twins)

>30 associated non-HLA loci (n=18 reaching GWA

significance)

Limitations & difficulties:

Individual risk variants have a modest magnitude of

risk (odds ratios 1.22.3)

Known loci explain

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Functional genetics: Defective expression and function of PD-1

-an inhibitor of T cell activation - in human SLE:

+ + +

Pst I

PD1.3G/

G

PD1.3G/

A

PD1.3A/

A

Increased frequency of the regulatory PD1.3A

SNP in SLE patientsDisruption of a RUNX1-binding site inPD1 gene

Decreased PD-1 expression

in presence of PD1.3A SNP

Defective PD-1mediated

suppression of T cells

Bertsias et al. Arthritis Rheum 2009

SLE patients have defective induction of PD-1 in

an in vitro model of auto-reactivity

Mi i h it bilit i SLE

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Missing heritability in SLE

RNA sequencing and eQTLs

Common DNA variants alter the expression levels and patterns o human genes.

Loci responsible for this genetic control are known as expression quantitative trait

loci (eQTLs) and may account for the phenotypic variation and susceptibility to

complex diseases

We are integrating RNA-seqwith the single-nucleotide polymorphisms (SNPs)

derived from GWAS. This should allow detection of more eQTLs (including rare

eQTLs) than the arrays and also variants responsible for alternative splicing

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Missing heritability in SLE: work in progress

Transcriptome profiling in patients with SLE, both at active stage and

during inactivity / remission

Well-characterized cohort of patients with SLE and major organ

disease (renal, CNS, severe cytopenias, severe serositis)

Next-generation sequencing technology (RNA-seq)-Integration of

genotype data to perform eQTL mapping

Mouse studies: comparative transcriptome analysis

Collaboration with M Dermitzakis

A. Identifying QTLs for molecular

phenotypes: the link between a cis-

and a trans acting genetic variant

B. Inference of gene interaction

networks: infer the

relationship and molecular

Transcriptomics in SLE

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and a trans-acting genetic variant

and a molecular phenotype, such

as gene expression

relationship and molecular

interactions between genes

C. Inference of phenotype

interaction networks and

their integration with genetic

information:how the

variance of one phenotype

has an impact on the

variance of another

D. Additive and interaction

effects of genetic variants.A

set of genetic variants that

seem to have a linear

(additive) effect on molecular

phenotypesLymphopenia

Nephritis

Type I IFN expression

G Bertsias, K Gertzimanaki, E Frangou

G d E i t i SLE

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Genes and Environment in SLE

Creation of an inception cohort of individuals are at-risk to develop SLE (first

degree relatives of patients, or individuals with preclinical autoimmunity)

Longitudinal sampling of serum, RNA, and recording of environmental and

lifestyle factors

Study: gene to gene and gene to environment interactions and epigenetic

changes contributing to SLE

Gene and environment interactions in human autoimmunity

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Gene and environment interactions in human autoimmunityCretan autoimmunity cohort: patients and persons at risk for autoimmunity

Multiple autoimmune diseases (RA, SLE, AS, MS, thyroid, psoriasis/PsA, IBD)

Questions and issues

Family level

clustering of multiple autoimmune diseases within a single family

development of a given autoimmune disease in a patient and another one to the relative

Individual patient level

development of an autoimmune disease in a patient and its predictors

type of autoimmune disease (ie, SLE vs other autoimmune diseases)

0rgan involvement within one particular disease (ie kidney disease vs neurologic disease in lupus)

severity of the disease

General

sexual dimorphism( with the staggering 9:1 female to male ratio) in SLE

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Cohorts

Autoimmune d.

At risk for

Biobank

Whole blood

(PaxGene)

Serum

Lymphoblastoid

cell lines (EBV) Fibroblasts (iPSCs)

Genes & gene regulation

Environment

GWAS

RNA transcription (RNA-seq)

TF recruitment (ChIP-seq)

DNA methylation (eg, RRBS)

Chromosome conformation

capture (eg, 5C)

Microbiome

Metabolome

Toxicome

UViome

The Cretan Autoimmunity Cohort

Relatives with AD

Autoantbodies

MRI findings

Incomplete disease

Registry of families with multiple affected members with a single or multiple diseases

Long term F/U RA, SLE, SpA

Psoriasis, PSA

Autoim. thyroid d IBD

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Sampling of environment

Extrinsic factors: Nutriome, pollutiome, microbiome, UVomeetc

How to best sample for epigenetic effects or determine the epigenetic

signature for specific prenatal or postnatal environmental exposures?

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62

What causes lupus?





Tissue injury



What and why



Genomics and transcriptomics

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Iniatiatives which may impact on SLE

Human Genome Project

Human genetic polymorphism (HapMap)

Expression and regulationfunctional elements of the human genome (ENCODE,

Encyclopedia Of DNA Elements

Microbiome

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Explore how DNA impacts healthIdentify and understand the differences inDNA sequencing among human populations

Understand what all genes doDiscover the functions of human genes by

experimentation and by finding genes with similar functions in the mouse,yeast, fruit fly and other sequenced organisms

Learn what the rest of the Human Genome does (junk or repetitive DNA etc)

Identify important elements in the non-generegions that present in human

and conserved in other organisms

Beyond the HGP -Unmet needs

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Haplotype Map of the Human Genome

Reasons

To understand the evolution of our species and its history

Understand the genetics of diseases, esp. the more common complex ones such as

diabetes, cancer, cardiovascular, and neurodegenerative.

To allow pharmaceutical treatments to be tailored to individuals (pharmacogenomics)

Goals

Define patterns of genetic variation across human genome

Guide selection of SNPs efficiently to tag common variants Public release of all data (assays, genotypes)

HapMap Project

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HapMap Project

The HapMapProject tests linkage between SNPs in various sub-populations (

Whites, Asians, Africans etc).

For a group of linked SNPs recombination may be rare over tens of thousands of

bases

A few "tagSNPs" can be used to identify genotypes for groups of linked SNPs

Makes it possible to survey the whole genome with fewer markers (1/3-1/10th)

SNPs arent everything: Copy Number Variations

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y g y

Redon et al. Nature 2006

Cost of sequencing constantly drops, while read length per

i i

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sequencing run increases

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The 1000 Genomes Project is the first project to sequence the genomes of a large

number of people, to provide a comprehensive resource on human genetic variation.

The goal is to find most genetic variants that have frequencies of at least 1% in the

populations studied.

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ENCODE & Human Disease

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What is ENCODE ?

Encyclopedia Of DNA Elements

A National Human Genome Research Institute (NHGRI) international

public research consortium

Main object: to identify all functional elements in the human genome

sequence.

Parallel projects for mouse and other model organism genomes

(mouseENCODE& modENCODE).

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ENCODE in numbers

10 years of research

1640 datasets

147 different cell types

Several different approaches to study:

RNA transcription (eg. RNA-seq),

Transcription factor recruitment (eg. ChIP-seq),

Chromatin structure (eg. Dnase-seq),

DNA methylation (eg. RRBS),

Chromatin interactions (eg. 5C).

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ENCODE results (1)

>30 research articles published last September in high impact journalsNature, Science& Genome Research.

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ENCODE results (2) 80% of the human genome participates in at least one biochemical RNA- and/or

chromatin-associated event in at least one cell type.

400.000 regions with enhancer-like features and 70.000 regions with promoter-likefeatures.

RNA production quantitatively correlates with chromatin structure.

Many non-coding variants in individual genome sequences lie infunctional regions.

SNPs assoc iated wi th d isease by GWAS are enrich ed wi th in n on -coding funct ional e lements

Relevance to human disease?

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ENCODE & disease

34% of GWAS SNPs overlap with DHSs (Dnase I

hypersensitive sites).

12% of GWAS SNPs overlap with transcription

factor occupied regions.

Red bars: GWAS SNPs Blue bars:control SNP sets

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ENCODE & autoimmune diseases

Overlap of disease phenotypes to selected transcription-factor-binding sites (left matrix) or DHSs

in selected cell lines (right matrix), with a count of overlaps between the phenotype and the cell

line/factor.

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Regulatory elements & SLE (1)

Ernst et al, Nature, 2011

Individual variants from a GWAS study for SLE were strongly enriched in enhancer states

specifically active in relevant cell types.

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Regulatory elements & SLE (2)

Ernst et al, Nature, 2011

SNP rs9271055 associated with lupus

coincides with a lymphoblastoid

(GM12878) strong enhancer and

strengthens a motif for ETS1, apredicted activator of lymphoblastoid

enhancers. This factor is further

implicated by lupus-associated variants

that directly affect the ETS1locus.

Example of a GWAS locus where a disease SNP affects

a conserved instance of a predicted causal motif

Autoimmune disease associated SNPs cluster in IRF9 interaction network

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Autoimmune disease associated SNPs cluster in IRF9 interaction network

SNPs in DHSs associated with autoimmune diseases repeatedly localize in recognition sequences for

transcriptional regulators (labeled ellipses) that interact with IRF9. Arrows indicate directionality of

relationship; dotted lines represent indirect interactions.

Gene-environment interactions

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he steady increase of chronic inflammatory disease

Steady increase from asthma to Type 2 DM, to RA to MS to IBD since WWII.

Increased awareness leading to increased diagnosis certainly plays a role

Follows industrialization and urban living

Not accounted by genetics alone; environmental factors

Epigenetic variables could represent the missing link between environment andgenetics ie methylation patterns in drug induced lupus.

-A transient prenatal exposure to a specific environmental stimulus may lead topersistent and heritable epigenetic changes

Increasing public health concern


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must be important for the pathogenesis of chronic inflammatory diseases

Extrinsic factors: Nutriome, pollutiome, microbiome

How to best sample for epigenetic effects or determine the epigenetic

signature for specific prenatal or postnatal environmental exposures?

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Increasing data support a gene /environment interaction: smoking & R

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Kallberg et alAnn Rheum Dis 2011;70:508511.

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Mi bi d SLE

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Microbiome and SLE

Gender bias: androgens influence microbiota ( castration)

Microbiota elevate serum testosterone

Microbiota affects the expresion of genes that may be protectingfrom autoimmunity (Cell 2013)

Microbiota alter sex hormone levels and regulate autoimmunity in

T1D ( Science 2013)

Microbiota affects the degradation of food to atherogenic molecules

( NEJM 2013)

NATURE| ARTICLE Published online15 September 2013

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Transcriptome and genome sequencing uncovers

functional variation in human Genome sequencing projects are discovering millions of genetic variants in humans, and

interpretation of their functional effects is essential for understanding the genetic basis ofvariation in human traits.

Here we report sequencing and deep analysis of messenger RNA and microRNA from

lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Projectthe firstuniformly processed high-throughput RNA-sequencing data from multiple humanpopulations with high-quality genome sequences.

We discover extremely widespread genetic variation affecting the regulation of most genes,with transcript structure and expression level variation being equally common butgenetically largely independent.

Our characterization of causal regulatory variation sheds light on the cellular mechanismsof regulatory and loss-of-function variation, and allows us to infer putative causal variantsfor dozens of disease-associated loci.

Altogether, this study provides a deep understanding of the cellular mechanisms oftranscriptome variation and of the landscape of functional variants in the human genome.

Systematic identification of transeQTLs as putative drivers

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y p

of known disease associations Nature 2013

Identifying the downstream effects of disease-associated SNPs ischallenging.

To help overcome this problem, we performed expression quantitative traitlocus (eQTL) meta-analysis in non-transformed peripheral blood samplesfrom 5,311 individuals with replication in 2,775 individuals.

We identified and replicated transeQTLs for 233 SNPs (reflecting 103independent loci) that were previously associated with complex traits at

genome-wide significance. Some of these SNPs affect multiple genes in transthat are known to be

altered in individuals with disease: rs4917014, previously associated withsystemic lupus erythematosus (SLE)1, altered gene expression of C1QBandfive type I interferon response genes, both hallmarks of SLE2, 3, 4.

DeepSAGE RNA sequencing showed that rs4917014 strongly alters the 3UTR levels of IKZF1in cis, and chromatin immunoprecipitation and

sequencing analysis of the trans-regulated genes implicated IKZF1as thecausal gene.

Variants associated with cholesterol metabolism and type 1 diabetesshowed similar phenomena, indicating that large-scale eQTL mappingprovides insight into the downstream effects of many trait-associatedvariants
http://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.htmlhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.htmlhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.htmlhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.htmlhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.htmlhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.htmlhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.htmlhttp://www.nature.com/ng/journal/vaop/ncurrent/full/ng.2756.html

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What causes lupus?





Tissue injury



What and why



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