Post on 11-Aug-2020
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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