-
Ignacio S. Caballero
bioinformatics graduate programBoston University
Using the Host Immune Response To Hemorrhagic Fever Viruses
To Understand Pathogenesis and Improve Diagnostics
Connor Lab
National Emerging Infectious Diseases Laboratories
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Understanding the host immune response to Ebola virus infection
Part I
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Part IIDistinguishing between hemorrhagic fevers
by using the host immune response
Understanding the host immune response to Ebola virus infection
Part I
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Why focus on the host immune response?
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Why focus on the host immune response?
1. Hemorrhagic fever symptoms are likely caused by a dysregulated host response
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Why focus on the host immune response?
2. We can measure the activity of the players but we dont understand a lot of the rules
1. Hemorrhagic fever symptoms are likely caused by a dysregulated host response
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Hemorrhagic Fever Viruses
Marburg
Bryan Hansen RML, NIAID
Ebola
CDC Public Health Library
-
Hemorrhagic Fever Viruses
Marburg
Bryan Hansen RML, NIAID
LassaCDC Public
Health Library
Ebola
CDC Public Health Library
-
Geographical distribution
Ebola(pre-2014)
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Geographical distribution
MarburgEbola(pre-2014)
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Geographical distribution
MarburgEbola(post-2014)
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Geographical distribution
MarburgEbola(post-2014)
Lassa
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Hemorrhagic fevers share similar symptoms
Ebola & Marburg
Lassa
Early
Headache
Malaise
Fever
Malaise
Sore throat
Fever
Weakness
Weakness
Headache
-
Hemorrhagic fevers share similar symptoms
Ebola & Marburg
Lassa
Early
Headache
Malaise
Fever
Malaise
Sore throat
Fever
Weakness
Weakness
Headache
Vomiting
Diarrhea
Middle
Diarrhea
Joint pain
Rash
Pharyngitis
Pharyngitis
Vomiting
-
Hemorrhagic fevers share similar symptoms
Ebola & Marburg
Lassa
Early
Headache
Malaise
Fever
Malaise
Sore throat
Fever
Weakness
Weakness
Headache
Vomiting
Diarrhea
Middle
Diarrhea
Joint pain
Rash
Pharyngitis
Pharyngitis
Vomiting
Late
Multiorgan failure
Diffuse coagulopathy
Hypovolemic shock
Mucosal bleeding
Hypovolemic shock
Pulmonary edema
Edema
Multiorgan failure
Mucosal bleeding
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Reasons to study hemorrhagic fever viruses
1. Difficult to diagnose during the early stages
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Reasons to study hemorrhagic fever viruses
2. High mortality rates
1. Difficult to diagnose during the early stages
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Reasons to study hemorrhagic fever viruses
3. Lack of treatments and vaccines
2. High mortality rates
1. Difficult to diagnose during the early stages
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Reasons to study hemorrhagic fever viruses
3. Lack of treatments and vaccines
2. High mortality rates
1. Difficult to diagnose during the early stages
4. Potential to be used as bioweapons
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Part IIDistinguishing between hemorrhagic fevers
by using the host immune response
Understanding the host immune response to Ebola virus infection
Part I
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We use animal models to study the immune response
Macaque
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We use animal models to study the immune response
Macaque
Blood
Ebola virus infection
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We use animal models to study the immune response
Immune Cells
Centrifugation
Macaque
Blood
Ebola virus infection
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We use animal models to study the immune response
Immune Cell RNA
RNA Extraction
Immune Cells
Centrifugation
Macaque
Blood
Ebola virus infection
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We use animal models to study the immune response
Immune Cell RNA
RNA Extraction
Immune Cells
Centrifugation
Macaque
Blood
Ebola virus infection
at BSL-4 at BU
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We use animal models to study the immune response
Immune Cell RNA
RNA Extraction
Immune Cells
Centrifugation
Macaque
Blood
Ebola virus infection
at BSL-4 at BU
Sequenced Reads
RNA Sequencing
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We use animal models to study the immune response
Immune Cell RNA
RNA Extraction
GENE
Alignment & Quantification
Gene Expression Levels
Immune Cells
Centrifugation
Macaque
Blood
Ebola virus infection
at BSL-4 at BU
Sequenced Reads
RNA Sequencing
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Sequencing files
Bioinformatics pipeline
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Sequencing files
Macaque Transcriptome+Tophat
Aligned Reads
Bioinformatics pipeline
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Sequencing files
Macaque Transcriptome+Tophat
Aligned Reads
Raw Counts
Feature Counts
Bioinformatics pipeline
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Sequencing files
Macaque Transcriptome+
Normalized Counts
Trimmed Mean of M-values
(edgeR)
Tophat
Aligned Reads
Raw Counts
Feature Counts
Bioinformatics pipeline
-
Sequencing files
Macaque Transcriptome+
Normalized Counts
Trimmed Mean of M-values
(edgeR)
Design Matrix
+Tophat
Aligned Reads
Raw Counts
Feature Counts
Bioinformatics pipeline
-
Sequencing files
Macaque Transcriptome+
Normalized Counts
Trimmed Mean of M-values
(edgeR)
Design Matrix
+Fold changes and p-values
Negative Binomial GLM
Empirical Bayes (edgeR)
+
Tophat
Aligned Reads
Raw Counts
Feature Counts
Bioinformatics pipeline
-
Fold changes and p-values
Sequencing files
Macaque Transcriptome+
Normalized Counts
Trimmed Mean of M-values
(edgeR)
Design Matrix
+
Negative Binomial GLM
Empirical Bayes (edgeR)
+
Tophat
Aligned Reads
Raw Counts
Feature Counts
Bioinformatics pipeline
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Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
Ebola sequencing dataset
1000 FFU via intramuscular injection (Barrenas et al., 2015)
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Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
Ebola sequencing dataset
1000 FFU via intramuscular injection (Barrenas et al., 2015)
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Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
Ebola sequencing dataset
1000 FFU via intramuscular injection (Barrenas et al., 2015)
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Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
Ebola sequencing dataset
Ebola (vaccinated) sequencing datasetDays post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None None None1000 FFU via intramuscular injection (Barrenas et al., 2015)
1000 FFU via intramuscular injection (Barrenas et al., 2015)
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GENE
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GENE
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GENE
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What are the gene expression changes that we would expect to see
during Ebola infection?
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Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
DNANucleus
Cell membrane
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Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
DNANucleus
Cell membrane
-
Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
MDA5/RIG-I
detection
DNANucleus
Cell membrane
-
Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
MDA5/RIG-I
detection
DNANucleus
PIRF3
Cell membrane
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Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
MDA5/RIG-I
detection
DNANucleus
PIRF3
P PIRF3 IRF3
Cell membrane
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Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
MDA5/RIG-I
detection
DNANucleus
PIRF3
P PIRF3 IRF3
P
NFKBIKBa
Cell membrane
-
Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
MDA5/RIG-I
detection
DNANucleus
PIRF3
P PIRF3 IRF3 NFKB
P
NFKBIKBa
Cell membrane
-
Interferon Beta
Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
MDA5/RIG-I
detection
DNANucleus
PIRF3
P PIRF3 IRF3 NFKB
P
NFKBIKBa
Cell membrane
-
Interferon Beta
Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat
Virus particles
viral RNA
MDA5/RIG-I
detection
DNANucleus
Interferon
PIRF3
P PIRF3 IRF3 NFKB
P
NFKBIKBa
Cell membrane
-
Nucleus
Cell membrane
Interferon
The production of interferon-stimulated genes is part of the antiviral response
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Nucleus
Cell membrane
InterferonInterferon receptor
The production of interferon-stimulated genes is part of the antiviral response
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Nucleus
Cell membrane
InterferonInterferon receptor
The production of interferon-stimulated genes is part of the antiviral response
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STAT1STAT2
Nucleus
Cell membrane
InterferonInterferon receptor
The production of interferon-stimulated genes is part of the antiviral response
-
STAT1STAT2
Nucleus
Cell membrane
InterferonInterferon receptor
The production of interferon-stimulated genes is part of the antiviral response
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STAT1STAT2
Interferon-stimulated response element Nucleus
Cell membrane
InterferonInterferon receptor
The production of interferon-stimulated genes is part of the antiviral response
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Interferon stimulated genes
STAT1STAT2
Interferon-stimulated response element Nucleus
Cell membrane
InterferonInterferon receptor
The production of interferon-stimulated genes is part of the antiviral response
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Interferon stimulated genes
STAT1STAT2
Interferon-stimulated response element Nucleus
Cell membrane
InterferonInterferon receptor
MX1IFIT1ISG15
OAS1HERC5
The production of interferon-stimulated genes is part of the antiviral response
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Ebola virus contains a protein that inhibits the production of interferon
Ebola virions
viral ssRNA
MDA5/RIG-I
sensing
DNANucleus
PIRF3
Interferon Beta
Interferon
P PIRF3 IRF3 NFKB
P
NFKBIKBa
Cell membrane
-
Ebola virus contains a protein that inhibits the production of interferon
Ebola virions
viral ssRNA
MDA5/RIG-I
sensing
DNANucleus
PIRF3
Interferon Beta
Interferon
P PIRF3 IRF3 NFKB
P
NFKBIKBa
Cell membrane
eVP35
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Ebola virus contains a protein that inhibits the production of interferon
Ebola virions
viral ssRNA
MDA5/RIG-I
sensing
DNANucleus
PIRF3
P
NFKBIKBa
Cell membrane
eVP35
-
Ebola infection leads to an early increase in the expression of interferon-stimulated genes
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Interferon-stimulated genes in vaccinated animals dont become highly expressed after infection
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The majority of genes in vaccinated animals dont change their expression throughout infection
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A subset of important immune genes becomes highly expressed in vaccinated animals
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Hypothesis 1: An unknown mechanism allows infected cells to stimulate interferon production
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Hypothesis 2: Non-productively infected cells can still produce interferon
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1000 FFU via intramuscular injection (Barrenas et al., 2015)
Ebola intramuscular datasetDays post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
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1000 FFU via intramuscular injection (Barrenas et al., 2015)
Ebola intramuscular datasetDays post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
Ebola aerosol datasetDays post-infection 0 3 6 8
Number of samples 4 3 3 2
Clinical symptoms None Fever Severe Severe1000 PFU via aerosol exposure
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Different routes of infection lead to comparable immune responses
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Different routes of infection lead to comparable immune responses
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Ebola infection leads to an early and strong induction of interferon stimulated genes
Conclusions from Part I
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Ebola infection leads to an early and strong induction of interferon stimulated genes
Circulating immune cells dont show this pattern of activation in vaccinated animals
Conclusions from Part I
-
Ebola infection leads to an early and strong induction of interferon stimulated genes
Circulating immune cells dont show this pattern of activation in vaccinated animals
The route of infection does not appear to cause lasting differences in the immune response
Conclusions from Part I
-
Part IIDistinguishing between hemorrhagic fevers
by using the host immune response
Understanding the host immune response to Ebola virus infection
Part I
-
Current diagnostic methods require the presence of the virus in the blood
Time
Viral Infection
day 0
-
Current diagnostic methods require the presence of the virus in the blood
Time
Viral Infection
day 0
Initial symptoms
day 2-4
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Current diagnostic methods require the presence of the virus in the blood
Time
Viral Infection
day 0
Initial symptoms
day 2-4
Virus enters the blood (viremia)
day 4-6
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Current diagnostic methods require the presence of the virus in the blood
RT-PCR diagnostic becomes effective
Time
Viral Infection
day 0
Initial symptoms
day 2-4
Virus enters the blood (viremia)
day 4-6
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Current diagnostic methods require the presence of the virus in the blood
RT-PCR diagnostic becomes effective
Time
Viral Infection
day 0
Initial symptoms
day 2-4
Virus enters the blood (viremia)
day 4-6
Activated immune response
No current test
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Is it possible to distinguish between different infections using the early host immune response?
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1000 PFU via aerosol exposure (Caballero et al., 2014)
Days post-infection 0 3 6 10
Number of samples 4 4 2 2
Clinical symptoms None Fever Severe Severe
Lassa sequencing dataset
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Marburg sequencing datasetDays post-infection 0 3 5 9
Number of samples 3 3 3 3
Clinical symptoms None Fever Severe Severe1000 PFU via aerosol exposure (Caballero et al., 2014)
1000 PFU via aerosol exposure (Caballero et al., 2014)
Days post-infection 0 3 6 10
Number of samples 4 4 2 2
Clinical symptoms None Fever Severe Severe
Lassa sequencing dataset
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Most frequent gene expression patterns
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Most frequent gene expression patterns
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Most frequent gene expression patterns
-
Most frequent gene expression patterns
-
Most frequent gene expression patterns
-
Most frequent gene expression patterns
-
Most frequent gene expression patterns
-
Most frequent gene expression patterns
-
Most frequent gene expression patterns
-
Most frequent gene expression patterns
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Most frequent gene expression patterns
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Most frequent gene expression patterns
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Ebola early transcriptional response
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Common early transcriptional response
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Fold
Ch
ange
Significance (FDR)
Lassa
Fold
Ch
ange
Marburg
The interferon response is activated as early as 3 days post-infection
Significance (FDR)
-
Fold
Ch
ange
Significance (FDR)
Lassa
Fold
Ch
ange
Marburg
The interferon response is activated as early as 3 days post-infection
Significance (FDR)
-
Fold
Ch
ange
Significance (FDR)
Lassa
Fold
Ch
ange
Marburg
Interferon Stimulated
Other
The interferon response is activated as early as 3 days post-infection
Significance (FDR)
-
Fold
Ch
ange
Significance (FDR)
Lassa
Fold
Ch
ange
Marburg
Interferon Stimulated
Other
The interferon response is activated as early as 3 days post-infection
Significance (FDR)
ISG15OAS1
MX1
DHX58
IFIT2
HERC5
-
Fold
Ch
ange
Significance (FDR)
Lassa
Fold
Ch
ange
Marburg
Interferon Stimulated
Other
The interferon response is activated as early as 3 days post-infection
Significance (FDR)
ISG15OAS1
MX1
DHX58
IFIT2
HERC5 MX1
ISG15OAS1
DHX58
IFIT2
HERC5
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Immune markers of infection
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Immune markers of infection
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Fold
cha
nge virus
lassamarburgebola_k
Fold
Ch
ange
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Fold
cha
nge virus
lassamarburgebola_k
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Fold
cha
nge virus
lassamarburgebola_k
10
5
10
5
10
5
-
12 Early Samples
What are the most informative genes?
-
12 Early Samples Marburg
Lassa
Uninfected
What are the most informative genes?
-
12 biomarker genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_nameFo
ld c
hang
e viruslassamarburgebola_k12 Early
Samples Marburg
Lassa
Uninfected
What are the most informative genes?
-
Can these genes classify blind samples?
66 Blind Samples
12 biomarker genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_nameFo
ld c
hang
e viruslassamarburgebola_k12 Early
Samples Marburg
Lassa
Uninfected
What are the most informative genes?
-
Can these genes classify blind samples?
66 Blind Samples
12 biomarker genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_nameFo
ld c
hang
e viruslassamarburgebola_k12 Early
Samples
12 biomarker genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Fold
cha
nge virus
lassamarburgebola_k
Marburg
Lassa
Uninfected
What are the most informative genes?
-
Can these genes classify blind samples?
66 Blind Samples
12 biomarker genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_nameFo
ld c
hang
e viruslassamarburgebola_k12 Early
Samples
12 biomarker genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Fold
cha
nge virus
lassamarburgebola_k
Marburg
Lassa
Uninfected
Marburg
Lassa
Uninfected
What are the most informative genes?
-
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
Each dot represents one of the 66 blind samples
Principal Component Analysis (An instruction manual)
-
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
Samples exist in 12-dimensional space (one dimension per gene)
Each dot represents one of the 66 blind samples
Principal Component Analysis (An instruction manual)
-
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
Samples exist in 12-dimensional space (one dimension per gene)
Each dot represents one of the 66 blind samples
PCA rotates the 66 samples in 12-dimensional space and returns the configuration with the clearest clusters
Principal Component Analysis (An instruction manual)
-
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
Samples exist in 12-dimensional space (one dimension per gene)
Each dot represents one of the 66 blind samples
I only plot the two dimensions that have the tightest clusters
PCA rotates the 66 samples in 12-dimensional space and returns the configuration with the clearest clusters
Principal Component Analysis (An instruction manual)
-
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
Samples exist in 12-dimensional space (one dimension per gene)
Each dot represents one of the 66 blind samples
I only plot the two dimensions that have the tightest clusters
PCA rotates the 66 samples in 12-dimensional space and returns the configuration with the clearest clusters
Principal Component Analysis (An instruction manual)
-
Biomarker genes are useful predictors of infection
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
-
Uninfected samples
Biomarker genes are useful predictors of infection
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
-
Marburg-infected samples
Uninfected samples
Biomarker genes are useful predictors of infection
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
-
Marburg-infected samples
Lassa-infected samples
Uninfected samples
Biomarker genes are useful predictors of infection
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
-
Marburg-infected samples
Lassa-infected samples
Uninfected samples
Biomarker genes are useful predictors of infection
Sequencing samples used to select the 12 biomarkers
Dimension 1 (80.79% Variance)
Dim
ensi
on 2
(9.
68%
Var
ian
ce)
-
Randomly picked genes are not useful predictors of infection
-
Days post-infection 0 3 6 10
Number of samples 4 4 2 2
Clinical symptoms None Fever Severe Severe
Lassa sequencing dataset
1000 PFU via aerosol exposure
-
Days post-infection 0 3 6 10
Number of samples 4 4 2 2
Clinical symptoms None Fever Severe Severe
Lassa sequencing dataset
1000 PFU via aerosol exposure
Independent Lassa microarray datasetDays post-infection 1 4 6 7 10
Number of samples 3 3 3 3
Clinical symptoms None None Fever Mild Severe10,000 TCID50 via intramuscular injection (Barrenas et al., 2015)
-
Validation in an independent arenavirus dataset
-
Validation in an independent arenavirus dataset
-
Validation in an independent arenavirus dataset
-
Conclusions from Part II
Viral hemorrhagic fever infection causes strong transcriptional changes in circulating immune cells
-
Conclusions from Part II
Viral hemorrhagic fever infection causes strong transcriptional changes in circulating immune cells
These changes are among the earliest signals of infection that we can detect
-
Conclusions from Part II
Viral hemorrhagic fever infection causes strong transcriptional changes in circulating immune cells
These changes are among the earliest signals of infection that we can detect
A subset of these changes are good discriminators of early stage infections
-
General Conclusions
Studying the host immune response to infection can provide novel insights into the molecular mechanisms that underlie pathogenesis
-
General Conclusions
Studying the host immune response to infection can provide novel insights into the molecular mechanisms that underlie pathogenesis
This approach can facilitate the development of diagnostics, therapeutics and vaccines for viral hemorrhagic fevers and other diseases
-
Future directions
Compare the patterns of macaque PBMC samples with those of human blood samples
-
Future directions
Compare the patterns of macaque PBMC samples with those of human blood samples
Understand the proteomic component of the host immune response
-
Future directions
Expand the analysis to include additional diseases like malaria and dengue fever
Compare the patterns of macaque PBMC samples with those of human blood samples
Understand the proteomic component of the host immune response
-
John Connor John Ruedas Erik Carter
Emily Speranza Kristen Peters
Jake Awtry Michelle Olsen
Acknowledgements
-
John Connor John Ruedas Erik Carter
Emily Speranza Kristen Peters
Jake Awtry Michelle Olsen
Ron Corley Tom Kepler
Evan Johnson Luis Carvalho
Acknowledgements
-
John Connor John Ruedas Erik Carter
Emily Speranza Kristen Peters
Jake Awtry Michelle Olsen
Ron Corley Tom Kepler
Evan Johnson Luis Carvalho
Acknowledgements
Judy Yen Claire Marie Filone
-
John Connor John Ruedas Erik Carter
Emily Speranza Kristen Peters
Jake Awtry Michelle Olsen
Ron Corley Tom Kepler
Evan Johnson Luis Carvalho
Acknowledgements
Judy Yen Claire Marie Filone
USAMRIID Anna Honko Arthur Goff Lisa Hensley
Whitehead Kate Rubins
-
John Connor John Ruedas Erik Carter
Emily Speranza Kristen Peters
Jake Awtry Michelle Olsen
Ron Corley Tom Kepler
Evan Johnson Luis Carvalho
Bioinformatics Program Department of Microbiology
Fulbright Comission Pasteur Institute French Guiana
Acknowledgements
Judy Yen Claire Marie Filone
USAMRIID Anna Honko Arthur Goff Lisa Hensley
Whitehead Kate Rubins