Human & Veterinary Respiratory Physilogy_DR.E.Muralinath_Associate Professor....
Using the Host Immune Response to Hemorrhagic Fever Viruses to Understand Pathogenesis and Improve Diagnostics
1. Ignacio S. Caballero
bioinformatics graduate program
Boston University
Using the Host Immune Response
To Hemorrhagic Fever Viruses
To Understand Pathogenesis
and Improve Diagnostics
Connor Lab
National Emerging Infectious Diseases Laboratories
3. Part II
Distinguishing between hemorrhagic fevers
by using the host immune response
Understanding the host immune response
to Ebola virus infection
Part I
5. Why focus on the host immune response?
1. Hemorrhagic fever symptoms are likely
caused by a dysregulated host response
6. Why focus on the host immune response?
2. We can measure the activity of the players
but we don’t understand a lot of the rules
1. Hemorrhagic fever symptoms are likely
caused by a dysregulated host response
16. Reasons to study hemorrhagic fever viruses
1. Difficult to diagnose during the early stages
17. Reasons to study hemorrhagic fever viruses
2. High mortality rates
1. Difficult to diagnose during the early stages
18. Reasons to study hemorrhagic fever viruses
3. Lack of treatments and vaccines
2. High mortality rates
1. Difficult to diagnose during the early stages
19. 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
20. Part II
Distinguishing between hemorrhagic fevers
by using the host immune response
Understanding the host immune response
to Ebola virus infection
Part I
21. We use animal models
to study the immune response
Macaque
22. We use animal models
to study the immune response
Macaque
Blood
Ebola virus
infection
23. We use animal models
to study the immune response
Immune
Cells
Centrifugation
Macaque
Blood
Ebola virus
infection
24. We use animal models
to study the immune response
Immune
Cell RNA
RNA
Extraction
Immune
Cells
Centrifugation
Macaque
Blood
Ebola virus
infection
25. 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
26. 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
27. 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
35. 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)
36. 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)
37. 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)
38. Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
Ebola sequencing dataset
Ebola (vaccinated) sequencing dataset
Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None None None
1000 FFU via intramuscular injection (Barrenas et al., 2015)
1000 FFU via intramuscular injection (Barrenas et al., 2015)
42. What are the gene expression changes
that we would expect to see
during Ebola infection?
43. Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
DNA
Nucleus
Cell membrane
44. Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
DNA
Nucleus
Cell membrane
45. Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
MDA5/RIG-I
detection
DNA
Nucleus
Cell membrane
46. Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
MDA5/RIG-I
detection
DNA
Nucleus
P
IRF3
Cell membrane
47. Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
MDA5/RIG-I
detection
DNA
Nucleus
P
IRF3
P P
IRF3 IRF3
Cell membrane
48. Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
MDA5/RIG-I
detection
DNA
Nucleus
P
IRF3
P P
IRF3 IRF3
P
NFKB
IKBa
Cell membrane
49. Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
MDA5/RIG-I
detection
DNA
Nucleus
P
IRF3
P P
IRF3 IRF3 NFKB
P
NFKB
IKBa
Cell membrane
50. Interferon
Beta
Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
MDA5/RIG-I
detection
DNA
Nucleus
P
IRF3
P P
IRF3 IRF3 NFKB
P
NFKB
IKBa
Cell membrane
51. Interferon
Beta
Interferon acts as an alarm signal warning
neighbouring cells about a detected immune threat
Virus
particles
viral
RNA
MDA5/RIG-I
detection
DNA
Nucleus
Interferon
P
IRF3
P P
IRF3 IRF3 NFKB
P
NFKB
IKBa
Cell membrane
60. Ebola virus contains a protein that inhibits
the production of interferon
Ebola
virions
viral
ssRNA
MDA5/RIG-I
sensing
DNA
Nucleus
P
IRF3
Interferon
Beta
Interferon
P P
IRF3 IRF3 NFKB
P
NFKB
IKBa
Cell membrane
61. Ebola virus contains a protein that inhibits
the production of interferon
Ebola
virions
viral
ssRNA
MDA5/RIG-I
sensing
DNA
Nucleus
P
IRF3
Interferon
Beta
Interferon
P P
IRF3 IRF3 NFKB
P
NFKB
IKBa
Cell membrane
eVP35
62. Ebola virus contains a protein that inhibits
the production of interferon
Ebola
virions
viral
ssRNA
MDA5/RIG-I
sensing
DNA
Nucleus
P
IRF3
P
NFKB
IKBa
Cell membrane
eVP35
63. Ebola infection leads to an early increase in the
expression of interferon-stimulated genes
69. 1000 FFU via intramuscular injection (Barrenas et al., 2015)
Ebola intramuscular dataset
Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
70. 1000 FFU via intramuscular injection (Barrenas et al., 2015)
Ebola intramuscular dataset
Days post-infection 0 4 7
Number of samples 3 3 3
Clinical symptoms None Fever Severe
Ebola aerosol dataset
Days post-infection 0 3 6 8
Number of samples 4 3 3 2
Clinical symptoms None Fever Severe Severe
1000 PFU via aerosol exposure
73. Ebola infection leads to an early and strong
induction of interferon stimulated genes
Conclusions from Part I
74. Ebola infection leads to an early and strong
induction of interferon stimulated genes
Circulating immune cells don’t show this
pattern of activation in vaccinated animals
Conclusions from Part I
75. Ebola infection leads to an early and strong
induction of interferon stimulated genes
Circulating immune cells don’t 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
76. Part II
Distinguishing between hemorrhagic fevers
by using the host immune response
Understanding the host immune response
to Ebola virus infection
Part I
78. Current diagnostic methods require
the presence of the virus in the blood
Time
Viral
Infection
day 0
Initial
symptoms
day 2-4
79. 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
80. 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
81. 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
82. Is it possible to distinguish
between different infections using
the early host immune response?
83. 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
84. Marburg sequencing dataset
Days post-infection 0 3 5 9
Number of samples 3 3 3 3
Clinical symptoms None Fever Severe Severe
1000 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
108. 12
biomarker
genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Foldchange
virus
lassa
marburg
ebola_k12 Early
Samples Marburg
Lassa
Uninfected
What are the most informative genes?
109. Can these genes classify blind samples?
66 Blind
Samples
12
biomarker
genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Foldchange
virus
lassa
marburg
ebola_k12 Early
Samples Marburg
Lassa
Uninfected
What are the most informative genes?
110. Can these genes classify blind samples?
66 Blind
Samples
12
biomarker
genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Foldchange
virus
lassa
marburg
ebola_k12 Early
Samples
12
biomarker
genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Foldchange
virus
lassa
marburg
ebola_k
Marburg
Lassa
Uninfected
What are the most informative genes?
111. Can these genes classify blind samples?
66 Blind
Samples
12
biomarker
genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Foldchange
virus
lassa
marburg
ebola_k12 Early
Samples
12
biomarker
genes
MX1 SIGLEC1 SAMD4A HSPA1B HSPA1L MMP8 ADAM28
0
10
100
gene_name
Foldchange
virus
lassa
marburg
ebola_k
Marburg
Lassa
Uninfected
Marburg
Lassa
Uninfected
What are the most informative genes?
112. Dimension 1 (80.79% Variance)
Dimension2(9.68%Variance)
Each dot represents one of
the 66 blind samples
Principal Component Analysis
(An instruction manual)
113. Dimension 1 (80.79% Variance)
Dimension2(9.68%Variance)
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)
114. Dimension 1 (80.79% Variance)
Dimension2(9.68%Variance)
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)
115. Dimension 1 (80.79% Variance)
Dimension2(9.68%Variance)
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)
116. Dimension 1 (80.79% Variance)
Dimension2(9.68%Variance)
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)
117. Biomarker genes are
useful predictors of infection
Dimension 1 (80.79% Variance)
Dimension2(9.68%Variance)
123. 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
124. 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 dataset
Days post-infection 1 4 6 7 10
Number of samples 3 3 3 3
Clinical symptoms None None Fever Mild Severe
10,000 TCID50 via intramuscular injection (Barrenas et al., 2015)
128. Conclusions from Part II
Viral hemorrhagic fever infection causes strong
transcriptional changes in circulating immune cells
129. 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
130. 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
131. General Conclusions
Studying the host immune response to
infection can provide novel insights into
the molecular mechanisms that underlie
pathogenesis
132. 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
134. Future directions
Compare the patterns of macaque PBMC
samples with those of human blood samples
Understand the proteomic component of
the host immune response
135. 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
137. John Connor
John Ruedas
Erik Carter
Emily Speranza
Kristen Peters
Jake Awtry
Michelle Olsen
Ron Corley
Tom Kepler
Evan Johnson
Luis Carvalho
Acknowledgements
138. 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
139. 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
140. 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