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•What do we need?
•Where do we stand currently?
•What are the stumbling blocks?
•Where can/should/will we be in 5 years?
New Technological Developments in Diagnostic
TestingCovering Infectious Diseases, UC Berkeley / October 2,
2003
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•What do we need?
•Where do we stand currently?
•What are the stumbling blocks?
•Where can/should/will we be in 5 years?
New Technological Developments in Diagnostic
TestingCovering Infectious Diseases, UC Berkeley / October 2,
2003
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The New Yorker, January 29, 2001
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•What do we need? Environmental & Clinical:
Broad spectrum detection/dx tools Rapid, real-time (quantitative),
automated, (pt-of-care) monitoring
Standardized sampling methods Understanding of natural background
•Agent (variation: genetic, antigenic, geo, temp)
•Setting (related agents, non-biological issues)
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current Dxapproaches
system indicators
difficulty in detection
opportunity for benefit
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•What do we need?
•Where do we stand currently?
•What are the stumbling blocks?
•Where can/should/will we be in 5 years?
New Technological Developments in Diagnostic
TestingCovering Infectious Diseases, UC Berkeley / October 2,
2003
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Current procedures and technologies
Detection Culture (reference, complete analysis) Immunoassays
Solid-phase, hand-held (“SMART”), FACS, ELISA Nucleic acids
Amplification: PCR, SDA…Capture: magnetic, Ab, electricalDetection: fluor, ECL, chromo, bDNA;
microarray, mass spectroscopy
(reliance on few antibodies!)
(reliance on type strains!)
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Syndromes of suspected* microbial origin: success in achieving microbiological
diagnosis • pneumonia: ~50-70%• encephalitis: ~30%• sepsis: ~10%• acute diarrhea: ~20-50%
*suspected on basis of response to antibiotics, among other observations
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Why might traditional approaches for pathogen identification have failed?
• Reliance on cultivation--insensitivity• Microbial phenotypic markers unreliable:
one can be mislead when one asks a microbe to perform in the laboratory!
• Serology delayed, or impossible• PCR not well deployed, problems with low
clinical sensitivity
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•What do we need?
•Where do we stand currently?
•What are the stumbling blocks?
•Where can/should/will we be in 5 years?
New Technological Developments in Diagnostic
TestingCovering Infectious Diseases, UC Berkeley / October 2,
2003
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Major challenges, obstacles
Diversity of potential agents(including bioengineered, chimeric organisms)
Variability, varying evenness of agents,in nature
Defining relationship between detected agentand disease risk
Complex biological background! Sampling, processing procedures
(standardization, calibration in real world)
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The Tree of Life(based on rRNA sequences)
Bacteria
Eukarya
Archaea
Pace NR. A molecular view of microbial diversity and the biosphere. Science 1997; 276:734
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The Tree of Life(based on rRNA sequences)
Bacteria
Eukarya
human-associated
Archaea
Pace NR. A molecular view of microbial diversity and the biosphere. Science 1997; 276:734
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Bacteria
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Emergence of infectious diseases
•Societal events: poverty, crowding, conflict, migration
•Globalization of food supply•Environmental changes•Human behavior: sexual, recreational,
diet, travel•Impaired host defenses, antibiotic use•Public health infrastructure
•Acquisition of toxins,adhesins
•Antigenic variation, e.g. new capsule
•Broadened host range•Improved growth
or transmissibility•Acquisition of drug R
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What does it mean for an infectious disease to “emerge”?
• Evolution of new agent to cause disease
(evolution of virulence) • Previously-recognized agent causes disease
with new features (clinical, epidemiological, geographical, histological)
• Pre-existing, but previously-unrecognized
agent makes itself known (± new disease features)
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Pathogen discovery: basicsPathogen discovery: basics
pathology
identification / relatedness
causation?
molecular signature
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pathology
identification / relatedness
causation?
molecular signature
microbe
host
Pathogen discovery: basicsPathogen discovery: basics
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Pathogen discovery: seeking molecular signatures
•broad range PCR•microbial/viral survey “phyloarray”•subtractive/comparative methods
• representational difference analysis• differential display
•expression or phage display library screening
(using host antisera or T-cells)•small molecule or protein detection (MS)
•host genome-wide transcript profiling (microarray, other)•host protein profiling (microarray, MS)
Host as source of signature
Pathogen as source of signature
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Pathogen discovery: seeking molecular signatures
•broad range PCR•microbial/viral survey “phyloarray”•subtractive/comparative methods
• representational difference analysis• differential display
•expression or phage display library screening
(using host antisera or T-cells)•small molecule or protein detection (MS)
•host genome-wide transcript profiling (microarray, other)•host protein profiling (microarray, MS)
Host as source of signature
Pathogen as source of signature
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digest specimen, purify / concentrate DNA
broad range (bacterial) rDNA PCR
rRNA gene
conserved region
variable region
analyze sequence, infer phylogeny of putative organism(s)
build case for disease causation
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Rhinosporidiosis
•slow-growing tumors of nasal mucosa, ocular conj.•southern India, Sri Lanka: prevalence 1-2% children
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Rhinosporidium seeberi: a fungus?
sporangium
endospore
stain: PAS reagent
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DiplomonadsMicrosporidiaTrichomonadsFlagellatesSlimemoldsAmoebidaCiliatesApicomplexansAlgaePlantsAcanthamoebaFungiAnimals0.10Eukaryaevolutionary distance
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Perkinsus (Protozoan Oyster Parasite)
Sarcocystis (Coccidian Protozoan)
Xenopus (Frog)
Mytilus (Blue Mussel)Tripedalia (Jellyfish)
Microciona (Sponge)
Diaphanoeca (Choanoflagellate)
Rosette Agent
Rhinosporidium seeberiDermocystidium sp.
Dermocystidium salmonis
Psorospermium haeckelii
Ichthyophonus hoferi
Aspergillus
Chytridium (Chytrid)
Mucor (Bread Mold)
Acanthamoeba (Amoeba)
Zamia (Plant)
Porphyra (Red Algae)
Lagenidium (Oomycete)
Labyrinthuloides (Slime Net)
.10
Artemia (Brine Shrimp)
Animals
DRIPs
Fungi
ProtistsChlorophytes
Rhodophytes
Heterokonts
Apicomplexa
95
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Other Animals
Jellyfish
Sponges
Choanoflagellates
DRIPs
Fungi
Plants Protists
0.1
DRIPs:• deepest branch of animals• aquatic parasites• hosts=fish…&
Rs in humans: water exposure
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Bioterrorism: future considerations
• Mining nature (unconventional agents)• Improving upon nature
• Engineered pathogens: cloning ofknown virulence factors/”islands”, deletion of inhibitory factors, host modifying factors (eg, cytokines), shuffled “evolved” vir factors
• Novel agents (pathogenic proteins, bioregulators, chimeric agents)
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Major challenges, obstacles
Diversity of potential agents(including bioengineered, chimeric organisms)
Variability, varying evenness of agents,in nature
Defining relationship between detected agentand disease risk
Complex biological background! Sampling, processing procedures
(standardization, calibration in real world)
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4.3-6.2% healthy humans positive without history of
anthrax or anthrax exposure
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Bacteria
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Uncultivated TM7 in the human mouth
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We know more about the tropical rain forest than we
do about the human endogenous microbial
flora!
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Major challenges, obstacles
Diversity of potential agents(including bioengineered, chimeric organisms)
Variability, varying evenness of agents,in nature
Defining relationship between detected agentand disease risk
Complex biological background! Sampling, processing procedures
(standardization, calibration in real world)
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The New Yorker, November 19, 2001
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The realities of anthrax detection, 2001...
Lack of standardized collection methods Low specimen analysis through-put Inadequate laboratory surge capacity Slow turn-around, late in disease course Inadequate delivery and implementation of
state-of-the-art technologies in the field Data interpretation: negatives and positives
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•What do we need?
•Where do we stand currently?
•What are the stumbling blocks?
•Where can/should/will we be in 5 years?
New Technological Developments in Diagnostic
TestingCovering Infectious Diseases, UC Berkeley / October 2,
2003
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Some “near-term” goals
Library of high-affinity binding reagents More sensitive binding detection Extensive database of sequences,
other signatures High-throughput labs, with surge capacity Standardized, automated specimen collection
and processing procedures, technologies
Further development of DNA microarray
approaches
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•1600 unique 70-mers•~140 viral genomes•Sensitivity=~100 viral particles
“cold”RV-infected
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Phyloarray v2:~10,000 rDNA oligo probes
Agilent: Theo Sana, Paul WolbertMike Eisen (LBL)Pat Brown (Stanford)
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Unexplained Deaths Project
(CDC EIP)
Unexplained Deaths Project
(CDC EIP)
•Acute, life-threatening illness in ages 1-49,
previously healthy; all routine diagnostic tests (-); “enhanced passive surveillance”
•Seek patterns, clusters, clues; look forinfectious agents using molecular (research) tools
total pop = 7.78 million
Nikkari et al, Emerg Infect Dis 8:188-194, 2002; Hajjeh et al, Emerg Infect Dis 8:145-153, 2002
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Unexplained Deaths Project
(CDC EIP)
Unexplained Deaths Project
(CDC EIP)
• 137 cases fit definition (5/95-12/98); ≥0.5/100,000• syndromes: neuro (29%), respiratory (27%),
cardiac (21%), multisystem (13%)
What are the causes of the unexplained cases?viruses, toxins, non-microbial processes,...specimen limitations
• 28% explained (serology; broad range PCR=8/46 cases)• Putative causative agents are all known agents
-broad range PCR: N. meningitidis, S. pneumoniae
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Microbe (as target):• agent specificity (adjustable)• signatures more easily defined
Microbe or host: relative advantages
Host (as target):• agent need not be present• early diagnosis? • outcome predictions
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Can one recognize and classify clinical (and pre-clinical) states of infection by examining host
gene response patterns?
Potential advantages: • broad range• early• microbe not required in specimen• prognostic value (& targets)
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37,632 spots/elements~32,494 cDNAs~10,250 named genes; ~18,000 unique
genes
Alizadeh A et al., Cold Spring Harb Symp Quant Biol 64:71, 1999; Nature 403:503, 2000
Two-color, comparative hybridization format
Human cDNA microarray
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transcriptional control
Comprehensive gene expression profiles integrate host genotype and environmental input
mRNA
gene
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cDNA microarray procedures-1
(Relman & Brown)
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cDNA microarray procedures-2
200 10000 50.00 5.644800 4800 1.00 0.009000 300 0.03 -4.91
Cy3 Cy5
Image analysisData filteringNormalization
R/G ratio represents relative abundance of transcripts
Cy5Cy3
Cy5Cy3
log2
Pattern recognition:• unsupervised (class discovery)-
clustering, SOM, SVD (PCA), ICA
• supervised (class prediction)-SAM, support vector machines, t/f-test (DLDA, ANOVA), modeling (waveform, periodicity) Experiments
Gen
es
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clusterby
genes
clusterby
microbialstimuli
microbial stimuli
genes
2-Way Hierarchical Clustering Methodology
microbial stimuli microbial stimuli
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• Variability--How noisy is the background?
Must each individual serve as his or her own control? Do patterns provide insight into physiology and “intrinsic-ness”?
• How well do blood cells “report” on local
processes? Other sources...?
Issues involving a complex background: gene expression
patterns from blood in healthy individuals
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femalesmales 48 PBMC samples from 19 individuals
clustered on the basis of genes with intrinsic scores >2 SD from mean (340)(mean square pairwise difference between/ mean square pairwise difference within individuals)
Whitney A et al, PNAS 2003; 100:1896-1901
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48 PBMC samples from 19 individuals clustered on the basis of genes with highest intrinsic scores (340)
Donor-intrinsic gene
expression
females
males
Whitney A et al, PNAS 2003; 100:1896-1901
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Looking further into the future
Biosensors: remote (e.g. cells), endogenous (e.g. flora)
Hyperspectral imaging, analysis
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Conclusions
Challenges associated with detection
and diagnosis are significant, but worthy of major investment
Current status: platforms are quite
promising; real-world issues still need further attention
Major pay-off may be achieved if we
embrace larger aspects of biology and disease, and anticipate future threats
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Acknowledgements
StanfordPat BrownTrevor HastieAsh AlizadehJennifer BoldrickMary Brinig
Paul LeppCleber OuverneyStephen PopperKate RubinsAddie Whitney
CDCJim LeDuc Marc FisherPeter Dull
DukeBarth RellerChris WoodsDavid Murdoch
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