Information Processing in Genetic Regulatory Networks Ofer Biham - PowerPoint PPT Presentation

Information Processing in Genetic Regulatory Networks Ofer Biham Mor Nitzan Hanah Margalit Yishai Shimoni Pascale Romby Baruch Barzel Pierre Fechter Adiel Loinger Azi Lipshtat Oded Rosolio Assaf Pe’er Yael Altuvia

Network and motifs T ranscriptional network of E. coli Motifs A A b c d e Other modules A A B A B b

Regulation Mechanisms  Difgerent levels of regulation  Transcriptional regulation  Post-transcriptional regulation (by sRNA-mRNA int.)  Post-translational regulation (by protein-protein int.) Transcriptional regulation Post-transcriptional regulation A B B m m S A m A B B gene b gene a gene b gene a

Information processing Transcriptional regulation A B C m C c

Input Functions Diverse two-dimensional input functions control bacterial sugar Genes, Kaplan, Bren, Zaslaver, Dekel and Alon, Molecular Cell 29, 783 (2008).

Multi-layer regulatory circuits Transcription regulation Post-transcriptional Integrated network Multi-layer feed-forward loop regulation by ncRNA ncRNA Asaf Peer, Mor Nitzan, Zohar Itzhaki, Hanah Margalit Transcription factor Transcription regulation Post-transcriptional regulation

Combination of regulations at difgerent levels A C m A m C S B gene c gene a gene b

Staphylococcus aureus  Pathogenic bacteria  Cause a wide range of human diseases  Disease manifestations depend on the expression of numerous virulence factors  Within S. aureus virulence pathways lies a regulator switch that is induced by a quorum sensing signal

Quorum sensing for a growing population  At low numbers, violent bacteria will be quickly targeted for degradation  Only at higher numbers, the bacteria become virulent.

Quorum sensing for a dense population  Outer bacteria act as a shield  Inner, protected bacteria excrete violent proteins

S. aureus virulence path

The Switch Quorum Quorum Sensing Sensing RNAIII Rot spa hla Adhesins, camouflage Exotoxins, Adhesins, camouflage Exotoxins, α α proteins -hemolysin proteins -hemolysin (defensive state) (offensive state) (defensive state) (offensive state)

A Simpler Switch Regulator Target 1 Target 2

Selector Switch

Selector Switch without activator/repressor activator repressor Target 1 Target 2

Double Selector Switch Top Regulator Bottom Regulator Target 1 Target 2

The model- rate equations (sRNA regulator) (mRNA transcripts of TF ) (TF protein) . (TF - promoter complexes) (mRNA transcripts of target 1) (mRNA transcripts of target 2) (Target 1 proteins) (Target 2 proteins ) (sRNA - target mRNA complexes)

Switching on and ofg sRNA TF Target 1 Target 2 Time (min)

Response to a spike

Leakage of Target 1 b N T 1 u TP Leakage T = b b (1 N ) (1 N ) T s + + TP u d s T m sRNA TF Target 1 Target 2

Mixed Feedback Loop

Bifurcation Diagrams

Stochastic Trajectories

Life-times of bistable states

Deterministic vs. Stochastic Models τ P S = S τ + τ S A

Probability Distribution

sRNA-target interaction E. Levine, Z. Zhang, T. Kuhlman and T. Hwa, Plos. Biol. (2007)

Fine-tuning of target expression E. Levine, Z. Zhang, T. Kuhlman and T. Hwa, Plos. Biol. (2007)

Post transcriptional network in HEK293 Cells miRs targets

Crosstalk between Competing endogeneous RNAs (ceRNAs) miR-Y mRNA target 2 mRNA target 1 ; Salmena et al., Cell 146, 353 (2011); Tay et al., Cell 147, 344 (2011) Bosia et al., Plos One 8, e66609 (2013); Figliuzzi et al., Biophys J. 104, 1203 (2013)

Crosstalk between ncRNAs

Crosstalk between mRNAs through their common regulators

Fast Transmission of Signals A (a) Wild-type (c) (b) R 0 R 0 R 0 R 0 R 0 R 0 Knock-down Knock-down of T0 of T0 T 0 T 1 T 0 T 1 T 0 T 1 T 0 T 1 T 0 T 1 T 0 T 1 B (a) Wild-type (c) (b) R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 Over-expression Over-expression of R0 of R0 T 1 T 1 T 1 T 1 T 1 T 1 C (a) Wild-type (b) (c) (d) R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 R 0 R 1 Knock- down of T10 Knock- down of T10 T 0 T 1 T 2 T 0 T 1 T 2 T 0 T 1 T 2 T 0 T 1 T 2 T 0 T 1 T 2 T 0 T 1 T 2 T 0 T 1 T 2 T 0 T 1 T 2

Signal Propagation – Experimental Data

Subnetwork of sRNA Regulators and theirTargets R 0 R 1 R 2 R 3 R 4 R 5 R 0 R 1 R 2 R 3 R 4 R 5 T 0 T 1 T 2 T 3 T 4 T 5 T 0 T 1 T 2 T 3 T 4 T 5

Decay Rate of the Signal R 0 R 1 R 2 R 3 R 4 R 5 R 0 R 1 R 2 R 3 R 4 R 5 T 0 T 1 T 2 T 3 T 4 T 5 T 0 T 1 T 2 T 3 T 4 T 5

Correlations in the Network

Summary  We have studied information processing in genetic regulatory networks that involve difgerent levels of regulation  These networks combine sharp on/ofg type regulation with fjne tuning processes, fast and slow processes, synchronization and subtle coordination  Further progress will require experiments both at the single cell level and at the cell population level

Transcriptional vs. Post-transcriptional regulation Transcriptional Post-transcriptional Response time Slow Fast Regulation type Sharp On/Ofg Enables fjne-tuning Regulator-target Non-stoichiometric Stoichiometric interaction Regulation TF copy number and Relative copy numbers of strength afginity to promoter sRNAs and mRNAs and their determined by afginity Directionality Directional – from Bi-directional regulator to target Energetic cost Protein synthesis RNA synthesis

Combination of regulations at difgerent levels A C m A m C S B gene c gene a gene b

Three variants of the DSS sRNA TF TF TF sRNA TF Target 1 Target 2 Target 1 Target 2 Target 1 Target 2

Dynamics of DSS variants

Leakage in target genes