Download Gene Expression Networks

NPTEL – Biotechnology -Systems Biology
Gene Expression Networks
Dr. M. Vijayalakshmi
School of Chemical and Biotechnology
SASTRA University
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 1 of 8
NPTEL – Biotechnology -Systems Biology
Table of Contents
1 GENE REGULATION AT THE SINGLE CELL LEVEL ............................................... 3
1.1 INTERPRETING PROTEIN DYNAMICS ............................................................................. 4
1.2 SPATIO TEMPORAL ANALYSIS OF GENE EXPRESSION USING SINGLE MOLECULE
TECHNIQUES ............................................................................................................. 5
2 REFERENCES ............................................................................................................. 8
2.1 TEXT BOOK .............................................................................................................. 8
2.2 LITERATURE REFERENCES ....................................................................................... 8
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 2 of 8
NPTEL – Biotechnology -Systems Biology
1 Gene regulation at the single cell level
Gene regulation is an intricate complex process, which involves genes, mRNAs and
proteins that dictate cellular phenotypes and their response to external stimuli. Recent
approaches employing genomics and proteomics and interactomic studies have helped
probe the structure and signalling of these complex networks. However, more
interesting aspects of the cell systems can be explored through the dynamics executed
by these networks. To probe the dynamics of gene networks is complex because of the
following reasons:
i. Simultaneous time resolved measurements of network components are required
for dynamics studies and they generally are not accurate.
ii. There is variability in responses even among the cells that are genetically identical.
Variation in cell parameters like cell size, stage of the cell cycle, metabolite
concentration and intrinsic stochasticity of biochemical reactions inside the cell
compel the cells to a differential response to the same external stimuli. Such
variations in dynamics are often measured by distributions of relevant observations
in a population which can be measured by techniques such as flow cytometry.
iii. Avoiding ambiguity in interpreting dynamic data requires tracking the fates of
individual cells in the population for a time span greater than the process time.
Since this time span equals several cell division cycles, collection and interpreting
data over cell division cycles is a predominant challenge.
Visualization of gene expression and single cell measurements at the intracellular and
intercellular levels at high resolution have made possible to understand the dynamics of
gene regulation in these circuits. Imaging mRNA activity using FISH, MS2-GFP,
molecular beacons, FRET and fluorescent microscopy at single molecule resolution
have given us a handle to understand the gene expression at single cell level. The
stochastic nature of gene expression and regulation are well studied using engineered
gene networks.
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 3 of 8
NPTEL – Biotechnology -Systems Biology
Newer and evolving techniques in the field have enabled real time measurement of
RNA. A major difficulty in using single fused fluorescence proteins is that they
sometimes fluoresce more than the cellular auto fluorescence. The MS2 tagging system
and RNA reporter RNA plasmid helps address this question. Once both the proteins are
expressed in live cells the multiple fluorophores fuse to MS2 capsid proteins, bind to the
MS2 binding sites in the UTR of RNA of interest. This technique gives a strong
fluorescent signal, which allows visualization of an individual RNA molecule. Real time
tracking of target RNA’s can also be done using in vivo hybridization with molecular
beacons. Molecular beacons are single stranded nucleic acid probes which contain a
fluorophore and a quencher that woo apart upon binding to target RNA sequence.
1.1 Interpreting protein dynamics
The green fluorescence protein (GFP) and its derivative proteins facilitate real time
visualization of proteins. Multi coloured fluorescence microscopy enables simultaneous
measurement of multiple protein concentrations and the relative roles of intrinsic and
extrinsic noise in gene expression. Fluorescence Resonance Energy Transfer (FRET)
utilizes a donor and acceptor fluorophore to visualize conformational changes in
individual molecules. The donor fluorophore transfers energy to the acceptor when the
two come together and change the wavelength of the fluorescence signal.
Fluorescence based techniques in imaging provide the flexibility to study gene
expression in single cells. Such studies show fluctuations in identical cells leading to the
thought that gene regulation predominantly controls cellular properties. The stochastic
variation in gene expression induces switching response in single cells creating binary
‘ON’, ‘OFF’ switches. Literature on signal processing in single cells (Issacs et al.,
Science. 2005), documents the relation between input and output signals in engineered
E.coli networks.
The relationship between the gene expression rate and the abundance of regulatory
proteins in single cells has been modelled using GRF, a gene regulation function
(Rosenfeld et al., Science. 2005). This study involved an engineered two-step
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 4 of 8
NPTEL – Biotechnology -Systems Biology
regulatory cascade in which the gene regulatory function in individual cells where
measured dynamically and simultaneously with the input output signals. Time-lapse
microscopy is employed to measure the GRF with population averaging. This interesting
experiment observed dynamic fluctuations of GRF in individual cells implying that
stochasticity in gene expression and minor variations in parameters limit the signal
transfer property of transcription networks.
Noise propagation through a three-step transcription regulatory cascade is probed by
the Van Oudenaarden group in E.coli systems. These experiments investigated the
abundance of gene products as different steps in the cascade and attempted to
correlate these evidences in single cell. This is done by measuring the expression
changes in the input and output genes by varying the concentration of the inducers in
the circuit.
Gene expression variations in individual cells may also influence the physiological
states with the endogenous pathways (Acar et al., Nature. 2005). The galactose
regulatory pathway in Saccharomyces cerevesiae was used as a model system to
investigate the determinants of stability in cellular memory. Constructed signalling
networks in E. coli, have been shown to generate bistability. Such networks have also
been shown to store memory through the creation of discrete states.
1.2 Spatio temporal analysis of gene expression using single
molecule techniques
While fluorophore tags enable detection of target molecules and their localization in vivo
fluorescence microscopy is widely adapted to determine the mean fluorescence of the
tagged proteins and also to locate single molecules. The high diffusion rate of single
molecules inside live cells makes their imaging difficult. This problem can be overcome
by reducing their diffusion rate by localizing the molecules to the membrane where they
diffuse slower than in the cytoplasm. A focused laser beam in a confocal microscope
achieves single molecule detection near field scanning optical microscopy (NSOM),
stochastic optical reconstruction microscopy, photo activated localization microscopy
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 5 of 8
NPTEL – Biotechnology -Systems Biology
and stimulated emission and depression (STED) are the most powerful techniques for
sub diffraction microscopy. These require scanning of large areas with a small window
and hence are too slow for characterizing live cell dynamics. In order to explain the
network dynamics completely, a mechanistic understanding of the networks is
important. This approach requires high Spatio temporal resolution of fluorescent
imaging and simultaneous measurement of the expression of network components
because study of the average properties of cell cultures is not enough and more
important is the sources of fluctuation and inter cellular variability. Micro fluidics and
optogenetics help track multiple species of RNA and proteins in vivo with sub cellular
evolution. Mechanical stimuli can regulate gene expression in live cells. The response
of an individual cell modification to a defined molecular environment depends on the cell
type and the mechanical stimulus. The mechanical stimuli require to elicit gene
expression in live cells have been probed using AFM, optical tweezers and magnetic
beads.
Fig 3 a) This device exposes single adherent cells to unconfined creep compression at forces ranging from 10nN to 200nN
or grater. Bean theory was used to calculate the force while measuring the resulting cellular deformation
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 6 of 8
NPTEL – Biotechnology -Systems Biology
Fig 3 b) Unconfined compressions of single cells modify cell gene expression by mechanotransduction. For chondrocytes,
this modification could occur in genes related to major processes involved in cartilage homeostasis and disease. Gene
expression was measured in compressed chondrocytes through single-cell RT-PCR
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 7 of 8
NPTEL – Biotechnology -Systems Biology
2 References
2.1 Text Book
1. Uri Alon, An Introduction to Systems Biology: Design Principles of Biological
Circuits,
2/e, CRC Press, (2006).
2.2 Literature References
1. Farren J. Isaacs, William J. Blake, James J. Collins, Signal processing in single
cells, Science, (2005), 307, 1886-1888.
2. Nitzan Rosenfeld et al., Gene Regulation at the Single-Cell Level, Science,
(2005), 307, 1962-1965.
E Joint Initiative of IITs and IISc – Funded by MHRD
Page 8 of 8