Principal
Investigator (PI)
Funding
Research
Cris Print's laboratory focuses on using
bioinformatic methods and integrative biology methods to understand disease.
A major challenge facing medical science at present is the integration of vast
and rapidly growing volumes of information into a holistic understanding of
disease. In some cases this integration involves the generation of mathematical
models such as gene regulatory networks - in other cases information can be
integrated using simple and common-sense methods. Information that may be
informative about disease processes includes:
- mRNA and miRNA information from microarrays or RNAseq
- genotype information from deep sequencing or SNP
analysis
- clinical information
-
digitised pathological information
- information from cell
biology and transgenic experiments.
Examples of research from our laboratory that attempt to integrate information
of different types synergistically to understand disease are detailed below and
cover the following general areas:
Some of this work is based in The University of Auckland, while other
projects are based in the Auckland Bioengineering Institute, Dr Andrew
Shelling's Medical Genetics Research Group, the biotechnology company GNI Ltd,
and Cambridge University in the UK, where Cris worked until mid 2005.
For general enquiries about this research please email
Cris Print.
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Lab members
-
Cherie Blenkiron
-
Daniel Hurley (Auckland Bioengineering Institute)
- Annette Lasham
- Sunali Mehta
- Andrew Miller (Auckland Bioengineering Institute)
-
Anita Muthukaruppan (Department of Obstetrics and Gynaecology)
- Li Wang
- Wendy Watkins
- Edward Walker (at The Plant and Food Institute)
- Deborah Wright
Current
NZ collaborators/co-PIs
Current overseas collaborators
- Hiromitsu Araki (GNI Ltd)
- Louise Hull and Maria Ohlsson-Teague (Adelaide University)
- Satoru Miyano, Seiya Imoto and Yoshi Tamada (Tokyo University)
- Stephen Charnock-Jones (Cambridge University)
The lab's ethos
For this work to be successful we believe that several points are important.
-
People. These multidisciplinary
projects require good collaboration between
specialists
in compuational biology, applied
mathematics, statistics, genetics, and most importantly with pathologists, physicians
and surgeons. In addition to these specialists, to
coordinate the projects it is essential to have generalists with a broad understanding of medicine, cellular and molecular biology, statistics
and computational biology. Postgraduate students in our laboratory are exposed to several research
disciplines and gain experience in collaborating and communicating across traditional research
boundaries.
- Conservative analysis. It seems most productive to minimise the
chances of "false discovery" by focussing on the intersection of different types
of information, and by setting a high threshold for accepting our hypotheses.
Methods such as Bayesian models in which uncertainty can be explicitly modelled
are
very attractive.
-
Validity. We do not get much satisfaction from simply combining high
content information from different sources, or from simply generating mathematical
models of the molecular aspects of a disease, no matter how elegant the methods
used are! Evaluation of this type of research by laboratory experiments or clinical
trials is important if we are to translate high content information into improved biological
understanding or clinical practice.
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General examples of our
research
(1) Endothelial cell biology
Endothelial cells line blood vessels
and orchestrate the growth and regression of blood vessels to meet the changing
demands of the tissues they supply. We have studied how endothelial cells contribute
to blood vessel biology and pathology using a number of techniques. We have focused
particularly on the process of endothelial cell apoptosis (programmed cell suicide).
We have studied
the role of apoptosis in blood vessel development using in vitro assays of angiogenesis,
in which human endothelial cells "grow" into blood vessels in tissue culture. Apoptosis occurs as the blood vessels grow (Figure 1, Duval et. al. Angiogenesis;
6; 171-183, 2003).
Figure 1. Apoptosis
(measured by caspase 3 activation, orange) occurs in the vessel-like structures
as they develop (click image to see higher resolution
version).
We have used tissue-specific transgenes to turn off the process
of apoptosis specifically in mouse endothelial cells. This has revealed that endothelial
cell apoptosis is essential for blood vessel development in embryos, since without
it embryos die before birth and show a multitude of developmental defects in small
blood vessels while maintaining normal development of large blood vessels (Figure
2, Duval et. al. Angiogenesis; 10; 55-68, 2007).
Figure 2. Placental blood vessels (brown) fail to develop in transgenic
mouse embryos in which endothelial cell apoptosis has been inactivated. WT = wild
type, TG = transgenic, F = foetal capillary, MS = maternal sinus and H = haemorrhage (click image to see
higher resolution version).
We are
currently using transgenic zebrafish to further study the role played endothelial cell apoptosis
in vascular development, in collaboration with
A/Prof Philip Crosier and
Dr Maria
Vega Flores.
Endothelial cell apoptosis is thought to be important in matching vessel anatomy
to tissue requirements for blood supply, and for patterning the cardiovascular system
during development, as well as for some types of cancer chemotherapy (stromal targeting
drugs). It is driven by an orchestrated "suicide program", which
includes protein-based (proteome), gene expression-based (transcriptome) and cell
surface carbohydrate-based (glycome) events. We have used gene arrays and glycomics
to map some of the events that may contribute to the process of endothelial cell
apoptosis (figure
3, FASEB J 18; 188-190, 2003). Using a similar approach, we collaborated
with Professor Jordan Pober from Yale University in the USA to understand how the
apoptosis regulator Bcl-2 may influence blood vessel maturation (Endothelium,
15(1); 59-71, 2008).
Figure 3. Summary of the potential roles that regulated RNA transcript abundance
and carbohydrate sulphation may play in preparing cells for apoptosis. Abbreviations;
ECM, extra-cellular matrix; HSGAG, heparan sulfate glycosaminoglycan (click image
to see higher resolution version).
We have used Bayesian Gene Network techniques in an attempt to infer the genetic networks that may underlie the process of apoptosis
in endothelial cells (figure 4). One of the master regulators of apoptosis hypothesised
by these networks (GABARAP) appears to be critical for apoptosis, since when its
expression is knocked down using RNAi, endothelial cell apoptosis is strongly inhibited
(Affara et. al. Philosophical; Transactions of the Royal Society; 362; 1469–1487, 2007).
Figure 4.
A graph representing a dynamic Bayesian gene network generated from apoptosis timecourse
data. Dots represents transcripts ("nodes") and arrows between the dots represent
potential cause and effect interactions between transcripts ("edges"). A hypothetical
master-regulator of apoptosis in these cells (GABARAP) is positioned at the top
of the network graph (click image to see higher resolution version).
In the female reproductive system cyclical angiogenesis and
vessel regression are precisely matched to the cyclical growth and regression of
the ovary and endometrium. In Cambridge our group have investigated the role played
by gene expression changes that may regulate angiogenesis in the endometrium and
in the disease endometriosis (e.g. Human Reproduction; 10; 2356-2366, 2004, figure
5).
Figure 5. Scatterplot showing gene expression changes induced in human endothelial
cells by soluble factors produced by proliferative-phase human endometrial epithelial
cells (click image to see higher resolution version).
Inflammation involves the passage of white blood cells from the lumen of
blood vessels into tissues, and appears to be controlled to a large extent by signalling
cascades within endothelial cells. In a student project in Partnership with Pfizer, we used gene arrays to map the RNA transcript
abundance changes that occur in endothelial cells during inflammation. This work
has revealed inflammation-associated patterns of gene expression in endothelial
cells and highlighted potential therapeutic targets. We are now using a new in vitro
model of inflammation to test the validity of our individual gene array results.
In this model we use RNAi techniques to "knock down" specific RNAs in endothelial
cells, and use computerised video microscopy to quantify the effects of this treatment
on the adhesion of flowing leukocytes to a stationary genetically modified endothelial
cell monolayer. Dr Christopher Kirton from Cambridge University visited our lab
in 2006 to help us set up this technique in Auckland.
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(2) Mechanisms of apoptosis of various cell types
Apoptosis is a genetically-programmed
form of cell suicide. In addition to studying the RNA and cell surface carbohydrate
changes that may contribute to this process in endothelial cells (described above),
we have also studied the role of NFkB- and HIF-dependant transcript abundance changes
in the regulation of neutrophil apoptosis (Walmsley et. al. Journal of Experimental
Medicine; 201; 105-115, 2005, figure
6). A PhD student co-supervised by Cris has also used proteomics to study the mechanisms of action of Bcl-2 family apoptosis regulators.
Cris has previously used gene knockouts to study the role of the apoptosis regulator
Bcl-2 family members in male germ cells, gut and leukocytes (e.g. PNAS 96: 14943-8,
1999, PNAS 95: 12424-12431, 1998, Oncogene 19: 3955-3959, 2000).
Figure 6. Scatterplot showing gene expression changes induced in hypoxia in human neutrophils, Journal
of Experimental Medicine; 201; 105-115, 2005 (click image to
see higher resolution version).
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(3) Research into new bioinformatic
and gene regulatory network techniques
Gene array studies have revealed a great
deal about cell biology and pathology. However, due to their technical limitations
they have sometimes proved frustrating and have sometimes produced results that
are very difficult to interpret. In collaboration
with computational biologists, our group has worked on the best ways to use basic
bioinformatic techniques to analyse gene array information (e.g. Schoenfeld et.al.
Angiogenesis; 7; 143-156, 2004) and implemented novel methods such as Independent
Component Analysis (ICA) (e.g. Saidi et. al. Oncogene; 23; 6677-6683, 2004, figure
7), as well as gene network methods (Bioinformatics; 24(7); 932-942, 2008;
Tamada et al.,
Pacific Symposium on Biocomputing; in press, 2008)
and Bayesian methods to combine in vivo and in vivo data (Bioinformatics;
23; 1936-44, 2007). These collaborations with computational biologists,
who can use their expertise to drive new computational method development ,while
we focus on biological interpretation and the laboratory components of the projects,
have proved very productive.
Figure 7. Independant Component Analysis (ICA) reveals patterns of gene expression
(called "components", denoted by *) associated with the loss of growth factor support
and subsequent apoptosis in endothelial cells, taken from FASEB J 18; 188-190, 2003
(click image to see higher resolution version).
We have
also combined siRNA gene knock-down techniques with Codelink' gene arrays to map
the transcriptome changes that occur in endothelial cells when over 400 different signalling molecules and transcription factors are reduced in abundance. In collaboration
with GNI ltd, we have used this gene array information
to infer endothelial cell Bayesian gene regulatory networks to assist with target
discovery. We are validating and tuning these network models using further siRNA
experiments and combining them with time course gene array information about endothelial
cell drug responses (e.g. Imoto et. al. Pacific Symposium on Biocomputing; 11; 559-571,
2006, figure
8). We believe that with careful use and extensive validation, gene
array studies combined with Bayesian inference techniques provide valuable tools
for target discovery and drug development (systems pharmacology). This is a fascinating
cross-disciplinary area, which merges the fields of biology and mathematics, and
translates research from academia to industry.
Figure 8. Conceptual view of our
use of Bayesian gene network analysis for systems pharmacology, Pacific Symposium
on Biocomputing; 11; 559-571, 2006 (click image
to see higher resolution version).
In collaboration with scientists in Adelaide we have combined microarray information
about mRNA and miRNA changes that occur in the disease endometriosis (Figure 9;
Molecular Endocrinology; in press 2008).
Figure 9.
Like others, we have found that analysis at the level of pathways rather than at
the level of individual genes is valuable for understanding the biology behind disease
processes. We have taken this approach to understand how molecules
in epithelial
cells and the stromal cells that surround them interact in the disease endometriosis (American Journal of Pathology; 173 (3); 700-715, 2008) (click image to see higher resolution version).
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(4) Gene Network analysis in cancer
In Auckland
we are inferring gene regulatory networks in three types of cancer cell; melanoma
(using A 375 cells), colon cancer, and breast cancer (using MCF-7 cells). We are inferring gene
networks in these cells based on gene array analysis of siRNA experiments, in which
approximately 70 transcripts are specifically "knocked down". This data is then combined with clinical microarray data sets form other groups.
These project is run
in close collaboration with the Auckland University Bioengineering Institute (Dr
Edmund Crampin and Prof Peter Hunter), Dr Mik Black from the University
of Otago, and the company GNI ltd. Ultimately, we hope to develop improved web-based
tools that combine molecular and clinicopathological information to better predict
individual patient prognosis and treatment outcome.
An example of a preliminary
Boolean melanoma cell gene network based on a growth factor-deprivation timecourse
experiment is shown in figure 10. This type of network
is being combined with Bayesian
gene networks and dynamical systems models using new computation methods we are developing as a framework that can be used by other
groups.
We are also studying the function of the putative master regulators
of cancer gene expression revealed by our gene networks using traditional cell biology
and xenograft assays. We have a particular interest in discovering novel molecular
signals that may mediate cross-talk between tumour cells and their supporting stromal
cells such as endothelial cells and leukocytes.
Figure 10. (A) k-means clustering
of A375 cell growth factor deprivation timecourse gene array data revels patterns
of gene co-regulation as A375 melanoma cells leave the cell cycle and begin the
process of apoptosis. (B) Boolean networks were inferred from this gene array data.
Gene-to-gene connections are shown. The transcripts with the greatest connectivity
to the regulation of other transcripts are placed nearest the top of the graph.
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Selected 2007 to 2010 publications
Yamaji M, Bielby H, Licence D, Chen W, Cook E, Smith S, PRINT C and
Charnock-Jones S. VEGF-A Loss in the Haematopoietic and Endothelial Lineage
Exacerbates Age-Induced Renal Changes. Microvascular Research.
In press 2010.
Mehta S., Shelling A., Muthukaruppan A., Lasham A., Blenkiron C., Laking G. and
Print C. Predictive and Prognostic Molecular Markers for Cancer Medicine.
Therapeutic Advances in Medical Oncology: 2, 125-148, 2010.
Tamada Y, Imoto S, Araki H, Nagasaki M, Print C, Charnock-Jones D S and Miyano
S. Estimating Genome-Wide Gene Networks Using Nonparametric Bayesian Network
Models on Massively Parallel Computers. IEEE/ACM Transactions on Computational
Biology and Bioinformatics, In press 2010.
Jeffs A, Glover A, Slobbe L, He S, Woolley A, Print C, Baguley B, and Eccles M.
A gene expression signature of invasion in metastatic melanoma cells.
PLoS ONE;
24(12):e8461, 2009.
Hentschell D.,M., Harfouche R., Piecewicz1 S., Basu S., Print C, Eavarone D.,
Kiziltepe T., Sasisekharan R., Sengupta S. Regulation of vasculogenesis by
glycosaminoglycans. Circulation; 120(19), 1883-92, 2009.
Ohlsson Teague E, Print C and Hull L. The role of microRNAs in endometriosis and
associated conditions. Hum Reprod Update, 16(2):142-65, 2010.
Araki H, Tamada Y, Imoto S, Dunmore B, Sanders D, Humphrey S, Nagasaki M, Doi A,
Nakanishi Y, Yasuda K, Tomiyasu Y, Tashiro K, Print C, Charnock-Jones S, Kuhara
S and Miyano S. Analysis of PPARα-dependent and PPARα-independent transcript
regulation following fenofibrate treatment of human endothelial cells.
Angiogenesis; 12(3), 221-229, 2009.
Ohlsson-Teague M, Van der Hoek K-H, Van der Hoek M B, Perry N, Wagaarachchi P,
Robertson S, Print C, and Hull L M. Differentially expressed microRNAs and their
mRNA targets constitute molecular pathways associated with endometriosis.
Molecular Endocrinology; 23(2); 265-75, 2009. (selected by Faculty of 1000
Medicine).
Brunet-Dunand S, Vouyovitch C, Araneda S, Pandey V, Vidal L, Print C, Mertani H,
Lobie P and Perry J. Autocrine human growth hormone promotes tumour angiogenesis
in mammary carcinoma. Endocrinology; 150;1341-1352, 2009.
Tamada Y, Araki H, Imoto, S, Nagasaki, M, Doi A, Nakanishi Y, Tomiyasu Y, Yasuda
Y, Dunmore B, Sanders D, Humphries S, Print C, Charnock-Jones S, Tashiro K,
Kuhara S, Miyano S. Unraveling dynamic activities of autocrine pathways that
control drug-response transcriptome networks. Pacific Symposium on Biocomputing;
2009: 251-63, 2009.
Enis D, Dunmore B, Johnson N, Pober J and Print C. Anti-apoptotic activities of
Bcl-2 correlate with vascular maturation and transcriptional modulation of human
endothelial cells. Endothelium, 15(1); 59-71, 2008. (This
paper gives examples of the use of pathway analysis and transcription factor target
analysis with basic microarray data sets).
Hull M, Rangel C, Borthwick J, Doig J, Johnson C, Smith S, Tavaré S, Print C, and
Charnock-Jones DS. Endometrial-peritoneal interactions during endometriotic lesion
establishment. American Journal of Pathology; 173(3); 700-715, 2008. (This paper gives an example of the use of xenograft experiments
to identify distinct molecular pathways in two different types of cell within the
same tissue).
Hirose, O., Yoshida, R., Imoto, S., Yamaguchi, R., Higuchi, T., Charnock-Jones,
D.S., Print, C., Miyano, S. Statistical inference of transcriptional module-based
gene networks from time course gene expression profiles by using state space models.
Bioinformatics; 24(7); 932-942, 2008.
Sykacek P, Clarkson R , Print C, Furlon R, Micklem G. Bayesian Modelling of Shared
Gene Function. Bioinformatics; 23; 1936-44, 2007 Evans AL, Bryant J, Skepper J,
Smith S, Print CG* and Charnock-Jones DS*. Vascular development in Embryoid Bodies.
Quantification of transgenic intervention and antiangiogenic treatment.
Angiogenesis 10; 217-226, 2007.
Cheng C-W, Bielby H, Licence D, Smith SK, Print CG, Charnock-Jones DS. Quantitative
cellular and molecular analysis of the effect of progesterone withdrawal in a murine
model of decidualisation. Biology of Reproduction; 76;
871-883, 2007.
Affara M, Dunmore D, Savoie C, Charnock-Jones S and Print C. Understanding Endothelial
Cell Apoptosis: What can the transcriptome glycome and proteome reveal.
Philosophical transactions of the Royal Society 362; 1469–1487,
2007. (This paper reviews general aspects of the biology and pathology of endothelial
cell apoptosis, and the ways in which advanced bioinformatic methods can address
questions in this field).
Duval H, Johnson N, Li J, Evans A, Chen S, Licence D, Skepper J, Charnock-Jones
D S, Smith S, and Print C. Vascular development is disrupted by endothelial cell-specific
expression of the anti-apoptotic protein Bcl-2. Angiogenesis
10; 55-68, 2007.
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