A Human Cytome Project - an idea - Update 17 Dec. 2005

A Human Cytome Project - an idea
http://ourworld.compuserve.com/homepages/pvosta/humcyt.htm

By Peter Van Osta, MD

Hi,

As the on-line version of my article on the Human Cytome Project and the
application of cytomics in medicine and drug discovery (pharmaceutical
research) evolves, I put the updated version in this newsgroup for
reference. The original "question" on a Human Cytome Project was posted in
bionet.cellbiol on Monday 1 December 2003.


Vision

Attaining new insight in the biology of man, shaping medicine,
biotechnology, drug discovery and development into a premier precision tool
of the future, for the improvement of disease diagnosis, disease outcome
prediction and curing the diseases of mankind.
Introduction

This article is dedicated to the patients hoping and waiting for new
treatments of unmet medical needs and the improvement of existing diagnostic
techniques and therapies. It is also dedicated to all the scientists working
in basic and applied research, working hard to deliver these new drugs and
treatments. A lot of hard work has been done, but a lot more is needed for
the future. Improving medicine, drug discovery and development is not a
simple endeavour. Although this article is critical about the (evolution of
the) overall process it does not want to diminish the individual
contributions of scientists and physicians over the years which save and
improve the lives of many people. Let us look at the present with the future
in mind.

The completion of the Human Genome Project (HGP) holds many promises for the
understanding of the genetics of man and the involvement of genes in human
diseases. However the use of this information has to be viewed from another
perspective as is currently being done, if we want to use this knowledge to
improve medicine more efficiently. Predicting the dynamics of the cell and
its fate in diseases from the genome upwards is likely to fail due to the
complexity of metabolic processing and environmental influences on the
cellular metabolism and the phenotype of the entire organism. Going "from
genes to health" improvement requires an understanding of disease processes
beyond the boundaries of genomics and proteomics. We need a better
understanding of cellular physiology and beyond. The "Book Of Life" is a
novel, not a dictionary.

The clinical reality of disease processes extends beyond the present-day
disease models and the (current) boundaries of scientific development. When
we close the doors of our labs behind us and as physicians are confronted
with the clinical reality of diseases in the outside world, our disease
models fail all too often, as we can witness in the diagnosis and treatment
of complex diseases. This is also painfully obvious in the dramatically high
attrition rates during clinical development of new drugs.

When the endpoint of research is not only an experiment in a laboratory, but
to have an impact on the clinical reality of everyday pathological
processes, we fail to deliver in more than 80 to 90 percent of all drugs
being developed. Reality extends beyond the frontiers of science. Outside
the boundaries of scientific knowledge, significant parts of
(biological/clinical) reality remain un-explained for and not well
understood.

Drug discovery and development has to come up with drugs which can stand the
test of clinical reality, but is being squeezed between the failing
(theoretical) disease models and the demands for success of pharmaceutical
companies and society. Applied research has to provide the step stones to
cross the river from basic theoretical disease models to clinical reality,
ideally without getting our feet wet or drowning before we reach the other
side of the river.

How do we close the gap from model to clinic and find new directions for
research? The functional correlation between genome structure and clinically
expressed disease is too low to lead to functional predictions from the
genome and even proteome level upwards, without taking into account the
spatial and temporal dynamics of cells, organs and organisms. Pathological
processes have to be viewed from another organizational level of biology in
order to capture the dynamics of in-vivo processes involved in diseases.

The current bottom-up view on genomic and proteomic research suffers from a
correlation and prediction deficit in relation to the entire organism. The
genome and proteome are the omega of biological research, not the alpha of
drug discovery or disease treatment. From disease to gene we may find a
link, but turning around and go back to develop a treatment for the clinical
disease fails in many cases. We may find that a gene or genes may be part of
a disease process, but we cannot explain the entire disease process from the
genome level alone. A gene may be involved in a disease, but the entire
disease process is not contained within the gene. To discover the
involvement of a gene or protein in a disease, does not predict the
potential for successful development of a treatment for the clinical disease
entity as such.

In-vivo variation is not an artefact of life, but a fact of life. The
extraction of the appropriate attributes of a biological process in health
and/or disease requires capturing the spatial and temporal dynamics of its
manifestations at multiple scales and dimensions of biological organization.
Disease entities express themselves in a space-time continuum in which their
physical and chemical attributes evolve in a highly dynamic way. Capturing
the appropriate features and disease describing parameters from the
background noise of their surrounding processes and structures is more
difficult than finding a needle in a haystack.

On Monday 1 December 2003 I posted a message about the idea of a Human
Cytome Project (HCP) to the bionet.cellbiol newsgroup (Van Osta P, 2003). It
seems that it was the right moment to ask the question, as there were
already ideas emerging on the role of the cell as the final arbiter in the
production of metabolic products and also the concept of predictive medicine
by cytomics (Valet G, 2003).

The idea of a Human Cytome Project is already being discussed at scientific
conferences (FOM 2004, ISLH 2004, ISAC XXII, EWGCCA 2004 .). At Focus on
Microscopy (FOM) in Philadelphia on Wednesday afternoon, 7 April 2004, the
idea of a Human Cytome Project was for the first time discussed at a
scientific meeting. A round table discussion was held at the European
Microscopy Congress (EMC). The next major conference with workshops on the
Human Cytome Project (HCP) and cytomics is the ISAC XXIII 2006 Conference in
Quebec, Canada.
Already articles start to appear on the idea (Valet G, 2004; Valet G, 2004b;
Valet G, 2004c; Valet G, 2005a; Valet G, 2005b).
As the idea of a Human Cytome Project seems to have generated some interest
in the scientific community, I decided to put the original message and
question on my personal website for reference, so here it is.

Overview of related articles on this website

The original posting of the idea can be found on this webpage.

Personal interest and background

Scientific background about the idea can be found this webpage

The potential impact on the efficiency of drug discovery and development

A proposal of how to explore the human cytome

A concept for a software framework for exploring the human cytome

References

Monday, 1 December 2003 10:57:46 +0100

Hi,

I was wondering if there is already something going on to set up a sort of
"Human Cytome Project"? In my opinion the hardware and most of the software
seems to be available to set up such a project? For the cellular level,
light-microscopy based reader technology would be very interesting to use?

Studying and mapping the genome, transcriptome and proteome at the
organizational level of the cell for various cell types and organ models
could provide us with a lot of information of what actually goes on in
organisms in the spatio-spectro-temporal space?

I have been thinking (working) about a concept which could provide the basic
framework for exploring and managing this cellular level of biological
organization research on a large scale, but I would like to know if there is
already some thought/work going on in the direction of setting up an
initiative such as a "Human Cytome Project" ?

This is just an idea, so I am really interested to hear if there is
something in it, or even if it is not worth while what I just wrote.

Best regards,

Peter Van Osta.

Why a Human Cytome Project?

Human Genome Project

".. Nearly two centuries ago, in this room, on this floor, Thomas Jefferson
and a trusted aide spread out a magnificent map ... the map was the product
of his courageous expedition across the American frontier, all the way to
the Pacific ... Today, the world is joining us here in the East Room to
behold a map of even greater significance. We are here to celebrate the
completion of the first survey of the entire human genome. Without a doubt,
this is the most important, most wondrous map ever produced by humankind..."
President of the USA, Bill Clinton, June 20, 2000

The Human Genome Project (Lander ES, 2003; Venter JC, 2003) has set a new
milestone in medicine and the understanding of human biology (Guttmacher,
A., 2002; Guttmacher, A., 2003). Since its conception in 1986, it has
answered many questions, but it has also left us with more questions to
answer and it opened new horizons for exploration (Dulbecco R., 1986;
Collins F., 2003). The results of the Human Genome Project lead to a first
estimate that there are only about 34,000 genes in the human genome and by
the end of 2003 the number was reduced to some 25,000 genes (Claverie J.-M.,
2001; Wright F. A., 2001; Pennisi E., 2003). Now at the end of 2004 the
euchromatic sequence of the human genome is complete, the number of genes is
estimated to be about 20,000 to 25,000 (Collins FS, 2004).

The Caenorhabditis (C. elegans) genome is comprised of over 18,000 genes.
The fruit fly (D. melanogaster) genome consists of about 13,000 genes and as
such it has fewer genes than C. elegans, although as an organism it is far
more complex. Gene number alone does not predict functional complexity.
Although there is much more variation in the sizes of the genomes, this is
not reflected in the number of genes.

The functional uncoupling of the dynamics of cellular function to its
genomic gene-count came as a shock. The complexity and diversity of
organisms is not reflected in the structural complexity of their genomes
alone, but to a large extent it is hidden in the dynamics of gene expression
and cellular processing. As there is no linear relation between the
complexity of an organism and the physical structure of its genome, there is
also no one-on-one relation between the phenotype of an organism and its
genome. Relatively small differences between organisms, such as man and
chimpanzee do result in large functional differences in gene processing and
functional expression.

The structural relatedness of the human and chimpanzee genome, does not
explain the large difference in brain function for which gene expression
profiles in the brain are a better predictive instrument (Caceres M, 2003;
Fortna A, 2004; Uddin M, 2004). Functional differences between chimpanzee
and man are more outspoken in the brain than in other organs. Gene
expression differences are more related to cerebral physiology and function
in humans than gene sequences. Epigenetic phenomena within individual cells
and differential processing in different cell types have more predictive
power than the piecemeal and one-dimensional gene sequence approach, when
applied on complex structures such as the brain (Wilson KE, 2004).

>From single gene and genome to the entire cell

Now we are starting to use the information coming out of the Human Genome
Project, people start to understand that the dynamics of the cell and its
fate in disease processes cannot simply be explained from its individual
genes, genome or its proteome. Although all cells in the human body share
the same genome, there is considerable heterogeneity in their phenotype and
dynamics. Structural information alone or information from too low an
organizational level cannot sufficiently predict higher-order phenomena as
it does not sufficiently take into account interactions at higher
organizational levels and influences from outside the low-level
organizational unit. Cells have come up with compensation mechanisms to
maintain their structural and functional integrity in the face of
perturbations and uncertainty (Stelling J, 2004). Organisms are capable of
buffering genetic variation (Hartman JL 4th, 2001). Genetic buffering
mechanisms modify the genotype-phenotype relationship by concealing the
effects of genetic and environmental variation on phenotype (Rutherford SL.,
2000).

So if the structure of the genome alone cannot explain the differences
between species, disease processes and the dynamics of the cell, where does
our functional complexity and interspecies differences come from? How do we
continue in the post-genome era to study the dynamics of the cell and entire
organisms? How are genes related to the function of an organism and where do
we loose track? These questions are not of academic importance alone, but
their answers have a significant impact on the diagnosis and treatment of
(complex) diseases, drug discovery and development.

Let us take a walk from gene to protein and take a closer look at "The
Central Dogma of Molecular Biology", which I personally prefer to call an
axiom instead of a dogma. Science should only have axioms and leave dogmas
to religion.

Associating genes with diseases

In order to start studying the contribution of a certain gene to a disease
we must first find the gene(s) which might play a role in a given disease.
The strength of the association must be detectable by the method being
applied, which in complex gene-disease relationships has to find the
association on a background of significant functional and phenotypical
noise, such as in multifactorial diseases like diabetes (Doria A., 2000).
Variation in the phenotypical expression of many quantitative traits
(length, weight .) is due to the simultaneous segregation of multiple
quantitative trait loci (QTL) as well as environmental influences. Genetic
dissection of complex traits and quantitative trait loci is a complex
process (Darvasi A., 1998; Darvasi A, 2002).A mono-factorial approach is
likely to fail in a multifactorial process of pathogenesis (Templeton AR.,
1998).

Giving a gene its place in a disease process is not a trivial endeavour and
it is complicated by both technological and methodological difficulties.
Association studies offer a potentially powerful approach to identify
genetic variants that influence disease processes (Lohmueller KE, 2003;
Roeder K, 2005). The density of Single Nucleotide Polymorphisms (SNP) makes
them a popular target for studying gene-disease associations. However it is
not only the density alone which counts, but also the information content of
a given polymorphism (Bader JS. 2001; Ohashi J, 2001; Byng MC, 2003; Chapman
JM, 2003; Garner C, 2003).

False positive correlations of genetic markers with disease are reported due
to a flawed statistical analysis (Nurminen M., 1997; Edland SD, 2004;
Wacholder S, 2004). In microarray experiments defining the appropriate
sample size to find differentially expressed genesis is an important issue
(Wang SJ, 2004). In complex diseases in which not only multiple genes and
the dynamics of gene products play a role, associating particular genes with
a disease entity is even more difficult than in so-called monogenic diseases
(Carey G., 1994; Long AD, 1999). Proper subgroup analyses in a randomised
controlled trial (RCT) require careful design (Brookes ST, 2001).

Turning a gene-disease association into determining its role in the actual
causation of a disease process is even further away from finding and
establishing a positive correlation (Templeton AR., 1998).

>From genome sequence to gene activity

The genome sequence alone does not allow us to predict the functional impact
of sequence variations as epigenetic modulation influences functional gene
expression. Epigenetic modulation of gene function is a cause of
non-Mendelian inheritance patterns and variability in the expression and
penetrance of a disease. Even transmission of an identical gene sequence is
not a guarantee for identical gene expression as the (in)-activation of a
gene by epigenetic modulation occurs differently when a gene is of paternal
or maternal origin. Where (in what cells or tissues) and when (at what stage
of development or under what conditions) genes are expressed is a highly
dynamic process. The repression of gene activity and the maintenance of the
repressed state are fundamental requirements of cell differentiation,
ordered embryonic development and tissue integrity (Czermin B, 2003). These
spatial and temporal gene expression patterns can be assembled into
"localizome" maps (Dupuy D, 2004).

Epigenetic modulation of gene expression is heritable during cell division
but is not contained within the DNA sequence itself (Reik W, 2001; Bjornsson
HT, 2004; Kelly TL, 2004; Chong S, 2004). Epigenetic modulation is one of
the problems encountered when cloning, as the cloning process differs in its
epigenetic regulation of (embryonic) gene expression (Mann M, 2002).

This differential inactivation of genes from maternal and paternal origin
even leads to functional X-chromosome mosaicism in women as their cells at
random inactivate one of their X chromosomes. X-inactivation occurs early in
embryonic development and all cells subsequent inherit a different
functional X chromosome. The inactivated X chromosome can be seen in a
microscope as a Barr body in the interphase nuclei of female mammals.
Differential activation of genes creates a functional chimera.

Chemical modification by methylation of cytosine residues is a major
regulator of mammalian genome function and plays an important role in the
intra-uterine development of an organism and the regulation of gene
expression (Urnov FD, 2001). Tissue specific imprinting in genes leads to
differential gene expression in different tissues (Weinstein LS, 2001).
Aberrant DNA methylation has been implicated in the pathogenesis of a number
of diseases associated with aging, including cancer and cardiovascular and
neurological diseases (Walter J, 2003; Jiang YH, 2004; Macaluso M, 2004). A
dietary component such as folic acid is a key component of DNA methylation
during in utero development, disease development and aging (McKay JA, 2004).
Genes and environment interact and this might play a critical role in the
pathogenesis and inheritance of complex diseases (Vercelli D, 2004).

Transcriptional regulation in eukaryotes involves structurally and
functionally distinct nuclear RNA polymerases, corresponding general
initiation factors, gene-specific (DNA-binding) regulatory factors, and a
variety of coregulatory factors that act either through chromatin
modifications or more directly to facilitate formation and function of the
preinitiation complex (Roeder RG., 2005).

The gene expression flow from mRNA to tRNA is not a smooth unregulated
process in itself. Cells use RNA-induced silencing complexes (RISCs)
programmed with small interfering RNA (siRNA) to knock down target RNA
levels (Wassenegger M, 1994; Robb GB, 2005). RNAi is used by Eukaryotes for
sequence-specific, post-transcriptional gene silencing (Cullen BR., 2004;
Scherr M, 2003). RNA silencing genes play a role in DNA methylation (Chan
SW, 2004). This mechanism adds another feedback loop onto the multiple
layers of gene expression regulating mechanisms.

The correlation of even a gene sequence to the first steps in its expression
does not show a one-on one relation to the gene sequence itself. Modulators
and regulators of transcription and translation are showing a highly dynamic
process regulation mechanism. Cells use several mechanisms to create
functional flexibility from (relative) structural (genome sequence)
rigidity. The genome is a repository of our genetic potential, but only a
part of it is active at different spatial and temporal locations during our
lifetime. It is not only important to know what we can do within the
limitations of our genomic boundaries, but also how we deal with this
potential in spatial and temporal patterns during our lives. We do not
deploy the full potential of our genome at every moment of our life and in
all our cells in the same way. Although all our cells share the same genome,
they are highly diverse in their structure and function, not only are they
spatially differentiated but also temporally. The relation of gene structure
to its function is a bidirectional process of which our understanding of the
impact of different modulators is still not sufficient to create highly
correlating disease models.

>From gene to protein, a bumpy road

A eukaryote, such as Homo sapiens, has no one-on-one relation to its genes.
The dynamics of gene expression is regulated by hypo-, iso- and epigenetic
operators. The gene may be the structural unit of inheritance, but the
protein domain is the functional unit of metabolism.

When we talk about protein structure, the primary structure refers to the
amino acid sequence in a protein (1D). The primary structure is most closely
related to mRNA and as such the gene sequence and gene structure from which
the protein originates. The terms secondary and tertiary structure refer to
the 3D conformation of a protein chain. Secondary structure refers to the
interactions of the backbone chain (alpha helical, beta ***, etc.).
Tertiary structure refers to interactions of the side chains. Quaternary
structure refers to the interaction between separate chains in a multi-chain
protein (4D). The combined shape of the secondary and tertiary structure and
the quaternary structure is referred to as the conformation of the protein.
With increasing dimensionality, the relation between a higher order
organization of protein structure and its gene relaxes as other physical and
chemical influences play an increasingly important role in its physical and
functional integrity.

In a mature enzyme, only a relatively small number of its amino-acids
interact with a ligand, the majority of amino-acids help to create the
appropriate 3D and even 4D structures required for its in-vivo
functionality. Structural proteins and enzymes may show interactions over
larger parts of their molecular surface to form functional homo- or
hetero-polymers in their quaternary structure. From a single gene to a
protein, we have to deal with the dynamics of gene expression regulation and
mRNA formation (promoters, cis- and trans-regulation, transcription,
splicing). We have to deal with the interaction of tRNA with mRNA in the
translation of an mRNA sequence into a protein sequence and post-processing
of the protein sequence into a functional 3D and 4D structure (Wobble,
sequence processing, protein folding and interaction).

A structural similarity at the genome level does not lead to functional
similarity, due to epigenetic regulation (Eckhardt F., 2004). Sequence
variation, due to mutations does not bleed through to the protein level
one-on one. Basic mechanisms act as powerful uncouplers of gene structure
from protein function. Mutations in the DNA and errors during transcription
of the DNA-sequence into mRNA are not linear predictive for the structure
and function of the protein resulting from the translation of the
DNA-sequence into the protein-sequence, due to the degeneration of the
genetic code. The deleterious effects of sequence variations are up to a
certain extent suppressed by the Wobble-mechanism used in base-pairing in
translating mRNA to protein (Crick F, 1966).

Protein sequence = k x gene sequence

In this formula, 'k' is always smaller than one for most amino acids built
into a protein, due to mechanisms such as splicing variation, Wobble
mechanism.

In eukaryotes, a relatively simple genome compared to their functional and
structural complexity can be used, because of the existence of introns and
exons. An exon in general defines a functional domain and these domains are
rearranged to create a more complex proteome than the genome it is derived
from. Constitutive and alternative splicing of genes is dynamically
regulated at the moment of transcription and pre-mRNA splicing by cis- and
trans-acting factors (Kornblihtt AR, 2004; Sharp PA, 1988). Before the
completion of the Human Genome Project was finished it was expected that man
would need about 100,000 genes to explain the structural and functional
complexity of our species. This number has collapsed to about 25,000 genes
and is about four times (75 percent) lower than expected (Collins FS, 2004).
The functional differences between species are more related to differential
processing, due to different up- and down regulation of genes in different
cell types and organs.

The use of different promoters and splicing variants is used to tune protein
and enzyme structure and function in different cell locations and organs
(Ayoubi TA, 1996, Masure S, 1999; Nogues G, 2003, Yeo G, 2004). Promoter
variation and differential splicing allows for spatiotemporal
differentiation in protein expression, while the organism does not have to
manage an explosion in genomic size and sequence-complexity. This mechanism
helps to uncouple the protein from the rigidity of the gene sequence in
order to allow for functional variation while restricting structural
variation at the genome level (Nadal-Ginard B, 1991). Functional
differentiation in gene expression allows for a better adaptability to
changing conditions, without the need for fast-paced changes in gene
structure.

Protein folding of a linear amino-acid sequence into a 3D protein also acts
as a functional uncoupler of gene sequence to protein function. Changes in
the physical and chemical environment of the protein may change the shape
and alter the conformation of a protein. By putting a protein in a different
physical and chemical environment which will change the ability of the van
der Waals, hydrogen, ionic and covalent bonds which hold the protein
together in its particular conformation, it is possible to cause the
molecule to unfold by breaking those bonds and make it change or even lose
its function (denaturation). 3D and 4D protein folding is a complex process.
Even today the protein folding problem remains one of the most basic
unsolved problems in computational biology. Predicting protein folding from
the gene upwards ignores the influence of the post-translational
modification (PTM) and the influence of the in-vivo physico-chemical
environment of the protein. Proteoglycans and glycoproteins are not derived
from a gene sequence as such, but their structure is the result of extensive
post-translational modification. Cell membranes contain phospholipids, which
are not encoded by DNA as such, but they result from metabolic processing
and nutritional components.

While the protein-sequence at the moment of translation is related to the
gene-sequence, the final structure and function of an enzyme is in addition
defined by post-translational modification (PTM) and its physico-chemical
environment (Kukuruzinska MA, 1998; Uversky VN, 2003; Schramm A, 2003;
Seddon AM, 2004). Studying protein folding is a computational complex
process and still the focus of intensive research (Murzin A. G., 1995;
Orengo, C.A., 1997; Dietmann S, 2001; Day R, 2003; Harrison A, 2003; Pearl
F, 2005). Epicellular regulation of protein glycosylation also plays an
important role in the dynamics of protein activity (Medvedova L, 2004).

The majority of proteins are subjected to a multitude of post-translational
modifications. Post-translational modification involves cleaving, attaching
chemical groups (prosthetic groups), internal cross-linking (disulfide
bonds). Already more than hundred different types of PTM are known, which
act as functional uncouplers of protein structure from the gene sequence
(Hoogland C, 2004). A protein precursor may be differently processed in
different cell types and, in addition, diseased cells may process a given
precursor abnormally (Dockray GJ., 1987; Poly WJ., 1997; Rehfeld JF., 1990;
Rehfeld JF, 2003). Post-translational protein modifications finely tune the
cellular functions of each protein and play an important role in cellular
signaling, growth and transformation (Parekh RB, 1997; Seo J, 2004).

In a functional protein only a very few specific residues are actually
responsible for enzyme activity, while the fold is much more closely related
to ligand type (Martin AC, 1998). The effect of an amino-acid change on
protein structure and function depends on the location of the amino-acid in
the 3D structure, its physico-chemical properties and the physico-chemical
environment it is being processed and used. Amino-acids which are distant
neighbours in the protein sequence can become close neighbours in the 3D
structure of the protein and as such a protein sequence variation is only a
weak determinant of the function of a mature protein.

Proteins do not operate in void, but they depend from other proteins and
molecules for their function. Proteins build complex cell signaling networks
(CSNs) in which the functional outcome cannot be predicted from each
individual protein alone (Berg EL, 2005; Eungdamrong NJ, 2004; Lengeler JW.,
2000).

By just going from DNA-sequence to 3D protein structure, the relation
between genome sequence and the functional status of a cell begins to fade.
By taking this relation even further from gene to organism, we lose
additional predictive power. How will be able to design models that will
allow us to predict the functional outcome of a disease, when we use a fuzzy
model to start with? Powerful uncouplers of the structural relation of even
a protein to the gene it is primarily derived from, do not allow us to draw
hard conclusions about impact on the functional status of an organism from
the gene and genome sequence.

>From proteome to cell

Eukaryotic cells are highly compartmentalized; proteins do not exist in the
cell as in a homogeneous fluid, but in different compartments of the cell,
each with a different physico-chemical environment. The 3D and 4D structure
of a protein and its functionality is highly dependent from the in-vivo
physico-chemical environment of the protein. Cellular structure and
metabolism is organized and differentiated in both space and time.

Studying proteins without taking into account their spatial and temporal
organization in a cell, ignores the complexity and dynamics of protein
expression and interaction in a cell. Studying proteins in-vivo reveals more
about their function and dynamics (Chen, X., 2002; Hesse J, 2002; Pimpl P,
2002; Viallet PM, 2003; Murphy R. F., 2004). Without information about the
relation between cellular structure and function, a lot of information is
lost. A 2D protein-profile may show the entire protein content of a cell,
but we lose all information about the intracellular spatial and temporal
distribution of these proteins.

Eukaryotic cells are highly spatially differentiated structures. Proteins
involved in trans-membrane trafficking, require a membrane to do their work
and cannot do their work outside this specific physico-chemical environment.
A protein has to reach the appropriate physico-chemical environment in the
cell in order to do its work properly (Graham TR., 2004). Studying a protein
outside its in-vivo physico-chemical context leads to a loss of correlation
with its in-vivo dynamics.

There are three main cellular compartments in a eukaryotic cell, the
nucleus, cytoplasm and the cell membrane. The nucleus itself is a highly
organized 3D structure with highly spatial and temporal differentiated DNA-
and RNA-processing machinery (Lamond AI, 2003; Politz, J., 2003; Pombo, A.,
2003; Iborra F, 2003; Spector DL., 2003; Cremer T, 2004). Both transcription
and splicing of the mRNA message are carried out in the nucleus (Sleeman
JE., 2004). The distribution of eu- and heterochromatin changes throughout
the cell cycle, chromosomes and spindles appear during cell division. The
dynamics of gene transcription is visible in the chromatin condensation
patterns in the nucleus (Craig JM., 2005; Lippman Z, 2004). The nuclear
envelope separates transcription and DNA replication in the nucleus from the
site of protein synthesis in the cytoplasm (Rodriguez MS, 2004).

The cytoplasm itself contains several organelles, smooth and rough
endoplasmatic reticulum (SER and RER), ribosomes, the Golgi apparatus,
mitochondria, lysozomes and the cell membrane. Each organelle deals with a
different set of processes necessary for cell development and maintenance.
The membranes of organelles are highly dynamic structures which undergo
profound changes during the life cycle of a cell (Ellenberg, J. 1997; Zaal,
K. J. M., 1999). The endoplasmic reticulum (ER) is a multifunctional
signalling organelle that controls a wide range of spatially and temporally
differentiated cellular processes (Berridge MJ., 2002).

The structural compartmentalisation of the intracellular environment allows
for a functional differentiation and provides a process flow management
mechanism. The membrane structure and the mitochondrial membrane potentials
(MMP) of mitochondria play an important role in their function. (Zhang H,
2001; Pham N.A, 2004). Microtubules play an important role in cellular
function and their organization and dynamics are being studied by microscopy
based techniques (De Mey J., 1981; De Brabander M., 1986; Geuens G, 1986; De
Brabander M, 1989; Geerts H., 1991; Olson KR, 1999).

The dynamics of intracellular ion-fluxes such as for calcium (Ca2+) is
organized in a highly dynamic and spatial and temporal complex pattern. Ions
are themselves not encoded by the genome, but play an important role in
cellular function. The intra- and extra-cellular dynamics of ions
(concentration, flux) interact with a spatial and temporally regulated
pattern for protein expression and differential protein activity. The
complexity of intracellular calcium-signaling extends beyond the mere
expression profiles of genes encoding the proteins involved in
calcium-dynamics (Berridge MJ., 1981; Bootman MD, 2002; Cancela JM, 2002;
Berridge MJ., 2003; Berridge MJ, 2003b). For their proper function and
survival cells have to manage Ca2+ concentration and flux in space, time and
amplitude (Bootman MD, 2001). Calcium is involved in the delicate process of
spatially and temporally organization of cellular communication (Berridge
MJ., 2004).

As an example of spatial compartmentalisation in the cell, hydrolytic
lysozomal enzymes require a specific physical and chemical environment to do
their work, which inside the cell only exists inside the lysozomes (De Duve
C, 1955). The boundary membrane of the lysozome keeps the hydrolytic enzymes
away from the rest of the cytoplasm and so controls what will be digested
(De Duve C., 1966).

The cell membrane separates the interior of the cell from its environment,
but is a highly dynamic structure (Kenworthy, A. K., 1998; Varma, R., 1998).
The appropriate spatial and temporal dynamics of the cell membrane are vital
for the survival of the cell. The cell membrane provides the physical
boundaries in which the cell can maintain a highly dynamic physical and
chemical environment. Cell-to-cell communication is dynamically managed at
the level of the cell membrane (Nohe A, 2004).

Proteins do their work in spatially different cellular environments and with
different spatial and temporal patterns. A protein can be mobile in one
cellular compartment and immobile in another (Ellenberg J., 1997).
Co-expressed proteins may in reality never interact with each other because
they do their work in separate cellular compartments. The substrates of
proteins may migrate through different cellular compartments in order to be
subjected to a highly dynamic interplay of enzymatic processes. Proteins
which do their work in the same cellular compartment may only be expressed
at different stages during the life cycle of a cell. Spatial and temporal
protein localization information can help us to find entries into eukaryotic
protein function (Kumar A, 2002).

An important temporal differentiation of cellular processes occurs during
the cell cycle. The different stages in the cell cycle each depend on the
spatial and temporal expression of multiple proteins. The passage of the
cell through the cell cycle is controlled by proteins in the cytoplasmic
compartment, such as different Cyclins, Cyclin-dependent kinases (Cdks) and
the Anaphase-Promoting Complex (APC). First there is the G1 phase (growth
and preparation of the chromosomes for replication). Secondly the cell
enters the S phase (synthesis of DNA and centrosomes) and finally the G2
phase which prepares the cell for the actual mitosis (M). The mitosis itself
consist of a spatial and temporal sequence of events, called the prophase
(mitotic spindle), prometaphase (kinetochore), metaphase (metaphase plate),
anaphase (breakdown of cohesins) and telophase where a nuclear envelope
reforms around each cluster of chromosomes and these return to their more
extended form.

However our understanding of the cell cycle is still far from complete. The
regulation of the cell cycle by G1 cell cycle regulatory genes is more
complex than we thought (Pagano M, 2004).

Cells also operate in a temporal pattern based on internal and external
clocks. Cellular events must be organized in the time dimension as well as
in the space dimension for many proteins to perform their cellular functions
effectively (Okamura H., 2004). Circadian molecular clocks regulate protein
dynamics in temporal paterns (Crosthwaite SK., 2004; Hardin PE., 2004; Harms
E, 2004; Hastings MH, 2004; Ikeda M, 2004; Rudic RD, 2004; Schwartz WJ,
2004; Shu Y, 2004; Takahashi JS., 2004). In mammals there exists a central
circadian pacemaker which resides in the hypothalamic suprachiasmatic
nucleus (SCN), but circadian oscillators also exist in peripheral tissues
(Yagita K, 2001).

We need to study and understand the intracellular in-vivo dynamics of
protein metabolism and its spatial and temporal organization in different
cell types. We need to study intracellular protein ecology, not just ex-vivo
protein interactions or building a protein catalogue of only scalar
dimensions. The spatial and temporal patterns of intracellular protein
dynamics are an important factor in health and disease.

The dynamics of cellular function

Taxonomy is the science of organism classification and refers to either a
hierarchical classification of things, or the principles underlying the
classification. Today the emphasis of biological research is on classifying
genes, proteins in large catalogues, instead of studying the spatial and
temporal dynamics of cellular processes in vivo. The global analysis of
cellular proteins or proteomics is now a key area of research which is
developing in the post-genome era (Chambers G, 2000; Ideker T., 2001;
Aitchison J.D, 2003). Proteins show functional grouping into modules which
can be grouped into elegant schemes (Hartwell, L.H., 1999; Segal, E., 2003).

In-vivo however the spatial and temporal distribution and interaction of
proteins with other proteins, substrates, etc., adds another layer of
complexity which is not taken into account by functional studies alone.
Expression studies, no matter how we group them, do not reveal the
intracellular spatial and temporal distribution of proteins and the
functional outcome of their metabolic activity (spatial and temporal
substrate trafficking) in various cellular compartments. Studying proteins
only from a functional point of view ignores the impact of their
intracellular spatial and temporal dynamics. Molecular taxonomy or systems
biology (genomics, proteomics) will not provide us with the functional
answers we need to know.

Systems biology studies biological systems systematically and extensively
and in the end tries to formulate mathematical models that describe the
structure of the system (Ideker T., 2001; Klapa MI, 2003; Rives A.W, 2003).
However the level of biological integration which is being studied, genes,
proteins, pathways is still too far away from pathological reality to allow
for the development of highly predictive and highly correlating disease
models in relation tot clinical reality. The end-point of present day
systems biology only takes into account infra-cellular dynamics and loses
track when iso- and epi-cellular phenomena interfere with the dynamics of
the model. Studying the physics and chemistry of protein interactions cannot
ignore the spatial and temporal dynamics of cellular processes. We study
nature "horizontally", e.g. the genome or proteome, while the flux in nature
goes "vertical", e.g. from genome to proteome. The focus of our systems
research is perpendicular to the flow of events in nature. The resultant
vector which signifies our understanding of nature is aligned with the way
we work, not with the true flow of events in nature.

The cell is at the crossroads of life itself, being the lowest order
functional unit operating in a functional complete way. It is the basic
object of nature. As such the cell is for life what the atom is for physics,
the smallest biological level of organization, operating as a functional
unit. Dysfunctional cells by whatever cause, either gene malfunction,
infection, nutritional or environmental problems will eventually cause the
entire organism to lose its functional integrity. The dynamics of cellular
systems allow for the adaptation of the cell to a wide variety of conditions
and challenges, a relatively uniform physical structure combined with a web
of interacting dynamic processes leads to the multitude of cells which we
see in living organisms. In a living organism there is no such thing as an
average cell type from a functional point of view. Cells are functionally
highly diverse in both spatial and temporal dimensions.

The stochastic variation of cellular processing at the molecular level is
another cause of functional uncoupling of the cytome from the genome and ads
to the variability in functional behavior between cells (McAdams H.H., 1999;
Raser J.M., 2004). Structural research alone underestimates the complexity
of dynamic processes as it does not capture sufficiently the dynamic
complexity of the cell. The dynamic interaction of processes in multiple
pathways is the centerpiece of cellular life, not the individual components
or even individual enzymatic reactions in the cell. There is no monotonic
sequence of causation from genome structure to cellular dynamics.

Cellular function can be compared to a symphony in which multiple
"instruments" contribute to a complex, but in a healthy state harmonic,
"sound".

Genes and the dynamics of disease processes

The challenges faced by the medical world today are no less today than the
ones we faced a century ago. The spectrum of diseases may have changed
through time, as degenerative diseases and cancer play an increasing role in
modern society. On the other side an old enemy is back on the rise, how much
we thought that infectious diseases were a thing of the past; they are back
and with a new and frightening face.

Our increase in the knowledge of the involvement of our genes and large
scale proteomics in disease processes has not lead to an increase in the
productivity of pharmaceutical research (Drews J., 2000; Huber, L.A., 2003;
Lansbury PT Jr., 2004). The gap between the gene and the functional outcome
of a disease is too wide to bridge it from one direction only (Workman P.,
2001). Much thought has gone into finding a way how the knowledge coming
out of genomics and proteomics could revolutionize drug discovery, such as
for drug target discovery (Lindsay MA., 2003). The target of a drug molecule
may be a protein, but the target of disease therapy is the entire cell and
by extension the cell population of an organism. Every drug and its target
may be part of a disease therapy, but the therapy is not restricted to the
drug and its target. Every target is part of a therapy, but not every
therapy is confined to a traditional drug target.

In the case of diseases where we have already found a genetic basis, this
does not always allow us to create a model for the disease process. To
discover the involvement of a gene in a disease process does not tell us
anything about its place and relative importance in the multiple and
multilevel elements involved in the causation of a disease, such as genes,
nutrition, infectious agents and the environment. To discover a causative
element is not the same as understanding and predicting its dynamic
involvement in a disease process. What we do know is that all causation has
to pass through cells, as they constitute the "quanta" of the organism
itself.

Many diseases of clinical importance have heterogeneous mechanisms which
lead to the disease and only in a subpopulation the diseases can be traced
back to a single gene. In most cases a multiplicity of mechanisms
contributes to the diseases process. Genetic information has a high
predictive value in only a minority of cases.

Non-coding sequences, inter-gene and epigenetic interactions have a
significant impact on the prediction of the age of occurrence, severity, and
long-term prognosis of diseases (El-Osta A., 2004, Perkins DO, 2004).

The importance of the dynamics of the cell and its involvement in
pathological processes and current therapeutic efforts also requires a
better understanding of its function and phenotype in its relation to
pathological processes in diseases, such as in cancer, Alzheimer disease and
infectious diseases, such as AIDS, tuberculosis (TBC), influenza (flu), etc.

Trying to predict a disease process from the genome (proteome) upwards, is
like trying to solve a higher order polynomial while omitting the majority
of elements and expecting that the equation will work:

e.g.: Disease process = a x x + b

Instead of using a higher order multi-dimensional model, closer to in-vivo
functional dynamics in which a matrix or web of causation and consequences
interacts in a high-dimensional space-time continuum:

e.g.: Disease process = a x un + b x vo + c x wp + d x yq + e x zr

In addition, each parameter which is being used in an equation is in itself
the result of an underlying or "overlying" dynamic process. Each layer of
organization can be fed into higher or lower order levels of organization as
there is always a cross-influence in both directions. It is a matter of
expanding or collapsing the set of parameters and taking into account or
ignoring underlying "modifying" influences. Reducing the complexity allows
for a better understanding of a simplified model, but has a decreased match
to the complexity and dynamics of biological reality. When we create a
model, we should not regard it as a one-on-one substitute for reality which
we capture only partially into our model.

Infectious diseases

Infectious diseases still pose a significant threat to the health and well
being of (modern) society. After years of relative neglect, nations are
increasingly aware of the present and future threats of infectious diseases
and are even setting up new agencies, such as the European Centre for
Disease Prevention and Control (ECDC) or expand the role of existing
organizations, such as the Centers for Disease Control and Prevention (CDC).
Beside their political and economical impact on society, how do we deal with
infectious diseases in science?

In infectious diseases the environment, in this case the infectious agents,
interacts in a complex way with the host defense system of which much
remains to be explored. We must be aware of the fact that the golden era of
antibiotics is already behind us as many infectious agents (e.g. TBC, MRSA
and other bacterial diseases) are showing an increasing resistance against
most classes of antibiotics which are available today (Davies J, 1994). We
have succeeded in less than a century to destroy our best weapons against
infectious diseases, due to misuse of antibiotics both by physicians and
their patients. Only the elderly remember the days when mortality due to
infections was a major cause of premature death, but the moment is
approaching when this nightmare will return. Emerging infectious diseases
(EIDs) and re-emerging infectious diseases challenge our defenses (Ranga S,
1997; Fauci AS., 2004; Morens DM, 2004).

Viral diseases (e.g. AIDS, influenza) are even harder to fight as they use t
he cellular machinery of the body itself to reproduce. We need to study the
pathological process in cells in more detail and in a different way, in
order to have a chance to succeed in the new therapeutic challenges ahead of
us. Viruses, under selective pressure of modern antiviral drugs are also
showing increasing resistance to treatment. We are running out of time in
our battle against infectious diseases and a systematic approach will only
give us the answers when it will be too late. We are not setting the agenda,
but the diseases are taking the lead.

Due to modern technology, the time to respond to a new infectious challenge
is being reduced. In modern times, diseases take planes too, which makes it
even harder to fight them by classical isolation or quarantine. Airplanes
may be safe to travel with, compared to other transport systems, but they
can cause secondary mortality by transporting pathogens over large distances
at a speed unknown to previous generations, which gives a new meaning to
airborne infections (Gerard E, 2002; Van Herck K, 2004; Blair JE, 2004).
Infectious diseases may initially go unnoticed in underdeveloped areas of
the world (e.g. Ebola virus Lassa fever, Marburg virus), but as soon as they
board a plane, it is modern technology which will give them free access to
the world (Clayton AJ, 1979; Gillen PB, 1999). A relatively long incubation
time combined with a high mortality rate will allow a disease to spread
widely and cause a pandemic, before we even can start a treatment program.
If an unknown disease causes such a pandemic, we may run out of time before
we can find a cure as we first have to develop a diagnostic tool. A recent
example which is a model of what can happen was the Severe Acute Respiratory
Syndrome or SARS (Peiris, J.S.M. 2003, Berger A, 2004; Heymann DL, 2004;
Tambyah PA, 2004).

Robert Koch presented his work on Tuberculosis on 24 March 1882 before the
members of the Berlin Physiological Society, which meant a breakthrough in
the understanding of this terrible disease (Winkle S, 1997, pp. 137-141).
Now after more than 100 years of research and drug development, TB is on the
rise again. In the war against infections such as Tuberculosis, there are no
easy wins. We may win a fight but for the majority of pathogens we can only
reach a status quo, but never completely win the war. Variability by
mutating is a powerful weapon against our drug treatments and pathogens use
it to their great advantage.

We must keep our defenses up to date and changing in order to outsmart our
bacterial and viral enemies. New antibiotics are not found within the human
genome. Penicillin was discovered by accident and many important antibiotics
were found at the most unlikely places (Fleming, A, 1929). No hypothesis or
model can be formulated to find the unexpected, but we have to find new
antibiotics as bacteria are closing in on us and some of our worst enemies
are even winning the race.

Scientists are waiting with fear for the next influenza pandemic which will
hit us some day (Gust ID, 2001; Capua I, 2004). Scientists are trying to
understand the lethal potential of the deadliest influenza epidemic of all
times, which occurred after the first World-War. Soon the virus which caused
the influenza pandemic, called the 'Spanish flu' will re-emerge out of the
test tubes of the laboratory. Recent outbreaks of avian flu have given us a
preview of what can happen and evidence is increasing that the possibilities
for spreading avian influenza A virus (H5 or H7 subtype) are worse than
previously was assumed (Koopmans M, 2004; Kuiken T, 2004).

New pathogens can have a devastating effect on a human population. Examples
of what can happen when a new infectious agent hits a population with little
or no immunological "experience" with a (re-)introduced pathogen, can be
found in the histories of indigenous people confronted with infectious
diseases introduced by European colonization as in Australia and Tasmania.
Within 100 years of European colonization the total population of full-blood
Aboriginal people in Tasmania became extinct. Introduced infectious diseases
killed many more Aborigines than did direct conflict. Infectious diseases
such as smallpox, measles, and influenza were major killers and even
chickenpox was deadly as the Aboriginals had no immunological history even
with chickenpox. Of the 90 percent of the Aboriginal population that died
out as a result of European contact, it is estimated that around 80 or 90
percent of the deaths were the result of disease.

Most people have no idea of the role smallpox played in the destruction of
an entire civilization after it was brought to America by the
conquistadores. About 50 to 90 percent of the Native American population
died of smallpox and the speed at which people died is beyond our
imagination (McMichael AJ, 2004; Winkle S., 1997, pp. 855-861). A mortality
of 50 percent for a new disease, for which we have no immunity, could kill
half of the population of a country or an entire continent. Western society
now has to fear the introduction of new pathogens from distant places and
when the disease has the right pathological profile; it will spread
extensively into the population before it is being diagnosed (e.g. AIDS).
Re-emerging infectious diseases are a global problem with a local impact. It
is an unpleasant thought that this time we will face the fate of the
indigenous people during European colonization. In modern times we not only
have to fear the accidental spreading of infectious diseases, but
bio-terrorism will challenge our defenses sooner or later (Broussard LA,
2001, Gottschalk R, 2004).

Finding the infectious agent for a new and unknown disease requires
something else than sequencing a genome as this approach only works when we
have the time to do the sequencing while the pathogen takes its course.
Analyzing the genome sequence of a new infectious agent can only start after
it has been isolated by more traditional means (Berger A, 2004). Once we
know the new pathogen, we can use its genome sequence to develop rapid
diagnostic tools, based on PCR, but in order to do this we must first
isolate it from the patient. Developing a therapy after this, takes much
longer and the genome sequence itself without additional functional
information is not enough. Only after Koch's postulates had been fulfilled,
the WHO officially declared on 16 April 2003 that a previously unknown
coronavirus was the cause of SARS.

Modifying the disease progression requires an interaction with the actual
disease process which extends beyond understanding the genome structure of
the pathogen. Focusing more on the dynamics of the interaction of cellular
systems with pathogens and using tools for functional research of the
disease process at the cellular level (and beyond) will hopefully allow us
to respond in time when we are faced with an unknown pathogen.

When we do not already have an antibiotic, antiviral drug or vaccine at hand
at the moment a new disease hits us, either by accident or on purpose in
biological warfare or bioterrorism, we are in serious (and lethal) trouble.
In this case the only thing left is the medieval solution of quarantaining
the infected people, which only works if we are able to contain them before
they spread over a country or even the planet (e.g. Ebola, SARS or HIV).

Although all cells in the human body may share the same genome, there is a
high spatial and temporal differentiation in gene expression and metabolic
dynamics in different cell types and organs. In HIV, it is the CD4
lymphocytes which express the receptors by which the virus can enter the
cell (Fauci AS, 1996). A hepatocyte may share its entire genome with a CD4
lymphocyte, but it does not express the proteins encoded by the gene which
allows the virus to enter the cell. The progress of a HIV infection is also
a highly dynamic process of interaction between the host and the virus (Wei,
X., 1995). The observation of differences in disease progress leads to the
discovery of a genetic restriction of HIV-1 infection and progression to
AIDS by a deletion allele of the CCR5 structural gene (Dean M, 1996). The
emerging picture on infectious diseases is one of highly polygenic patterns,
with occasional major genes, along with significant inter-population
heterogeneity (Frodsham AJ, 2004). The complex interactions and regulation
of the Interleukin-1 (IL-1) family of proteins is just one of the issues in
elucidating the dynamics of the human immune system (Laurincova B., 2000).

Clinical observations lead to genetic conclusions, but the way back to
clinical treatment of diseases is a long and winding road for which the gene
sequence or protein structure does not provide us with all the necessary
information about the dynamics of the disease process. Studying the cellular
dynamics of disease processes provides us with one of the step stones from
gene to clinic. By focusing on genomics and proteomics alone, there remains
a correlation and predictive deficit in our disease models.

Mendelian diseases

Mendelian inherited and monogenic diseases have always been at the center of
attention in the relation of genetic variation to diseases. Monogenic
diseases served as a model to prove the use of genetic information to the
development of a disease and the outcome of a disease process.
Phenotype-genotype relationships are complex even in the case of many
monogenic diseases. Increasingly complex interactions have now been
demonstrated in a number of monogenic Mendelian diseases (Nabholz CE, 2004).
The (phenotypical and functional) expression and development of even a
monogenic disease depends on its context, which comprises both other genes
and environmental factors. These inter-gene and epigenetic interactions have
a significant impact on the prediction of the age of occurrence, severity,
and long-term prognosis of even 'genetic' diseases (Cajiao I, 2004; Hull J,
1998; Frank RE, 2004; Salvatore F, 2002; Sontag MK, 2004; Sangiuolo F,
2004).

The beta-thalassemias show a remarkable phenotypic diversity caused by the
action of many secondary and tertiary modifiers, and a wide range of
environmental factors (Weatherall DJ., 2001). Sickle cell anaemia and cystic
fibrosis can serve as an example that genotype at a single locus rarely
completely predicts phenotype (Summers KM., 1996). Although the gene defect
in Huntington's disease is known for years, the contribution of the gene
defect to the functional out come of the disease is not yet known
(Georgiou-Karistianis N, 2003). Cell based research will help to elucidate
the disease mechanism in Huntington's disease (Arrasate M, 2004).

In cystic fibrosis, the severity of the disease cannot be linked one-on-one
to genetic variation in CFTR (Grody W, 2003). Cystic fibrosis is the most
common autosomal recessive disorder in Caucasians, with a frequency of
approximately 1 in 3000 live births, so finding a cure for this disease has
a high impact on our society. Success stories with rare diseases may sound
impressive from a scientific point of view, but there is no escape from the
economic reality of the size of the patient population. So let us take a
closer look at cystic fibrosis as it is a disease of which the gene held
responsible for the disease was identified about 14 years ago (Rommens JM,
1989; Collins FS., 1990). The method (reverse genetics) used to identify
the gene, did not require an understanding of the gene function at that
moment or any understanding of the impact of genetic heterogeneity on the
phenotypical expression of the disease (Iannuzzi MC, 1990; Audrezet MP,
2004). By starting form the gene for a single genetic disease such as cystic
fibrosis, where did we get after 14 years of hard labour?

A once 'monogenic' disease such as cystic fibrosis shows remarkable
phenotypic variation and clinical variation (Decaestecker K, 2004). By now
about 1000 gene mutations of the cystic fibrosis transmembrane conductance
regulator gene (CFTR) have been identified, which leads to a highly variable
phenotypic and clinical presentation of the disease. (McKone EF, 2003).
Mutations in the CFTR gene have been classified into 5 functional categories
(Welsh MJ, 1993). A list of 1000 mutations is reduced to 5 functional
classes at the protein level, which leads to a ratio of 0.5 percent for each
mutation to lead to a distinct CFTR chloride channel dysfunction. Due to the
functional uncoupling of gene structure to protein function in cystic
fibrosis, genetic sequence variation has a low impact on functional
variation on the protein level (1000 to 5). More important than gene
sequence variation is the spatial location of a mutation in the 3D structure
of a protein. (Rich DP, 1993). Even more important is the cellular and organ
location of a functional defect as in Cystic Fibrosis mainly the
pathological process (Pseudomonas aeruginosa infection) in the lungs are a
major cause of morbidity and mortality (Elkin S, 2003).

Other genes act as modulators of the disease outcome, even in a disease such
as cystic fibrosis, once regarded as a monogenic disease (Hull J, 1998,
Frank RE, 2004; Salvatore F, 2002; Sontag MK, 2004; Sangiuolo F., 2004). We
even need to take into account epigenetic information and environmental
influences on disease outcome, even in a so called monogenic disease as
cystic fibrosis.

Human populations show considerable genetic heterogeneity (allelic
variation) and even geographic variation, which leads to difficulties in
using gene sequence based diagnostic tools (Liu W, 2004; Raskin S, 2003).
So, the sequence of one individual's genome allows studying one person's
genetic profile, but does not lead to a population-wide prediction of
genetic profiles. Genetic heterogeneity uncouples clinical outcome from
model gene sequences (Imahara SD, 2004). This problem is not solved by
simply adding more sequence information without a functional understanding
of the meaning of sequence variation on phenotypic expression and disease
outcome in the patient. Structural information without functional
understanding leads to predictive deficits. The functional understanding of
a disease process must be at the level of the patient and his cells and not
at a lower order organizational level, such as the genome or proteome alone.

Genetic heterogeneity leads to a reduced sensitivity and an increase in
false negative results if a genetic test is not adapted to this genetic
heterogeneity. A mutational test leads to a simpler almost 'binary' readout,
instead of the more 'analog' interpretation of a continuum of values in a
functional test, but this comes at a price. A test which detects a disease
marker at a higher organizational level can detect a disease more easily and
will lead to less false negatives in this case.

The complexity of even monogenic diseases and the web of functional
interactions between at the genome level, protein interactions and
environmental influences on the disease outcome will dilute the predictive
power of structural sequence information and the DNA-level. Using
low-dimensional intracellular data to predict iso- and epicellular phenomena
has a low predictive power to be used in clinical situations as such.

No pharmaceutical company would like the idea that it requires 14 years of
preclinical research to reach an IND after a new drug target was identified
as in cystic fibrosis. Even if only 1000 genes out of our 25,000 were
involved in human diseases and would require the same amount of work, it
would take us the equivalent of 14,000 years of work on the scale as was
needed to achieve the same results as for the cystic fibrosis gene. But up
to this moment no causal (gene) therapy came out of the identification of
the CFTR gene, but an improvement of prenatal diagnostics (Klink D, 2004).

Pseudomonas aeruginosa lung infection is the major cause of morbidity and
mortality in patients with cystic fibrosis (Elkin S, 2003). Over the past
decades we have seen an improvement of symptomatic therapy, but still no
causal therapy, leaving aside a lung transplant.

How are we going to develop drugs which have a large enough patient
population to pay for the costs of drug discovery and development if we need
to target individual mutant protein molecules? If it can be so difficult to
go from a single gene to develop a therapy based on genetic information, how
do we expect to proceed for the entire genome and proteome?


Degenerative diseases and cancer

The increasing longevity of western population is increasingly straining
public healthcare systems, due to an increase in incidence of degenerative
diseases and cancer. A diminishing active population has to support the
growing financial demands of a healthcare system. Improving the health and
self-reliance of the growing number of elderly people by efficient
treatments of degenerative diseases and cancer is an important political
issue. Where are we and where are we going to in science to solve these
fundamental problems of modern society?

Unraveling the pathological mechanism of a complex disease is a major
scientific challenge and still beyond reach of present day science in many
cases. For degenerative diseases, such as Alzheimer disease , cancer, birth
defects, cardiovascular diseases, Parkinson's disease, diabetes, and nerve
degeneration it is the dynamics of the cellular machinery itself which
fails. Sharing one genome does not lead to sharing the same pathology, as
cellular differentiation leads to a highly diverse spatial and temporal
cellular function and morphology. Differential and heterogeneous
degeneration patterns of different cell types are the consequence of a
highly differentiated spatial and temporal expression pattern of proteins in
different cell types and different sub-cellular compartments.

Unravelling part of the genetics of a disease does not yet bring therapeutic
success. Multiple genes and (multiple) environmental factors contribute to
the disease process and its clinical outcome in complex diseases (Liebman
MN, 2002). In Crohn's disease the gene defect found does not explain the
severity of the disease (Peltekova VD, 2004). In breast cancer genetic
variants of BRCA1 and BRCA2 do not have a consistent level of penetration
and as such their presence alone does not explain the disease process (Ford
D et al, 1998; Hartge, 2003). Although there is evidence for the involvement
of the gene for PPAR-gamma in type 2 diabetes is, the mechanism by which it
contributes to the disease process of diabetes is not clear and could not be
deduced from genetic information alone (Barroso I, 1999).

In APC (Adenomatous Polyposis Coli) and HNPCC (Hereditary Non-Polyposis
Colorectal Cancer) a genetic origin, only accounts for about 5 percent of
all cases of colorectal cancer (Kinzler, 1996). Genes which are involved in
diabetes, such as GCK (glukokinase) , HNF1A and HNF4A (Hepatic Nuclear
Factor) are linked to less than 5 percent of cases of diabetes (Edlund,
1998, Fajans, 2001).

On of the major emerging health problems of modern society is Alzheimer's
disease (AD). This is not only because widely known people, such as the
former president of the USA, Ronald Reagan, suffered from the disease in a
long and unpleasant disease process. Today AD is still a chronic disease
without a cure which causes patients to receive long-term care (Souder E,
2004).

Presently available drugs improve symptoms, but do not have a profound
disease-modifying effect and fail to alter the course of AD, so it may be
time to change the way we think about AD therapeutics (Crentsil V., 2004;
Citron M., 2004; Kostrzewa RM, 2004)? Will we see a breakthrough in the
understanding of the cellular and molecular alterations that are responsible
for the degeneration of neurons in AD patients (Mattson MP., 2004)?

In Alzheimer's disease (AD), only a minority of cases can be linked to a
single hereditary gene mutation, the complexity of the disease process
extends beyond our present understanding and disease models (Selkoe DJ.,
2001; Eikelenboom P, 2004). Neurodegeneration in AD may be caused by
deposition of amyloid beta-peptide in plaques in brain tissue (Amyloid
Hypothesis), but no causal treatment has come out of this in 10 years of
hard work (Hardy J, 2002; Lee HG, 2004; Lee HG, 2004b). Little is understood
about the dynamics of amyloid beta-peptide and its fundamental role in the
disease process of AD (Regland B, 1992; Koo EH., 2002; LeVine H 3rd., 2004).

A complex disease requires studying and understanding a complex in-vivo
pattern of a spatially and temporally changing metabolic process, which goes
beyond studying gene expression profiles, either single or multiplexed.
Studying the multi-scale spatial and temporal dynamics of a complex disease
process in a long-term space-time continuum is a tremendous scientific
challenge. Instead of focusing on individual (molecular) targets in drug
research and therapy, complex diseases may require pathway-engineering or
cell replacement to restore the appropriate dynamics of spatial and temporal
patterns of intracellular molecular processes. Functional or structural
protein (re-) modeling or restoration in-vivo may be a better approach for
complex diseases than just docking a small molecule to an active binding
site?

At this moment the cell is the target for many therapeutic efforts to come
to a causal therapy of complex diseases, which we can now only treat with
external substitution, such as diabetes. Many diseases are far more complex
and multi-factorial than monogenic diseases and should be studied with more
power at a higher biological level than the genome or proteome to capture
the complexity of the disease process.

One of the most promising domains of research today is stem cell research
(He Q, 2003; Doss MX, 2004). Since the isolation and growth in culture of
proliferative cells derived from mouse embryos in 1981, stem cell research
has come a long way (Evans MJ, 1981; Martin GR., 1981). Instead of treating
complex disease processes with a multitude of drugs, each with its own
spectrum of sometimes serious and cumulative side effects, failing
components of the human cytome could be engineered or replaced by stem cells
(adult or embryonic) differentiated into the appropriate cell type.

When the distortion of cellular metabolism goes beyond a mere dysfunction of
a single protein, a complete replacement of the dysfunctional cells has a
better change to restore the complex and delicate balance and regulation of
metabolic processing. The fine dynamics of spatial and temporal regulation
of cellular metabolism and its response to changing demands of an organism
in complex diseases are best met by replacing the failing part of the cytome
with a well balanced cellular substitute. Those parts of cellular processes
which are beyond the reach of (present-day) drug therapy or which are
insufficiently treated by non-cellular means have the prospect of being
restored to a physiologically appropriate level. With stem cell therapy we
would be able to replace a non-functional part of the human cytome with a
set of functioning and dynamically regulated cellular units.

Several diseases which currently cannot be treated or cured completely are
the target of intensive research. In diabetes long term insulin replacement
therapy does not prevent a multitude of chronic and severe side effects,
such as circulatory abnormalities, retinopathy, nephropathy, neuropathy and
foot ulcers. In juvenile diabetes however there is an immunological
component which complicates treatment. The prospect to find a cure for
diabetes which would restore the dynamics of insulin production is an
important scientific and social challenge (Heit JJ, 2004).

There is hope for the development of stem cell therapies in human
neurodegenerative disorders (Kim SU., 2004; Lazic SE, 2004; Lindvall O,
2004). Much research goes into finding a cure for degenerative diseases such
as Parkinson's disease (Drucker-Colin R, 2004; Hermann A, 2004; Roitberg B,
2004). Scientists are investigating the possibility to treat a failing heart
with cellular cardiomyoplasty (Wold LE, 2004)

When we want to use stem cells for disease therapy we have to deal with the
functional and structural characteristics of cells which are being used
(Baksh D, 2004). The differentiation of stem cells of either adult or
embryonic origin, into mature and functional cells is a complex and
dynamically regulated process. Understanding the differentiation pathways of
embryonic and adult stem cells and their spatio-temporal dynamics of
differentiation and structural organization will require intensive research
(Raff M., 2003). When using stem cells from an individual which suffers from
a degenerative disease, the disease may not be cured when the same deficient
pathway is activated in the differentiating stem cell. The molecular process
may need to be corrected first in this case, for instance by gene therapy or
by using exogenous stem cells.

Gene therapy also holds many promises for the therapy of life threatening
diseases, but in order to improve gene therapy we will need a better
understanding on what goes on inside the cell and what the consequences are
on the cellular metabolism when we modify its function by inserting genes.
At this moment monogenic diseases are the target for gene therapy, but in
the future entire parts of pathways may need reconstruction. The gene is the
means to achieve the ultimate goal to change the cellular metabolism to cure
a disease.

The scientific challenges posed by complex diseases, such as many
degenerative and chronic diseases and cancer will keep scientists busy, far
beyond the current scope of present day science.

Drug discovery and development

How to explore and find new directions for research

Conclusion

The future development of this idea will decide if a Human Cytome Project
(HCP) will become reality. The road from gene to phenotype is not a simple
path, but a multidimensional space built from an extensive web of
interacting processes. I can only provide ideas and explain why it would
benefit society and science to explore the cytome in a more organized and
systematic way as is currently being done. The cellular level of biological
organization deserves more in-depth exploration and quantitative analysis to
improve our understanding of important human disease processes in order to
allow us to deal with the scientific and medical challenges we are facing
today and will be facing in the future.

History of article

Original HCP message, 1 December 2003

Update and first article on website 30 Jan. 2004

Posting of HCP article version 24 Sept. 2004

Posting of HCP article version 12 Oct. 2004

Posting of HCP article version 19 Oct. 2004

Posting of HCP article version 25 Oct. 2004

Posting of HCP article version 10 Nov. 2004

Posting of HCP article version 22 Nov. 2004

Posting of HCP article version 6 Jan. 2005

Meetings

Focus On Microscopy, Philadelphia, USA - 2004

ISAC XXII, Montpellier, France - 2004

European Microscopy Congress, Antwerp, Belgium - 2004

EWGCCA, Mol, Belgium - 2004

10th Leipziger Workshop, Leipzig, Germany - 2005

ISAC XXIII, Quebec, Canada - 2006

Links

Biomedical Structural Research
Towards a Human Cytome Project
Draft: Human Cytome Project
Cytomics
E-Cell Project
Physiome Project
IUPS Physiome Project
GIOME Project
Functional genomics
Cyttron
National Resource for Cell Analysis and Modeling - NRCAM
Prediction in Cell-based Systems (Predictive Cytomics)
Human Genome Project
Post-Human Genome Project Progress & Resources
How Many Genes Are in the Human Genome?
NCBI
Acknowledgments

I am indebted, for their pioneering work in automated digital microscopy, to
Frans Cornelissen, Hugo Geerts, Jan-Mark Geusebroek, Roger Nuyens, Rony
Nuydens, Luk Ver Donck and their colleagues. Many thanks also to the
pioneers of Nanovid microscopy, Marc De Brabander, Jan De Mey, Hugo Geerts,
Marc Moeremans, Rony Nuydens and their colleagues.

References

References can be found here

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Email: pvosta at cs dot com

The author of this webpage is Peter Van Osta, MD.

A first draft was published on Monday, 1 December 2003 in the
bionet.cellbiol newsgroup. I plan to post regular updates of this text to
the bionet.cellbiol newsgroup.

Latest revision on 11 October 2005


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