ME en de Gezondheidsraad
Jan van Roijen - 15.03.2005 03:09
Zeer Geachte Volksvertegenwoordiger,
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Vermoeidheids-syndroom van de Gezondheidsraad.
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*predisponerende-, uitlokkende- en instandhoudende factoren*.
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Public Health Genomics at CDC
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Accomplishments & Priorities 2004 (CDC/Coordinating Center
for Infectious Diseases) (includes a lot of references to CFS)
 http://www.cdc.gov/genomics/activities/ogdp/2004/cocid.htm or
http://www.cdc.gov/genomics/activities/ogdp/2004/print/CoCID_2004.pdf
CoCID Genomics Working Group:
In 1996, CDC's National Center for Infectious Disease (NCID)
established the Genetic Working Group (GWG) with the purpose
of addressing issues concerning the role of genetics in
infectious diseases and promoting collaboration between
investigators . This working group was further expanded in 2003
to coordinate the genomic research interests of NCID, National
Immunization Program (NIP), and National Center for HIV, STD,
TB Prevention (NCHSTP) and the Office of Genomics and
Disease Prevention (OGDP). The Coordinating Center for
Infectious Diseases (CoCID, created May 2004) includes NCID,
NIP and NCHSTP.
The overall mission of this working group includes:
Identifying and conducting investigations of host genes
associated with infectious diseases and vaccine safety that
have public health relevance and to which interventions and
preventions can be targeted; Building laboratory and
epidemiological capacity for genomics; Training public health
professionals in laboratory and epidemiological aspects of host
genomics and infectious diseases; Communicating information
about genomics and infectious diseases to the public through
meetings, seminars, web sites, and publications.
Seven high priority research areas have been identified for host
genetics in CoCID. These reflect areas where CoCID and its
partners are making or can make a significant impact in
research that leads to new control and prevention methods or
development of control and prevention strategies.
*) Infectious Diseases: resistance and susceptibility to and
severity of infectious diseases.
*) Chronic disease etiology: clarifying host-pathogen
interactions in the causation and pathophysiology of chronic
diseases such as **chronic fatigue syndrome (CFS)**, arthritis,
cancer, and cardiovascular diseases.
*) Vaccines: response, failure, adverse events; using host
genetic information for vaccine design.
*) Antimicrobial and adjunctive therapy response: failure,
adverse events and development of new therapies.
*) Prevention: identification of genetically at-risk populations for
infectious diseases; targeting of prevention strategies to these
populations.
*) Development of rapid molecular tools based on DNA, mRNA
and protein expression patterns that can be used in surveillance,
studies monitoring response to therapy, and basic-research.
*) Drug metabolism: defining pharmacogenetic factors
associated with response to drug therapy, drug interactions, and
disease outcome in tuberculosis and other infectious diseases.
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Selected extracts:
Infectious Diseases: Resistance and Susceptibility to Infection
and Disease, and Severity of Infectious Diseases
A better understanding of the role of various genetic factors in
the outcome of infectious diseases will lead to development of
novel therapies and better prevention efforts. Recent progress in
high throughput genotyping and decoding of the human genome
has led to new optimism in identifying such genetic risk factors.
The availability of well-characterized large cohorts from different
populations will be critical in identifying such risk factors. CDC
has access to epidemiologically well-characterized different
infectious disease cohorts. These cohorts are unique resources
in identifying such genetic risk factors. Several investigations at
CDC that have integrated genomics research are highlighted
below.
..
The chronic fatigue syndrome (CFS) program to integrate
genomics into chronic infectious diseases and illness
(CDC/NCID/DVRD/Viral Exanthems and Herpes Virus Branch
(VEHB)): The CFS Molecular Epidemiology Program was
established in 1997. The CFS Program was designed to apply
rapidly evolving cutting-edge genomics, proteomics, and
bioinformatics technology to epidemiologic studies whose
objective is CFS prevention and control. Its aim is to
characterize CFS at a systems biology level by integrating
surveillance, case definition, and clinical studies with genomics,
proteomics and bioinformatics. The effort includes data from
populationbased and clinical studies. Examples of each of these
are described below.
***) Integration of gene expression, clinical, and epidemiological
data to characterize CFS
*) Integrated the peripheral blood gene expression results with
epidemiological and clinical data to determine whether CFS is a
single or heterogeneous illness
*) Using statistical tests and cluster analysis to distinguish CFS
subjects and identify differentially expressed genes
*) The latest results suggest that CFS is a heterogeneous
illness. The differentially expressed genes imply fundamental
metabolic perturbations that will be further investigated and
illustrates the power of microarray technology for furthering our
understanding CFS37
***) Host gene expression profiles that precipitate post-infective
and chronic fatigue syndromes in response to common viral and
rickettsial infections
*) An example of a CFS gene expression study that is based on
model systems
*) Studying host gene expression profiles following acute
infection with Epstein Barr Virus (EBV), Coxiella burnetti (the
causative agent of Q fever) and Ross River Virus (RRV) in
collaboration with the University of New South Wales in Sydney,
Australia. Some observations from this longitudinal study
include:
a) severity of the acute illness is a powerful predictor of the
likelihood of development of post-infective fatigue syndrome
(PIFS) at three and six months,
b) although the pattern and severity of symptoms in the acute
illness were correlated with production of pro-inflammatory
cytokines, these relationships did not persist through to the PIFS
phase of the illness,
c) identified several novel gene expression correlates of
individual symptoms using microarray gene expression profiling
***) Exercise responsive genes measured in peripheral blood of
women with chronic fatigue syndrome and matched control
subjects
*) Measuring peripheral blood gene expression profiles of
women with CFS and matched controls before and after
exercise challenge to search for markers of CFS-associated
post-exertional fatigue, differential expression of
exercise-responsive genes classified in chromatin and
nucleosome assembly, cytoplasmic vesicles, membrane
transport, and G protein-coupled receptor ontologies between
CFS patients and controls
***) Integration of gene expression and clinical data from CFS
and nonfatigued subjects enrolled in a two day clinical evaluation
in Wichita, Kansas
*) Following a well-characterized cohort of people with CFS over
a fouryear period to determine if unique gene expression
profiles are associated with symptom occurrence or persistence
of illness. Evaluated each subject and the corresponding
multiple samples using a 40,000-gene microarray. Data analysis
is in progress. This will also serve as the dataset for C3, the
CFS Computational Challenge, (described in section F 'Major
Conferences')
***) Development of a text mining tool that provides gene
information in specific disease and biological context
*) Pioneered a number of genomic and bioinformatics
technologies at CDC's VEHB (see CDC MAdB in Infrastructure
section below)
*) Simultaneous assessment of tens of thousands of genes
using highthroughput technology, such as gene expression
profiling using microarrays
*) Key to getting specific digital information associated with
genes is mining text in the appropriate biological, clinical and
epidemiological context. Development of a text-mining tool that
will provide biologic and disease relevant information for genes
identified as important by microarray gene expression profiling
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Major conferences
*) Integrating Disparate Data to Simulate Lymphocyte Function
(CDC/NCID/DVRD):
The CDC CFS Research Program sponsored a workshop,
Integrating Disparate Data to Simulate Lymphocyte Function, at
the Banbury Center, Cold Spring Harbor Laboratory, on
September 19-22, 2004.
The objective was to discuss current knowledge concerning
lymphocyte function and to identify means by which
computational modeling could be used to understand how this
complex biologic system functions in persons with CFS. The
workshop brought together experts in immunology, molecular
biology, computer sciences, and molecular modeling. Specific
aims were to
1) define the types of laboratory and clinical data involved in the
current concept of lymphocyte function in normal and abnormal
states;
2) present approaches for integrating genomic, proteomic,
clinical, and epidemiologic data in such models; and 3) define
the level of abstraction and types of assumptions necessary to
create the next generation of molecular models.
*) C3: The CFS Computational Challenge:
The CFS Research Program is hosting a CFS Computational
Challenge (C3).
The results of this challenge will help elucidate the
pathophysiology of CFS, identify markers of CFS (or subsets of
CFS) that may be useful for effective diagnosis and treatment of
CFS, and formulate hypotheses to test in future studies.
The CFS Research Program has conducted a 2-day in-patient
clinical study of 227 persons identified with CFS, other
unexplained chronically fatiguing illnesses, and randomly
selected non-fatigued controls from the general population of
Wichita, Kansas. Subjects were carefully evaluated medically
and psychiatrically.
Investigators obtained measurements of their neuroendocrine
status, cytokine profiles, sleep, cognitive function, and evaluated
their lifetime stress history and coping mechanisms.
To classify parameters of CFS, they evaluated disability, fatigue
characteristics, and the impact of cumulative symptoms. Finally
they measured expression levels of 40,000 genes in peripheral
blood cells.
The challenge will engage computer scientists,
bioinformaticians, statisticians, biologists and clinicians to mine
biologically and clinically meaningful information relevant to
diagnosis and therapeutic intervention of CFS from the Wichita
Clinical Study data set. Participants will be organized into
teams.
The challenge will begin with a 1-day workshop where an
introduction to CFS will be given along with a description of the
dataset for C3. Each teams results will be presented as a paper
and judged for biological and mathematical soundness by an
expert panel. All participants will present their results at the
Banbury Center, Cold Spring Harbor Laboratory, September
18-21, 2005.
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 E-Mail: j,van,roijen@chello.nl
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