MicroRNA circolanti: da messaggeri cellulari a nuova classe di biomarcatori del diabete e delle sue

complicanze

Francesco Dotta

UOC Diabetologia, Azienda Ospedaliera Universitaria Senese;Dip. Scienze Mediche, Chirurgiche e Neuroscienze, Università di Siena;

Fondazione Umberto Di Mario ONLUSToscana Life Science Park, Siena

Il sottoscritto Prof. Francesco Dotta dichiara di aver ricevuto negli ultimi due anni compensi o finanziamenti dalle seguenti Aziende Farmaceutiche e/o Diagnostiche:

- Astra Zeneca

- Eli Lilly

- GlaxoSmithKline

- Johnson & Johnson

- Merck Sharp & Dohme

- Novo Nordisk

pol II

nucleus

Drosha

DicermicroRNA

duplex

cytoplasm

RISC RISC

microRNA

mRNA 3’-UTR

mRNA 3’-UTR

microRNA

MicroRNAs

• MicroRNAs (miRNAs) are smallnon-coding RNA molecules of 19-24 nt

• miRNAs interact with 3’UTR ofmRNA target leading to inhibitionof gene expression throughtranscriptional inhibition or mRNAdegradation

• They have been shown to playimportant roles in many cellprocesses such as differentiation,proliferation and apoptosis

• MiRNAs are dysregulated innumerous pathologies leading tofunctional alterations

• Specific miRNA mimic/inhibitorsare now being tested in phase I/IIcilinical trials for several diseases

DGCR8Drosha

Gene microRNA

Trascrizione

Pri-miRNA

Drosha-DGCR8complex

Pre-miRNA

RNA Pol II

Esportina-5

Nucleo Citoplasma

DICER-1 Ago2

microRNA

RISC

Blocco della traduzione

Degradazione dell’RNA messaggero

Ago2microRNA

mRNA 3’UTR

Ago2

Ribosoma

microRNA

mRNA 3’UTR

1

2

I microRNA

I microRNA stabiliscono una nuova modalità di controllo delle funzioni cellulari

-Specificità: alcuni microRNA sono espressi in modo specifico indeterminati tessuti o cellule

-Ciclo cellulare: e.g. microRNAs miR-15b or miR-21 controllanol’espressione di geni del ciclo cellulare , CCND1, CCND9, CDC25a [Xia H. etal 2009]

-Apoptosi: e.g. miR-15a and miR-16 inducono apoptosi regolando ilgene anti-apoptotico BCL2 [Cimmino A. et al. 2005]

-Differenziamento/sviluppo: alcuni microRNA controllano ildifferenziamento cellulare

-Pluripotenza: recentemente un gruppo di microRNA è stato utilizzato perriprogrammare cellule somatiche in sostituzione dei fattori diYamanaka(cellule IPS -- Oct3/4, Sox2, Klf4, c-Myc) [Miyoshi N. et al 2011]

Membrana plasmatica

Insulina

Granulofilina

miR-9

miR-124a

miR-124a

Granulo secretorio di insulina

miR-96

miR-34

I microRNA regolano molti geni target e molti processi cellulari contemporaneamente

Wang T. et al 2014 BMC Bioinformatics

MicroRNAs and beta-cell inflammation

miRNAs role in the immune systemKey beta cell processes and the miRNAs involved

Filios R.S and Shalev A., Diabetes 2015

microRNAs are involved in pancreatic islet functions and in immune system homeostasis

Sebastiani G & Dotta F et al, JEI 2017

Circulating microRNAs: different secretion mechanisms

MicroRNAs in biofluids are potential excellent biomarkers

• Highly stable in clinical samples(e.g. plasma/serum)

• Minimally invasive

• Biomarkers for:

o Diagnosis

o Prognosis

o Treatment response

Circulating microRNAs expression profiling in T2D

Zampetaki A et al. Circ.Res. 2010

- Bruneck prospectic cohort study n=822 subjects

- Screening of 754 microRNAs in plasma samples

- 13 miRNAs differentiallyexpressed (downregulated)in plasma of T2D patients

Zampetaki A et al. Circ.Res. 2010

19 NGT subjects developed T2D over 10y observation period

Baseline microRNAs expression in 19 subjects vs matched CTRs

Circulating microRNA miR-126 is downregulated in T2D

Zampetaki A et al. Circ.Res. 2010

Endothelial miR-126 in T2D: a mirroring effect

T2D w/o complications

(n=12)

T2D with macrovascularcomplications

(n=12)

T2D with microvascularcomplications

(n=12)Gender (M/F) 6/6 8/4 6/6

Age (years) 67,6 ± 4,9 67,6 ± 5,6 67,5 ± 4,5 BMI (Kg/m2) 28,8 ± 4,7 28,9 ± 5,1 32,1 ± 8,2

Duration of T2D (years)

17,6 ± 4,4 15,7 ± 6,4 15,1 ± 4,3

HbA1c (%) 6.8 ± 0,8 7,3 ± 0,8 7,5 ± 0.9 Triglyceride

(mg/dl)137,0 ± 100 127,2 ± 66,9 137,5 ± 72,0

Total cholesterol (mg/dl)

157,9 ± 27.3 148,0 ±23,0 174,8 ± 31,2 ***

LDL cholesterol (mg/dl)

79,9 ± 18.2 81,3 ± 20,2 97,0 ±27,9

HDL cholesterol (mg/dl)

52,7 ± 8.4 41,4 ± 12,5 * 49,9 ± 13,8

Albuminuria (A/C)

0.4 ± 0.3 0,58 ± 0,53 4,3 ± 8,2 **

eGFR-MDRD (ml/min/1,73m2)

85,3 ± 17,8 80,4 ± 17,2 72,0 ± 21,0

eGFR-CDK-EPI (ml/min/1.73m2)

84,8 ± 12,7 80,0 ±12,6 73,7 ± 20,7

Circulating MicroRNAs expression profiling in diabetic complications

miR-31 is upregulated in sera of T2D patient with microvascular complications

Circulating MicroRNAs in EURODIAB prospective complications study

n=143 Controls

n=312 T1D patients with Cardiovascular disease or retinopathy or albuminuria at follow-up

Barutta F et al 2016. Acta Diab.

miR-126 is negatively associated to proliferative retinopathy

Barutta F et al 2016. Acta Diab.

PREVENT-1: included patients without retinopathy at baseline

PROTECT-1: included patients with non-proliferative retinopathy at baseline

Zampetaki et al 2015. Diabetes

miR-27b and miR-320a are associated with incidence and with progression of retinopathy

Zampetaki et al 2015. Diabetes

Erener S et al et al. JCI Insight 2017

Unbiased microRNA screening (745 miRNAs analyzed)

35 Diff Expressed

- 27 Upregulated - 8 Downregulated

Erener S et al et al. JCI Insight 2017

Erener S et al et al. JCI Insight 2017

*P < 0.05, **P < 0.01, ***P < 0.001, ****P < 0.0001 after 1-way ANOVA with Dunnett’s post-hoc test. n = 7–32.

Erener S et al et al. JCI Insight 2017

Follow up:1, 3, 6, 12 and 60 month after T1D

diagnosis

6 miRNAs at 3months predict beta-cell function

and glycaemic control outcome

Samandari N et al. Diabetologia 2017

miR-197-3p positively correlates with C-peptide at 6 and 12 months

Samandari N et al. Diabetologia 2017

I Cohort II Cohort6

mon

ths

12 m

onth

s

microRNAs are associated with islet autoimmunity

Snowhite et al. Diabetologia 2017

TrialNet cohort

- AAB+ vs AAB-- AAB+ Non Progressors (NP) vs AAB+ Progressors (P)

AAB+ vs AAB-

15 microRNAs

7 microRNAs

AAB+ Non Progressors (NP) vs AAB+ Progressors (P)

Snowhite et al. Diabetologia 2017

miR-21-3pmiR-21-3p miR-29-3p

miR-424-5p

Snowhite et al. Diabetologia 2017

Experimental design

RNA Extraction from 50ul of plasma

NOD non-progressors mice (22w) n=5NOD progressors mice (12-21w) n=5

Megaplex miRNA RT and preamplification reactions

TaqMan miRNA Array Card(PanelA 2.1)

Data Analysis(Expression Suite 2.1)

Single assay validation of differentially expressed

miRNA

Expression analysis of miRNAdifferentially expressed in plasma

in the pancreas (pancreatic islets and lymphocytic

infiltrate)

Test the expression of miRNAsdifferentially expressed in NOD

mice in human samples

Fold change cut off: <0.35; >2.5

P value cut off:<0.05 Mann Whitney on2^-dCT values

Mancarella F-Ventriglia G et al., Manuscript in preparation

Plasma microRNA profiles in NOD progressors diabetic mice

Plasma microRNAs single assay validation

Mancarella F-Ventriglia G et al., Manuscript in preparation

Laser Capture Microdissection

Quick Hematoxilin staining and visualization

Snap Frozen 2-Metilbutane

mouse pancreas

6um cryostat sections

Microdissection of the area of interest

RNA extraction and quality evaluation

microRNAs expression analysis by single assay RT

PCR

Pancreatic islets

Lymphocyticinfiltrate

Normoglycemicislets with score 0-1

and 2-3 were collected separatelyCD3

INS

Islets Heterogeneity

miR-409-3p Plasma-Pancreas mirroring

Mancarella F-Ventriglia G et al., Manuscript in preparation

miR-409-3p(historical samples)

ROC - miR-409-3p(historical samples)

Plasma miR-409-3p expression in T1D patients -Historical Samples

Mancarella F-Ventriglia G et al., Manuscript in preparation

Serum BD Vacutainer GEL Matrix Yellow top tubes

Plasma BD Vacutainer EDTA Purple top tubes

1300g 10min , 4°C 1300g 10min , 15-25°C

max 6h

1200g 20min , 10°C 1200g 20min , 10°C

Aliquots300-500ul

Aliquots300-500ul

-80°C Storage

Standard Operating Procedures to analyze microRNAs

miR-409-3p(validation samples)

ROC- miR-409-3p(validation samples)

Plasma miR-409-3p expression in T1D patients -Validation SOP Samples

Mancarella F-Ventriglia G et al., Manuscript in preparation

La prossima sfida per la Diabetologia:

La “Precision Medicine”

Si può parlare di nuova era della Precision Medicine perché oggipossiamo contare su:Database biologici su larga scala;Potenti strumenti di caratterizzazione “omica” del paziente;Strumenti informatici per la gestione di Big Data;

Il costo per sequenziare un genoma

Nat.Rev.Immunol., doi:10.1038/nri3820 , March 2015