| Peter Van Osta 2004-10-25, 4:08 am |
| Hi,
As my 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.
Original version:
http://ourworld.compuserve.com/home...osta/humcyt.htm
A Human Cytome Project - an idea
Introduction
Although the completion of the Human Genome Project holds many promises
for the understanding of the genetics of man and the involvement of genes
in human diseases, the use of this information has to be viewed from
another perspective as is currently being done. Predicting the dynamics of
the cell and its fate in diseases from the genome information upwards is
likely to fail due to the complexity of metabolic processing and
environmental influences on the cellular metabolism.
The functional correlation between genome structure and 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 organization
of cells, organs and organisms. Pathological processes have to be viewed
from another organizational level in order to capture the complexity of
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 and disease
treatment.
On Monday 1 December 2003 I posted a message about the idea of a Human
Cytome Project 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 Human Cytome Project is already
being discussed at scientific conferences such as Focus on Microscopy and
the European Microscopy Congress and already articles start to appear on
the idea (Valet G, 2004). 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. 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.
The path which lead to the idea of a Human Cytome Project
I will give a bit more background to the path which for me has lead to the
idea that something of a Human Cytome Project might be feasible. The idea
for large scale screening of the dynamics of the (living) cell came when I
visited the Sanger Center in the UK in 2001 and was shown a big room
filled with DNA-sequencers. From then on I wanted to create a system which
could mean for cell-based research (cytomics) what DNA-sequencing had
meant for Human Genome research. However I did not want to create a
catalog of the cytome, but to allow for the functional exploration of the
cell in order to capture and describe the dynamics of cellular processes
and not only create a catalog of its components. The multidimensional
world of the cell requires a higher-dimensional approach than the linear
world of DNA and also a different inner- and outer resolution is needed
for each level of biological integration. Today powerful techniques to
explore the cytome are available, such as flow cytometry (Edwards B.S.,
2004) and advanced digital microscopy (Price J. H., 2003) which enables
the exploration of the cellular function and phenotype. There are now
exciting technological developments going on in what is called High
Content Screening which will allow us to explore cellular systems on a
large scale (Taylor DL, 2001; Giuliano KA, 2003). These developments and
other technological advances made me feel confident that the exploration
of the human cytome would be feasible.
I myself wanted to know if a system to explore the cytome on a very large
scale could be implemented and would work. The nice thing of working in an
industrial environment is that you can create what you dream of and see it
in operation in a real-life situation. As technologies evolve, it should
be easy to exchange components of a system or expand it with new
technologies. The system should therefore be modular and scalable, the
core of the system should be of a different design than the interface to
the outside world and they should evolve separately, only linked to each
other for the exchange of information. The core has to be able to deal
with multidimensional spaces and datasets and manage the dataflow between
modules, each module dealing with a part of the entire process, from
acquisition to data generation. The design should allow for distributed
operation, so a system could run on different platforms and interact with
components over a network. It should use open standards for its
communication with the outside world to allow for easy integration in a
heterogeneous environment. The output of the system should be a set of
linked feature hyperspaces, each describing structural and functional
aspects of the individual cell and its components.
Since 2001 I have been thinking about, and working on, the design of a
scalable system, of which the M5 framework is now operational and it
allows me to study its practical use in more detail.
The predecessor of this system dates back to the early nineties of the
twentieth century (Cornelissen F., 1993; Geerts, H., 1992; Van Osta P.
2002). This use of digital microscopy originated from Nanovid microscopy
long ago (De Mey J., 1981; De Brabander M., 1986; Geuens G, 1986; Geerts
H., 1991) and from monitoring the Calcium flux in individual
cardiomyocytes (Cornelissen F., 1993). Drug screening by using cellular
models in automated systems was done in this environment for many years,
before it became fashionable in the outside world. Why a Human Cytome
Project?
Human Genome Project
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. 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; 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 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. 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.
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?
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. In a mature
protein, only a relatively small number of its amino-acids interact with a
ligand, the majority of amino-acids help to create the appropriate 3D
structure required for its functionality.
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 structure (Wobble, sequence processing, protein folding).
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 suppress by the Wobble-mechanism used in base-pairing in
translating mRNA to protein (Crick F, 1966).
Protein sequence = a x gene sequence
In this formula, a, is always smaller than one for most amino acids built
into a protein, due to mechanisms such as splicing variation and the
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). 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 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 strcture and function
in different cell locations and organs (Ayoubi TA, 1996, 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.
Protein folding of a linear amino-acid sequence into a 3D protein also
acts as a functional uncoupler of gene sequence to protein function. 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 its post-translational processing and its
physico-chemical environment. 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 protein function.
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.
There are two main cellular compartments in a eukaryotic cell, the nucleus
and the cytoplasm. The nucleus shows a distinct spatio-temporal
organization. The distribution of eu- and heterochromatin changes
throughout the cell cycle, spindles appear during cell division.
The cytoplasm itself contains several organels, smooth and rough
endoplasmatic reticulum (SER and RER), ribosomes, the Golgi apparatus,
mitochondria and the cell membrane.
Proteins do their work in these compartments and two proteins may in
reality never interact with each other because they do their work in
separate cellular compartments or at different stages during the life
cycle of a cell.
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. 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.
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.
The dynamics of cellular function
The cell is at the crossroads of life itself, being the lowest order
functional unit operating in a functional complete way. 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. The stochastic variation of cellular processing at the
molecular level is a 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.
The dynamics of disease processes in cells
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).
Genes and diseases
In the case of diseases where we have already found a genetic basis, this
does not always allow us to find an explanation for the disease process.
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 cancer, Alzheimer disease,
infectious diseases, such as AIDS, tuberculosis (TBC), influenza (flu),
etc.
Infectious diseases
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).
Viral diseases (e.g. AIDS, influenza) are even harder to fight as they use
the 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). Scientists are waiting with fear for the next influenza
pandemic (Gust ID, 2001; Capua I, 2004). 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).
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. Almost 50 percent of the indigenous population died of
smallpox and the speed at which people died is beyond our current
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,
would kill half of the population. 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. 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. Focusing on cellular systems
and using tools for discovery at this level will allow us to respond in
time when we are faced with an unknown pathogen.
Although all cells in the human body may share the same genome, there is a
high spatial and temporal differentiation in gene expression in different
cell type 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 lead 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). 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 does not provide the necessary information.
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). 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). In cystic fibrosis, the severity of
the disease cannot be linked one–on-one to genetic variation in CFTR
(Grody W et al, 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 (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 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.
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
For degenerative diseases, such as Alzheimer disease and cancer, birth
defects, cardiovascular diseases, and nerve degeneration it is the
cellular machinery itself which fails. One of the most promising domains
of research today is stem cell research, which has to deal with the
functional and structural characteristics of cells which are being
studied. Gene therapy 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 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. At this moment the cell is the target for
many therapeutic efforts to come to a causal therapy of diseases, which we
can now only treat with external substitution, such as diabetes. These
diseases are far more complex and multi-factorial than monogenic diseases
and should be studied from a different perspective to capture the
complexity of the disease process.
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 (Barroso I,
1999) and could not be deduced from genetic information alone.
Multiple genes and (multiple) environmental factors contribute to the
disease process and its clinical outcome. In AD (Alzheimer’s Disease),
only a minority of cases can be linked to hereditary gene mutations. 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).
Disease models in pharmaceutical research
At the end of the drug discovery pipeline, there are patients waiting for
treatments, company presidents and shareholders waiting for profit and
governments trying to balance their health care budget. For pharmaceutical
and biotech companies, the critical issue is to select new molecular
entities (NME) for clinical development that have a high success rate of
moving through development to drug approval. Finding new drugs (which can
be patented to protect the enormous investments involved) and at the same
time reducing unwanted side effects is vital for the industry. The cost to
develop a single drug which reaches the market has increased tremendously
in recent years and only 3 out of 10 drugs which reached the market in the
nineties generated enough profit to pay for the investment (DiMasi, J.,
1994; Grabowski H, 2002; DiMasi JA, 2003). This is mainly due to the low
efficiency and high failure rate of the drug discovery and development
process. Pharmaceutical companies are always trying to reduce this
failure rate in order to bring the enormous costs down involved in drug
discovery and development. Only about 1 out of 5,000 to 10,000 drugs makes
it from early pre-clinical research to the market, which is not an example
of a highly efficient process. The current focus of the pharmaceutical
industry on blockbuster drugs is a consequence of the mismatch between the
soaring costs and the profits required to keep the drug discovery and
development process going. If the industry cannot bring the costs down, it
may as well try to raise its income by changing its price policy, but this
shifts the solution for the problem from in- to outside the company and
places the burden on the national health care systems. Companies which
were more successful in the past achieved a higher efficiency even without
the availability of extensive genomic and proteomic data, new low-level
disease models, technology and more data alone are not sufficient to
improve the drug discovery process (Drews J. 1999; Horrobin DF, 2003; Omta
S.W.F., 1995).
To be complete, there are alternative views which criticize the
calculation of the cost of drug discovery and development. The consequence
of accepting this alternative view would be that the pharmaceutical
industry would be losing money due to costs outside its core mission,
which is even worse, because research and development can be improved, but
this would not help in this case. The result is in each case, that drugs
are only worth while to develop, if they have an enormous market
potential, otherwise they do not earn back the money invested, when they
finally they reach the market.
The basic numbers for time spent and costs made in drug discovery and
development can be found in several documents published by institutes
which generate reports about the pharmaceutical industry (Boston
Consulting Group, Tufts Center for the Study of Drug Development,
Pharmaceutical Research and Manufacturers of America (PhRMA) , etc.). Let
us now take a closer look at the drug discovery and development process.
It takes an average pharmaceutical company about 10 to 15 years and US$
500 to US$800 million to bring one new drug to the market. Of these 15
years about 6.5 years or 43 percent of the total time is spent in
pre-clinical research and about 7 years or 46 percent of the total time is
time spent in clinical research (1.5 years in phase I, 2 years in phase II
and 3 years in phase III). To process a New Drug Application (NDA) takes
the FDA on average 1.5 years based on the results and documents provided
by the pharmaceutical industry. About 0.1 percent of the original
molecules screened in drug discovery enter phase I (5 out of 5,000 to be
optimistic) and 0.02 percent of the original molecules finally reach the
FDA (1 out of 5,000). Of the 5 molecules entering phase I, about 4 out of
5 or 80 percent fail to make it to a NDA. After approval by the FDA, the
drug hits the market and enters phase IV of the clinical study process.
What can we learn out this numbers? The drug discovery process (target
identification, target validation, lead identification/optimization and
preclinical development such as ADMET) fails to predict the failure of a
drug in clinical development for 4 out of 5 or 80 percent of the molecules
which enter phase I. A new drug spends about 90 percent or 13.5 years of
his career within the discovery and development process, before it reaches
the FDA for the last 10 percent or 1.5 years. So the FDA does not account
for the majority of the time it takes to bring a new drug to the market,
nor does it account for the majority of failures which is only 20 percent
or 1 out of 5 drugs which enter phase I or 1 out of 5,000 (0.02 percent)
if we start from the beginning of the process. Although the investments in
the early stages of the drug discovery process have increased
tremendously, this means nothing compared to the cost of failure in phase
III of a clinical trial. A failure in phase IV in general means lawsuits
against the company and a serious blow to its reputation.
In order to improve this process, where should we try to optimize it?
After about 7 years in pre-clinical research, a new drug is ready for
filing an initial new drug application (IND) after which the FDA’s
Center for Drug Evaluation and Research (CDER) oversees the clinical
studies. The clinical trials, from phase I to III are highly regulated and
a company can only optimize the flow of events, but up to a large part it
cannot decide freely what needs to be done in these stages of the process.
Once a drug hits the FDA (CDER), strict rules need to be followed for the
approval and failure to comply will only delay this process. So it is by
improving the quality and shortening the process in drug discovery, a
pharmaceutical company can make a significant difference.
How should we proceed to improve drug discovery? We have seen an enormous
investment in research at the infra-cellular level, such as HTS, genome
based and proteome based disease models in the past ten years and at the
same moment have witnessed a disproportional decline in the productivity
of research and development in drug discovery (Bleicher KH, 2003). The
pharmaceutical industry has yet to find a way to reduce its high attrition
rates (Kola I., 2004). The consolidation in the pharmaceutical industry
will not solve this problem in the long run, as it only reduces the costs
but does not improve scientific productivity; it only postpones the moment
of truth. The scientists themselves will have to find new ways to improve
their productivity; management cannot do this in their place. Society
tries to protect itself against the adverse effects of new drugs, such as
with Thalidomide in the sixties (McBride WG, 1961) This is done by
increasingly stringent regulations but the currently used methods in the
discovery process for new drugs cannot keep pace with these new
requirements. However, as we can see, increasingly strict regulations do
not explain all the problems pharmaceutical research is facing today.
There is a fundamental problem with studying disease-relevant mechanisms
in the current disease models as the pharmaceutical industry has been
investing heavily in studying the bricks, instead of looking at the
building as a whole. You could also think of it as a pointillist painting,
of which we have been looking at the individual dots, instead of looking
at the entire painting. Another analogy is that we are trying to explain
the tidal patterns of the oceans, by studying a water molecule and
ignoring the moon. We have to look at biological phenomena at the
appropriate scale of integration and from a functional point of view in
order to get a grip on the development of pathological processes. We
should try to understand disease processes at a higher level of biological
integration, closer to the clinical reality, than the genome or proteome.
An integrated cellular approach is needed to study disease processes
(Lewis W. 2003).
The early stage disease models don’t work as they should do and do not
provide enough predictive power. One can study cellular components, like
DNA and protein as such, but this will not reveal the complex interactions
going on at the cellular level of biological integration or in other
words, the cytome . Both medicine and pharmaceutical research would
benefit from using more cell oriented disease models and even higher-order
models, instead of using infra-cellular models to try to describe complex
pathological processes at a molecular level and getting lost in the maze
of molecules which are the building blocks of cells. An important moment
in the drug discovery and development pipeline is the transition from
discovery research to clinical development, for which different approaches
to develop gatekeepers have been proposed to reduce the failure rate in
drug development on both sides of the transition (Lappin G., 2003;
Nicholson J.K., 2002; Pritchard J.F., 2003). Drug discovery should
improve the quality of drugs it allows to enter development and drug
development should be able to protect itself from drugs likely to fail in
phases I to III. A better quality of drugs entering drug development is
needed, not just more quantity. Failing in larger numbers will not bring
the solution to create a better process from discovery to phase III an IV.
A highly defined oligo-parametric infra-cellular disease model used in
High Throughput Screening (HTS) which in its setup ignores the complexity
of higher order biological phenomena, may produce beautiful results in the
laboratory, but fails to generate results of sufficient predictive power
to avoid considerable financial losses later on in the drug discovery
pipeline (Bleicher KH, 2003). A living cell may be a less well defined
experimental environment for the biochemist, but it will provide us with
the additional modulating influences on our disease models which are lost
in lower-order disease models.
Sub-cellular disease models
Studying subcomponents of cellular pathways ignores the functional unity
of the biological processes in the cell and the functional interactions
between pathways. Without a better understanding of the phenotypic and
functional outcome in the cell, the failure rate of the drug discovery
process will remain high and very costly. There is a predictive deficit in
the current oligo-parametric disease models used in pharmaceutical
research which necessitates complex and expensive studies later on in the
drug development pipeline to make up for the predictive deficit. The
popular techniques to explore and analyze low-dimensional data at high
speed are based on the idea that this would provide all the data with
sufficient predictive power to allow for a bottom-up approach to drug
discovery. The current High Throughput Screening (HTS) and other early
stage methods allow gathering low-dimensional data at high speed and
volume, but their predictive power is too low as they lack depth of
descriptive power (Perlin MW, 2002; Entzeroth M, 2003). The knowledge
gathered at the infra-cellular level has to be viewed in its relation to
the (living) cell and the biological and non-biological processes
influencing its function and health, which requires a top-down functional
and phenotypical approach rather than a bottom-up descriptive approach.
Complex disease processes cannot be explained by simple oligo-parametric
low-level models. A high-speed oligo-parametric disease model does not
equal high predictive power. It is not the ability to study a simplified
disease model at high speed which will allow us to succeed, but we must
study and verify the functional outcome of the disease process itself.
A game of chess is not described by naming its pieces, but by the spatial
and temporal interaction of both players or in other words the flow of
actions and reactions, described in a space-time continuum and if we add
the color it is a spatio-spectro-temporal flow of events. The individual
pieces or moves do not explain the final outcome of the game, only when
the entire process is analyzed from a positional and functional point of
view we can understand the reason why one player wins or loses. You have
to study a game of chess at the appropriate organizational level in order
to understand it or you will fail to find an explanation for the outcome
of the game.
Metabolic variation in disease models
Nowadays the first stages of drug discovery use genetically homogeneous
disease models, which as a result do not show the same metabolic
heterogeneity of patient populations. Pharmaco-genomics is used to study
differences in drug metabolism, but not to design or use early stage
disease models with sufficient genetic heterogeneity to select drug
molecules which will hold their activity in a metabolic heterogeneous
environment. Genetic heterogeneity and metabolic variation are not taken
into account in the first stages of the drug discovery process. Optimizing
a drug molecule for binding to one particular genetic variant, imminently
leads to failure in a normal distributed patient population.
Cellular disease models
Cellular disease models need to be related to at least the in vivo
cellular disease process we want to study, so a validation of this
correlation is important (Dimitrova D. S., 2002; Lidington EA, 1999;
Thornhill MH, 1993).
We now know that metabolic pathways show complex interactions and that
gross genetic rearrangements can impair entire parts of cellular
metabolism. The cellular models used in research should be validated for
their functional and phenotypical representation of in vivo, in-organism
processes. However many popular cell lines are not selected for their
close linkage to clinical reality, but for their maintainability in the
laboratory, lack of phenotypical variation, ease of transfectability, etc.
.. It is assumed that those cellular models are a valid representative of
the disease process, but almost never a thorough assessment is being done.
Primary cell lines cells in general require a more complex tissue culture
medium than most popular cell lines. Cancer cells (and transformed cells)
can usually grow on much simpler culture medium. Replicative senescence
and varying behavior at each passage (which may necessitate a change of
cell lines for long term experiments) also make primary cell lines less
popular, as they necessitate a change of cell lines and variability in
experimental data. Reduction of unpleasant variability in experiments by
choosing a specific disease model may create ‘nice’ results, but of a
reduced predictive value. Quite often results obtained with one cell line,
cannot be confirmed by using another cell line, without even talking about
primary cells.
CHO cells (Chinese Hamster Ovary, Cricetulus griseus) are used in many
assays, but they are not derived from a human cell and are aneuploid
(Tjio, J. H., 1958). HeLa cells are derived from an aggressive cervical
cancer; they have been transformed by human papillomavirus 18 (HPV18) and
have different properties from normal cervical cells (Gey, G.O., 1952).
The U-2 OS osteosarcoma cell line is easy to maintain and transfect
(Ponten J, 1967). The PC12 cell line which responds reversibly to nerve
growth factor (NGF) has been established from a rat adrenal
pheochromocytoma, it has a homogeneous and near-diploid chromosome number
of 40 (Greene LA, 1967). HEC cells are derived of a human endometrial
adenocarcinoma cell line (Kuramoto H., 1972) and are also very popular.
But even within individual cell lines there is not always homogeneity in
phenotype and function. Cancer cells in culture show chromosomal
instability as they tend to lose parts of chromosomes (Duesberg P., 1998,
Duesberg P, 2004). Continuous sub-cultivation of cells and an increase in
the number of passages may lead to chromosome rearrangements and loss of
functional reactivity (Dzhambazov B, 2003). Loss of function destabilises
a cell when critical parts of pathways are lost.
Many of the most popular cell lines lack parts or even entire chromosomes
and therefore large chunks of metabolic pathways. A drug molecule can not
interact with the proteins which are not present in the cell line and an
adverse or even positive effect will go unnoticed. Functional loss of
proteins and enzymes in cancer cell makes them unresponsive to drugs if
the protein(s) which are the target of a drug are lost without killing the
cell as such.
A traditional (homogeneous) cell culture in the laboratory may not yet
mimic the physiological conditions in an entire organism, so our approach
to cell-based research (and beyond) requires some redesign also. Creating
a virtual organism, by differential screening of a multitude of cell type
representing the main cell types in the human body (cardiomyocytes,
hepatocytes …) could help us to improve the predictive value of cellular
disease models.
In recent years we have seen developments towards an up-scaling of the
capacity of cellular research. Techniques such as High Content Screening
(HCS) can be applied to cellular systems on a large scale (Abraham VC,
2004). Subcellular differential phenotyping is already possible on a large
scale by using human cell arrays (Conrad C, 2004).
From individual cell to cytome
Due to the heterogeneity of cell types and between cells in a healthy and
disease state, we need to take this heterogeneity into account. Cytomes
can be defined as cellular systems and the subsystems and functional
components of the body. Cytomics is the study of the heterogeneity of
cytomes or more precisely the study of molecular single cell phenotypes
resulting from genotype and exposure in combination with exhaustive
bioinformatics knowledge extraction (Davies E, 2001; Ecker RC, 2004).
In order to get the broader view on pathological processes, we should move
on to the phenotypical and functional study of the cellular level or the
cytome in order to understand what is really going on in important disease
processes. Although the genome and proteome level have their predictive
value in order to understand the processes involved in disease (and
health), the cytome level allows for an understanding of pathological
phenotypes at a higher level. By integrating the knowledge from the genome
and proteome, we could give guidance to the exploration of the cytome,
which was not possible before this knowledge was available. The cytome
level will also provide guidance to focus the research at the genome and
proteome level and so creating a better cross-level understanding of what
is going on in cells (Gong JP, 2003). Some would see this as taking a step
back from the current structural and systematic descriptive approach, but
it is mainly a matter of integrating research at another level of
biological integration and looking in a different way to the web of
interactions going on at the cellular level. Biological processes do not
exist in a void, but they are a part of a web of interactions in space and
time, rather than being an island on their own. A cell is a
multidimensional physical structure (3D and time) with a finite size, not
a dimensionless quantity. We cannot ignore the spatial and temporal
distribution of events, without losing too much information.
In recent years the tools have matured to start studying the cellular
level of biological integration, but the tools are still used in the same
way as if they were derived from low-content high-throughput phenomena as
this is still the dominant research model. The tools to generate and
explore a high-dimensional feature space are still scattered and not
brought into line with the exploration of the cytome.
Functional processing in cellular pathways
The interconnection of genome, proteome and cytome data will be necessary
in order to allow for an in-depth understanding of the processes and
pathways interacting at the cellular level. A monocausal approach will
have to be replaced with a poly- and pluricausal approach in order to
understand and explain the phenomena going on at the cellular level.
Pluricausal means causal contributions at different levels, such as genes,
other cells and environmental influences. Polycausal means multiple causal
contributions at the same biological level, such as polygenic diseases or
multiple agonistic and antagonistic environmental influences. The concept
of a multithreaded, multidimensional, weighed causality is needed in order
to study the web of interactions at the cellular level. A drug modulates
cellular function, but changes can de studied at different levels of
biological integration:
Disease outcome = drug x (a x clinicaln + b x physiologicalp + c x
cellularq + d x geneticr )
Diagnosis and drug discovery merge if we take parallel models for both. A
cause (e.g. a single gene defect, a bacteria) can have multiple
consequences and as such be poly-consequential, which is the mirror
situation of a single consequence being caused by multiple causes
(co-causality or co-modulation) acting either synergistic or antagonistic
(e.g. a disease with both a genetic an environmental component). In
reality, a pathological condition is a mixture of those extremes (e.g. a
bacterial or viral infection and the host’s immune system) and as such a
simple approach is not likely to succeed in unraveling the mechanism of a
disease. With the current systematic and descriptive approach however, we
get lost in the maze of molecular interactions, as we are looking at too
low a level of biological integration and we get lost in a maze of
structures and interactions. The cell is the lowest acceptable target, not
its single components, like DNA or proteins.
We are looking at the alphabet, not even words or sentences, nature is not
a dictionary, but it is a novel. We should study the flow of events in a
cell with more power, not only the building blocks. As an example, Mendel
did not need to know about DNA in order to formulate his laws of
inheritance and he did not know that the discovery of the physical carrier
of inheritance, DNA, would confirm his views later on, but his laws are
still valid as such. Certainly physics was not at the stage it was in the
20th century when Newton formulated the law of gravity. When Einstein
formulated his relativity theory, he did not have modern physics at his
disposal. His theory does not fit well to the quantum level, but does
explain phenomena at a higher level of functional integration and as such
is an appropriate model. What we find should not be in contradiction to
what structural descriptive research discovers, but we should not wait for
its completion to start working on the problems we are facing in medicine
and health care today.
How to explore and find new directions for research
At the moment we expect that an oligo- or even mono-parametric analysis
will allow us to draw conclusions with sufficient predictive power to work
all the way up to the disease processes in an entire organism. We are
using disease models with a predictive deficit, which allow us to gather
data at great speed and quantity, but in the end the translation of the
results into efficient treatment of diseases fails in the majority of
cases. The cost of this inefficient process is becoming a burden, which
both society and the pharmaceutical industry will not be able to support
indefinitely. As "the proof is in the pudding", not in its ingredients, we
have to improve the productivity of biomedical and pharmaceutical research
and broaden our functional understanding of disease processes in order to
prepare ourselves for the challenges facing medicine and society.
If there were no consequences on the speed of exploration in relation to
the challenges medicine is facing today, the situation would of course be
entirely different. In many cases, the formulation of an appropriate
hypothesis is very difficult and the resulting cycle of formulating a
hypothesis and verifying it is a slow and tedious process. In order to
speed up the exploration of the cytome, a more open and less deterministic
approach will be needed. Analytical tools need to be developed which can
find the needle in the haystack, without a priori knowledge or in other
words we should be able to find the black cat in a dark room, without
knowing or assuming that there is a black cat. An open and
multi-parametric exploration of the cytome should complement the more
traditional hypothesis driven scientific approach, so we can combine speed
with in-depth exploration in a two-leveled approach to cytomics. The
multi-parametric reality which we need to deal with requires a more
multi-factorial exploration than the way we explore the cellular level at
this moment.
We now close our eyes to much of the complexity we observe; because our
disease models are not up to the challenge we are facing today. Feeling
happy with answers to questions in low-complexity disease models will not
help us at the end of the drug discovery pipeline. We reduce the
complexity of our datasets beyond the limits of predictive power and
meaningfulness. We must reduce the complexity of possible conclusions
(improvement or deterioration), but not the quality of data representation
or data extraction into our mathematical models. The value of a disease
model does not lie in the technological complexity of the machinery we use
to study it, but in its realistic representation of the disease process we
want to mimic. A disease model which fails to generate data and
conclusions which hold into drug development, years later, fails to
fulfill its mission. Disease-models are not meant tot predict future
behavior of the model, but to predict the outcome of a disease and a
treatment. The residual gap between the model and the disease is in many
cases too big to allow for valid conclusions out of experiments with
current low-level disease models. Due to deficient early-stage disease
models, the attrition rate in pharmaceutical research is still very high
(80 percent or 4 out of 5 drugs in clinical research). The easy targets to
treat are found already, we need a paradigm shift to tackle the challenge
ahead of us.
Gathering more and better quality information about cellular processes,
will hopefully allow us to improve disease models up to a point where
improved in-silico models will help us to complement in-vivo and in-vitro
disease models (Takahashi, K., 2003). What to do and the way to go?
The goal of a Human Cytome Project
The phenotypical and functional characterization of the (human) cytome is
the ultimate goal of an endeavor on the scale of a Human Cytome Project.
We should reach a point where we are able to design disease models which
are capable to capture the complexity of the in-vivo in-organism dynamics
of (a) disease processes with high predictive power. This knowledge should
be made broadly available for the improvement of diagnostics and disease
treatments. It is the prerequisite to come to a better understanding of
disease processes and to improve treatments for new, complex and life
threatening diseases for which we do not find an answer with our current
genome and proteome oriented approach only.
Studying the Cytome
First try to walk and then run. Studying the (human) cytome as such is
basically another way of looking at research on cellular systems. We go
from a higher level of biological organization (cytome) to a lower one
(proteome and cytome). Any research which starts from the molecular single
cell phenotypes in combination with exhaustive bioinformatics knowledge
extraction, is cytomics (Valet G, 2003). The only thing you need is
something like a flow-cytometer or a (digital) microscope to extract the
appropriate datasets to start with. Even a small lab or group can take
this approach and prove the concept, either for diagnostics, drug
discovery or basic research. Generating cytome-oriented data and getting
results is within reach of almost every scientist and lab. Increasing the
throughput may be required for industrial research and for a large scale
project, but this is not necessary for a proof of concept or for studying
a specific subtopic.
Organizational aspects
To study the entire human cytome will require a broad multidisciplinary a
multinational approach, which will involve scientists from several
countries and various disciplines to work on problems from a functional
and phenotypical point of view and top-down, instead of bottom-up. Both
academia and industry will have to work together to avoid wasting too much
time on scattered efforts and dispersed data. The organizational
complexity of a large multi-center project will require a dynamic
management structure in which both academia and the industry participate
in organizing and synchronizing the international effort. Managing and
organizing such an endeavor is a daunting task and will require excellent
managerial skills from those involved in the process, besides their
scientific expertise (Collins F.S., 2003b).
The challenges of Human Cytome Project will not allow us to concentrate on
only a few techniques or systematically describing individual components,
but we must keep a broad overview on the cell and its function and
phenotype by multi-modal exploration. We will need an open systems design
in order to be able to exchange data and analyze them with a wide variety
of exploratory and analytical tools in order to allow for creating a broad
knowledgebase and proceed with the exploration of the cytome without
wasting too much time on scattered data.
The project should be designed in such a way that along the road
intermediate results would already provide beneficial results to medicine
and drug development. Intermediate results could be derived from hotspots
found during the process and worked out in more detail by groups
specializing in certain areas. As such the project could consist of a
large scale screening effort in combination with specific topics of
immediate interest. The functional exploration of pathways involved in
pathological processes, would allow us to proceed faster towards an
understanding of the process involved in a disease. It is best to take a
dual approach for the project, which on one side focuses on certain
important diseases (cancer, AD …), and on the other side a track which
focuses on cellular mechanisms such as cell cycle, replication, cell type
differentiation (stem cells). The elucidation of these cellular
mechanisms, will lead to the identification of hot-spots for further
research in disease process and allow for the development of new
therapeutic approaches.
Technology
Every scientific challenge leads to the improvement of existing
technologies and the development new technologies. Technology to explore
the cytome is available today and exciting developments in various fields
are going on at the moment.
Advanced microscopy techniques are available to study the morphological
and temporal events in cells, such as confocal and laser scanning
microscopy, digital microscopy, spectral imaging, FRET, FLIM, FRAP,
labelling of biomolecules by quantum dots, EM.
High Content Screening (HCS) is available for high speed and large volume
screening of cells and tissues.
Flow Cytometry allows us to study cells in great detail and interesting
developments are leading to fast imaging in flow.
Data analysis an data management
Managing and analyzing data in a multidimensional linked feature space or
hyperspace will require a change in the way we look at data analysis and
data handling in order to succeed. Building the multidimensional matrix of
the web of cross-relations between the different levels of biological
organization, from the genome, over the proteome, cytome all the way up to
the organism and its environment, while studying each level in a
structural (phenotype) and functional way, will allow us to understand the
mechanisms of pathological processes and find new treatments and better
diagnostics tools. A systematic descriptive approach without a functional
complement is like running around blind and it takes too long to find out
about the overall mechanisms of a pathological process or to find distant
consequences of a minute change in the pathway matrix.
We should also get serious on a better integration of functional knowledge
gathered at several biological levels, as the scattered data are a problem
in coming to a better understanding of biological processes. The current
data storage models are not capable of dealing with heterogeneous data in
a way which allows for in-depth cross-exploration. Data management systems
will need to broaden their scope in order to deal with a wide variety of
data sources and models. Storage is not the main issues, the use and
exploration of heterogeneous data is the centerpiece of scientific data
management. Data originating from different organizational levels, such as
genomic (DNA sequences), proteomic (protein structure) and cytomic (cell)
data should be linked. Data originating from different modes of
exploration, such as LM, EM, NMR and CT should be made cross-accessible.
Problems to link knowledge originating from different levels of biological
integration is mainly due to a failure of multi scale or multilevel
integration of scientific knowledge, from individual gene to the entire
organism, with appropriate attention to functional processes at each
biological level of integration.
Standardization and quality
On the experimental side, standardization of experimental procedures and
quality control is of great importance to be able to compare and link the
results from multiple research-centers. But quality is not only a matter
of experimental procedures, but also of disease model validation and
verifying the congruence of a model with clinical reality.
We need to set up procedures for instrument set-up and calibration, define
experimental protocols (reagents…) and standardize data exchange (XML
…). The methods used for data analysis, data presentation and
visualization need to be standardized. We need to define quality control
(QC) procedures and standards which can be used by laboratories to test
their procedures. A project on this scale requires a registration and
repository of cell types and cell lines (e.g. ATCC, ECCC). This way of
working is already implemented for clinical diagnosis, by organizations
such as the EWGCCA, which could help to implement standards and procedures
for a Human Cytome Project. References
* Abraham VC, Taylor DL, Haskins JR., High content screening applied
to large-scale cell biology, Trends Biotechnol. 2004 Jan;22(1):15-22.
* Audrezet MP, Chen JM, Raguenes O, Chuzhanova N, Giteau K, Le
Marechal C, Quere I, Cooper DN, Ferec C, Genomic rearrangements in the
CFTR gene: extensive allelic heterogeneity and diverse mutational
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Human Cytome Project ?, Dec. 1 2003,
http://news-reader.org/article.php?...l&post_nr=14902
* Van Osta P., Geusebroek J.M. , Ver Donck K., Bols L., Geysen J., ter
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Meetings
* Focus On Microscopy –2004
* ISAC XXII Montpellier – 2004
* European Microscopy Congress –2004 * EWGCCA –2004
Links
* Towards a Human Cytome Project
* Draft: Human Cytome Project
* Cytomics - Info
* Functional genomics
* Cyttron
* Prediction in Cell-based Systems (Predictive Cytomics) * Biomedical
Structural Research
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