Diapositiva 1 - Uninsubria

Metodi post-genomici in
biochimica cellulare
Metodi post-genomici
Metodi post-genomici
Quantitative analysis of systems biology by taking advantage of available
genomic information at the level of
1.
2.
3.
4.
5.
6.
7.
SNPs analysis associated to disease or drug response
mRNA (transcriptomics)
Protein (proteomics)
Post-translational modifications (aka “modificomics”)
Surface exposure (surfomics)
Protein-protein interactions (interactomics)
Small metabolites (metabolomics) and their relations
(metabonomics)
Many other fantasy exercises (glycomics, lipidomics, allergenomics,
degradomics, excluding – perhaps – comics...)
G.B Smejkal, “I’m an –omics, you’re an -omics... ” Exp.
Rev. Proteomics 3 (2006) 383-385
Metodi post-genomici
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•
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Modelli cellulari e animali
Trascrittomica
Proteomica
Systems biology
Modelli cellulari e animali
(farmacologici e/o genetici)
Modelli cellulari
(farmacologici e/o genetici)
• Facilità di mantenimento e
trattamento
• Possibilità di combinare
trattamento farmacologico e
manipolazione genetica
• Utili per riprodurre un singolo
meccanismo
• Cellule umane (o murine)
Modelli animali
(farmacologici)
• Intero organismo vs. cellule
isolate
• Trattamento sistemico o lesione
chimica locale
• Possibilità di valutare l’effetto
anterogrado/retrogrado
Modelli animali (e vegetali?!?)
(genetici)
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•
•
•
•
Organismi modello
Genoma noto
Non solo topo!
Vita breve
Invertebrati (e piante…)
Trascrittomica
Trascrittomica
Trascrittomica
Trascrittomica
Trascrittomica
• Distanza Euclidea
• Correlazione di Pearson
Proteomica
• Non c’è correlazione tra quantità di mRNA e
quantità di proteina (Gygi et al., 1999)
• Il proteoma è un’istantanea del fenotipo a livello
biochimico
• Il proteoma tiene conto del processing delle
proteine
S.P.Gygi et al., Mol. Cell. Biol. 19, 1720 (1999)
Proteomica
• Metodi basati su 2-DE
• Metodi “gel-free”
Two-dimensional electrophoresis (2-DE)
Staining
pI 3-10 NL
170 kDa
95 kDa
72 kDa
170 kDa
95 kDa
72 kDa
55 kDa
43 kDa
55 kDa
43 kDa
34 kDa
34 kDa
26 kDa
26 kDa
17 kDa
17 kDa
Group B
Group A
pI 3-10 NL
Proteomica (2-DE)
Vantaggi
• Possibilità di caricare campioni non
purificati
• Risoluzione estremamente alta
• I gel 2 –DE sono collettori di frazioni
proteiche molto efficienti
• Proteine sono protette all’interno della
matrice del gel
Problematiche
• Gradiente di pH
• Limiti nel determinare proteine poco
rappresentate
• Capacità di caricare campione
• Proteine idrofobiche
• Proteine ad alto peso molecolare
Proteomica (2-DE)
• A global, unbiased approach
• Hypothesis-generating rather than hypothesis-driven
• A “find the difference” game between two conditions
Control
Treated
Proteomica (2-DE)
Proteomica (2-DE)
Proteine
Colorazione
Acquisizione
Analisi di immagine
Proteomica (2-DE)
Find the difference…
Controllo
Esordio Precoce
Esordio Tardivo
Proteomica (2-DE)
Find the difference…
Proteomica (2-DE)
Metodi statistici
Proteomica (2-DE)
Identificazione delle proteine
• Peptide mass fingerprinting
• LC-MS/MS
• Western blot (non globale)
Proteomica (2-DE)
Peptide Mass Fingerprinting
Proteomica (2-DE)
Peptide Mass Fingerprinting
Proteomica (2-DE)
Peptide Mass Fingerprinting (Limiti)
• La proteina non è presente nel database
• La proteina è ricca di modificazioni co/posttraduzionali
• Lo spot nasconde più di una proteina
Proteomica (2-DE)
Peptide Mass Fingerprinting (Limiti)
• La proteina non è presente nel database
• La proteina è ricca di modificazioni co/posttraduzionali
• Lo spot nasconde più di una proteina
Proteomica (2-DE)
LC-MS/MS
Differential in-gel electrophoresis (DIGE)
 Matching not needed
o High cost
 Spatially accurate
o Weak signal
 Sensitive to small
quantitative changes
o Only binary comparison
DIGE
Control [Cy5]
Pharmacological Treatment [Cy3]
Gel-based vs. Gel-free
Webb-Robertson and Cannon, Brief Bioinform 2007;8:304-317.
Poor detection of acidic- basic- proteins
poor solubility of membrane proteins
limited loading capacity of gradient pH strips (crowding effect)
Low reproducibility of gels
relatively low throughput
2D gels perform robust separations
2D gels are well-suited for PTM analysis
Parallel, quantitative and label-free readout
Monteoliva and Albar, BRIEFINGS IN FUNCTIONAL GENOMICS AND PROTEOMICS.
VOL 3. NO 3. 220–239.
Proteins do the job, not peptides
Proteomica (gel-free)
• Metodi quantitativi (ICAT, iTRAQ, …)
• Protein arrays
Gel-free
MS & Proteomics
Quantitative Proteomics
• Labelling (ICAT, iTRAQ, SILAC, 18O enrichment, …)
• Label free (AQUA, SRM/MRM, …)
Isotope-coded affinity tagging
(ICAT)
Isobaric Tagging for Relative and
Absolute Quantitation (iTRAQ)
Selected/Multiple reaction
monitoring (SRM/MRM)
Proteomica (gel-free)
• Protein arrays (e SELDI)
What next?
• You will call your preferred MS expert to ask
her/him to identify your spots
• You will get a list of protein names
• What tells you that list?
Systems Biology
Systems Biology
• Necessità di analizzare
un elevato numero di
informazioni (Network
analysis)
• Necessità di arricchire
un ridotto numero di
informazioni (Network
enrichment)
Systems Biology
• Interazione fisica
• Stesso pathway (KEGG)
• Stessa Gene Ontology
(GO)
Protein Networks
Cellular processes are regulated by protein
interaction networks
Protein networks:
• control development programs
• regulate signal transduction pathways
• manage metabolic pathways
• are based on physical interactions or
cellular localization
Protein networks
Graph: a graphical representation
of elements (nodes) connected by
edges.
Nodes are proteins, edges are
interactions
Protein networks
Hub: connecting several nodes
Subnetwork
Protein networks
Regulating interactions:
Controls
Inhibits
Feedback
Interacts with…
Building protein networks
•Co-occurrence in databases
•Physical interactions
•Genomic proximity
•Expression
•Proteomics
•Literature (pubmed)
•Pathways (KEGG, Reactome, …)
•GO Terms
Available Databases
• Free, online PPI data
– IntACT (EBI) http://www.ebi.ac.uk/intact/
– DIP http://dip.doe‐mbi.ucla.edu/dip/Main.cgi
– MINT http://mint.bio.uniroma2.it/mint/Welcome.do
– BIND/BOND http://bond.unleashedinformatics.com/
– HPID http://wilab.inha.ac.kr/hpid/
– UniProt http://www.uniprot.org/
– NCBI Entrez Gene http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene
• Pathways
– Reactome http://www.reactome.org/
– KEGG http://www.genome.jp/kegg/pathway.html
– Panther http://www.pantherdb.org/pathway/
– NCI Nature PathwayInteractionDb http://pid.nci.nih.gov/
– BioPATH http://www.molecular‐networks.com/biopath/index.html
• Commercial applications
– GeneGO, Ingenuity Pathway Analysis…
GO enrichment
Physical Interaction- IntAct
Physical Interaction- IntAct
Physical Interaction- IntAct
KEGG Pathways, Reactome
String 9.0
http://string-db.org/
String 9.0
BioProfiling
http://www.bioprofiling.de/
BioProfiling
Global networks
R spider
R spider implements the Global Network statistical framework to analyze gene list using as
reference knowledge a global gene network constructed by combining signaling and metabolic
pathways from Reactome and KEGG databases. Reactome is an expert-authored, peerreviewed knowledgebase of human reactions and pathways. Reactome database model
specifies protein-protein interaction pairs. The meaning of "interaction" is broad: 2 protein
sequences occur in the same complex or they occur in the same or neighbouring reaction(s).
Both, Reactome signaling network and KEGG metabolic network were united into the integral
network. For the human genome, the resulting integral network covers about 4000 genes
involved in approximately 50,000 unique pairwise gene interactions.
Reference: if you will find the results produced by R spider usefull, please cite:
1. Antonov A.V., Schmidt E., Dietmann S., Krestyaninova M.,Hermjakob H. R spider: a
network-based analysis of gene lists by combining signaling and metabolic pathways from
Reactome and KEGG databases Nucleic Acids Research, 2010, Vol. 38, No. suppl_2 W118W123
PPI spider
PPI spider implements Global Network statistical framework to analyze gene/protein list
using as reference knowledge a global protein-protein interaction network from IntAct
database. For the human genome, the reference network covers about 7960 genes
involved in approximately 40,000 unique pairwise interactions.
Reference: if you will find the results produced by PPI spider usefull, please cite:
1. Antonov A.V., Dietmann S., Rodchenkov I., Mewes H.W. PPI spider: A tool for the
interpretation of proteomics data in the context of protein protein interaction
networks. PROTEOMICS. Volume 9, Issue 10, 10 May 2009.
ProfCom
What is Cytoscape?
http://www.cytoscape.org/
What is Cytoscape?
“Cytoscape is an open source bioinformatics
software platform for visualizing molecular
interaction networks and integrating these
interactions with gene expression profiles
and other state data”
Fully customizable through plugins
http://www.cytoscape.org/
Cytoscape plugins
BiNGO
(Biological Network Gene Ontology)
Tool per determinare le GO statisticamente sovrarappresentate
Consensus PathDB
ConsensusPathDB-human
integrates functional interaction
networks including complex
protein-protein, metabolic,
signaling and gene regulatory
interaction networks in Homo
sapiens. Data originate from
currently 24 public resources for
functional interactions,
interactions that we have curated
from literature. Additionally,
biochemical pathways have been
imported from several databases
for use in pathway analyses. Data
are integrated in a complementary
manner and redundancies are
avoided.
MCODE
MCODE is a Cytoscape plugin that finds clusters (highly interconnected regions) in a
network. Clusters mean different things in different types of networks. For instance,
clusters in a protein-protein interaction network are often protein complexes and parts
of pathways, while clusters in a protein similarity network represent protein families.
Un esempio
(realizzato a Busto…)
Un modello cellulare per identificare
nuovi meccanismi e nuovi bersagli
terapeutici
Quali
condizioni?
Il Modello
Un nuovo
bersaglio!
Trova le
differenze!
Cosa hanno
in comune?
71
Il modello cellulare
•
La linea cellulare umana SH-SY5Y
incamera dopamina, ma la immagazzina
con difficoltà nelle vescicole
Simile a quello che succede nella
malattia di Parkinson
α-sinucleina
•
(Gómez-Santos et al., 2003)
La linea viene trasfettata stabilmente per
esprimere α-sinucleina or β-galattosidasi
I livelli di α-sinucleina sono alterati
nella malattia di Parkinson
72
Le condizioni sperimentali
-Sinucleina
Dopamina
Controllo
NON trattate
Controllo
trattate con
Dopamina
Effetto combinato
α-Sinucleina
NON trattate
α-Sinucleina
trattate con
Dopamina
73
Trova la differenza!
74
Trova la differenza!
Dopamina
11
α-Sinucleina
4
• sintesi proteica
• citoscheletro
• mitocondri
• trascrizione
• stress ossidativo
• mitocondri
• trasduzione del segnale
Entrambi i
fattori
8
75
Cosa hanno in comune?
Dopamina
α-Sinucleina
Entrambi i
fattori
76
Generare nuove ipotesi
NF-κB
Apoptosi
77
Ritorno al modello
VDAC2
Ctr
DA
VDAC1
Ctr
DA
VDAC2
Ctr
DA
VDAC3
Ctr
DA
VDAC2 4h
Inibitore GSK3β
ctr
ctr inib
DA
DA inib
Alberio, Fasano, Rizzuto e altri,
in preparazione
78
Partendo dal modello cellulare,
verso nuovi bersagli terapeutici
Alterazione della
Dopamina
Proteine che
cambiano
NF-κB, i VDACs,
GSK3β. Nuove
terapie?
79
Stage disponibili
• Proteomica differenziale del ruolo dei
mitocondri nella patogenesi della malattia di
Parkinson
• Validazione in larga scala di marcatori ematici
di malattia di Parkinson