Using the Host Immune Response to Hemorrhagic Fever Viruses to Understand Pathogenesis and Improve Diagnostics

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

Understanding the host immune response to Ebola virus infection

Part I

Part IIDistinguishing between hemorrhagic fevers

by using the host immune response


Part I

Why focus on the host immune response?


1. Hemorrhagic fever symptoms are likely caused by a dysregulated host 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

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)


MarburgEbola(pre-2014)


MarburgEbola(post-2014)


MarburgEbola(post-2014)

Lassa

Hemorrhagic fevers share similar symptoms

Ebola & Marburg

Lassa

Early

Headache

Malaise

Fever

Malaise

Sore throat

Fever

Weakness

Weakness

Headache


Ebola & Marburg

Lassa

Early

Headache

Malaise

Fever

Malaise

Sore throat

Fever

Weakness

Weakness

Headache

Vomiting

Diarrhea

Middle

Diarrhea

Joint pain

Rash

Pharyngitis

Pharyngitis

Vomiting


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

Reasons to study hemorrhagic fever viruses

1. Difficult to diagnose during the early stages


2. High mortality rates



3. Lack of treatments and vaccines




3. Lack of treatments and vaccines



4. Potential to be used as bioweapons




Part I

We use animal models to study the immune response

Macaque


Macaque

Blood

Ebola virus infection


Immune Cells

Centrifugation

Macaque

Blood



Immune Cell RNA

RNA Extraction

Immune Cells

Centrifugation

Macaque

Blood



Immune Cell RNA

RNA Extraction

Immune Cells

Centrifugation

Macaque

Blood


at BSL-4 at BU


Immune Cell RNA

RNA Extraction

Immune Cells

Centrifugation

Macaque

Blood


at BSL-4 at BU

Sequenced Reads

RNA Sequencing


Immune Cell RNA

RNA Extraction

GENE

Alignment & Quantification

Gene Expression Levels

Immune Cells

Centrifugation

Macaque

Blood


at BSL-4 at BU

Sequenced Reads

RNA Sequencing

Sequencing files

Bioinformatics pipeline

Sequencing files

Macaque Transcriptome+Tophat

Aligned Reads


Sequencing files

Macaque Transcriptome+Tophat

Aligned Reads

Raw Counts

Feature Counts


Sequencing files

Macaque Transcriptome+

Normalized Counts

Trimmed Mean of M-values

(edgeR)

Tophat

Aligned Reads

Raw Counts

Feature Counts


Sequencing files


Normalized Counts


(edgeR)

Design Matrix

+Tophat

Aligned Reads

Raw Counts

Feature Counts


Sequencing files


Normalized Counts


(edgeR)

Design Matrix

+Fold changes and p-values

Negative Binomial GLM

Empirical Bayes (edgeR)

+

Tophat

Aligned Reads

Raw Counts

Feature Counts


Fold changes and p-values

Sequencing files


Normalized Counts


(edgeR)

Design Matrix

+

Negative Binomial GLM

Empirical Bayes (edgeR)

+

Tophat

Aligned Reads

Raw Counts

Feature Counts


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)

Days post-infection 0 4 7



Ebola sequencing dataset

Ebola (vaccinated) sequencing datasetDays post-infection 0 4 7


Clinical symptoms None None None1000 FFU via intramuscular injection (Barrenas et al., 2015)


What are the gene expression changes that we would expect to see

during Ebola infection?

Interferon acts as an alarm signal warning neighbouring cells about a detected immune threat

Virus particles

DNANucleus

Cell membrane


Virus particles

viral RNA

DNANucleus

Cell membrane


Virus particles

viral RNA

MDA5/RIG-I

detection

DNANucleus

Cell membrane


Virus particles

viral RNA

MDA5/RIG-I

detection

DNANucleus

PIRF3

Cell membrane


Virus particles

viral RNA

MDA5/RIG-I

detection

DNANucleus

PIRF3

P PIRF3 IRF3

Cell membrane


Virus particles

viral RNA

MDA5/RIG-I

detection

DNANucleus

PIRF3

P PIRF3 IRF3

P

NFKBIKBa

Cell membrane


Virus particles

viral RNA

MDA5/RIG-I

detection

DNANucleus

PIRF3

P PIRF3 IRF3 NFKB

P

NFKBIKBa

Cell membrane

Interferon Beta


Virus particles

viral RNA

MDA5/RIG-I

detection

DNANucleus

PIRF3

P PIRF3 IRF3 NFKB

P

NFKBIKBa

Cell membrane

Interferon Beta


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

Nucleus

Cell membrane

InterferonInterferon receptor


STAT1STAT2

Nucleus

Cell membrane



STAT1STAT2

Interferon-stimulated response element Nucleus

Cell membrane



Interferon stimulated genes

STAT1STAT2


Cell membrane



Interferon stimulated genes

STAT1STAT2


Cell membrane


MX1IFIT1ISG15

OAS1HERC5


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 virions

viral ssRNA

MDA5/RIG-I

sensing

DNANucleus

PIRF3

Interferon Beta

Interferon

P PIRF3 IRF3 NFKB

P

NFKBIKBa

Cell membrane

eVP35


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

Interferon-stimulated genes in vaccinated animals dont become highly expressed after infection

The majority of genes in vaccinated animals dont change their expression throughout infection

A subset of important immune genes becomes highly expressed in vaccinated animals

Hypothesis 1: An unknown mechanism allows infected cells to stimulate interferon production

Hypothesis 2: Non-productively infected cells can still produce interferon


Ebola intramuscular datasetDays post-infection 0 4 7




Ebola intramuscular datasetDays post-infection 0 4 7



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

Different routes of infection lead to comparable immune responses

Ebola infection leads to an early and strong induction of interferon stimulated genes

Conclusions from Part I


Circulating immune cells dont show this pattern of activation in vaccinated animals



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





Part I

Current diagnostic methods require the presence of the virus in the blood

Time

Viral Infection

day 0


Time

Viral Infection

day 0

Initial symptoms

day 2-4


Time

Viral Infection

day 0

Initial symptoms

day 2-4

Virus enters the blood (viremia)

day 4-6


RT-PCR diagnostic becomes effective

Time

Viral Infection

day 0

Initial symptoms

day 2-4


day 4-6


RT-PCR diagnostic becomes effective

Time

Viral Infection

day 0

Initial symptoms

day 2-4


day 4-6

Activated immune response

No current test

Is it possible to distinguish between different infections using the early host immune response?

1000 PFU via aerosol exposure (Caballero et al., 2014)

Days post-infection 0 3 6 10


Clinical symptoms None Fever Severe Severe

Lassa sequencing dataset

Marburg sequencing datasetDays post-infection 0 3 5 9


Clinical symptoms None Fever Severe Severe1000 PFU via aerosol exposure (Caballero et al., 2014)

1000 PFU via aerosol exposure (Caballero et al., 2014)





Most frequent gene expression patterns

Ebola early transcriptional response

Common early transcriptional response

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


Significance (FDR)

Fold

Ch

ange

Significance (FDR)

Lassa

Fold

Ch

ange

Marburg


Other


Significance (FDR)

ISG15OAS1

MX1

DHX58

IFIT2

HERC5

Fold

Ch

ange

Significance (FDR)

Lassa

Fold

Ch

ange

Marburg


Other


Significance (FDR)

ISG15OAS1

MX1

DHX58

IFIT2

HERC5 MX1

ISG15OAS1

DHX58

IFIT2

HERC5

Immune markers of infection

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


0

10

100

gene_name

Fold

cha

nge virus

lassamarburgebola_k


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


12 biomarker genes


0

10

100

gene_nameFo

ld c

hang

e viruslassamarburgebola_k12 Early

Samples Marburg

Lassa

Uninfected


Can these genes classify blind samples?

66 Blind Samples

12 biomarker genes


0

10

100

gene_nameFo

ld c

hang


Samples Marburg

Lassa

Uninfected



66 Blind Samples

12 biomarker genes


0

10

100

gene_nameFo

ld c

hang


Samples

12 biomarker genes


0

10

100

gene_name

Fold

cha

nge virus

lassamarburgebola_k

Marburg

Lassa

Uninfected



66 Blind Samples

12 biomarker genes


0

10

100

gene_nameFo

ld c

hang


Samples

12 biomarker genes


0

10

100

gene_name

Fold

cha

nge virus

lassamarburgebola_k

Marburg

Lassa

Uninfected

Marburg

Lassa

Uninfected


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)


Dim

ensi

on 2

(9.

68%

Var

ian

ce)

Samples exist in 12-dimensional space (one dimension per gene)




Dim

ensi

on 2

(9.

68%

Var

ian

ce)



PCA rotates the 66 samples in 12-dimensional space and returns the configuration with the clearest clusters



Dim

ensi

on 2

(9.

68%

Var

ian

ce)



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


Biomarker genes are useful predictors of infection


Dim

ensi

on 2

(9.

68%

Var

ian

ce)

Uninfected samples



Dim

ensi

on 2

(9.

68%

Var

ian

ce)

Marburg-infected samples

Uninfected samples



Dim

ensi

on 2

(9.

68%

Var

ian

ce)


Lassa-infected samples

Uninfected samples



Dim

ensi

on 2

(9.

68%

Var

ian

ce)


Lassa-infected samples

Uninfected samples


Sequencing samples used to select the 12 biomarkers


Dim

ensi

on 2

(9.

68%

Var

ian

ce)

Randomly picked genes are not useful predictors of infection





1000 PFU via aerosol exposure





1000 PFU via aerosol exposure

Independent Lassa microarray datasetDays post-infection 1 4 6 7 10


Clinical symptoms None None Fever Mild Severe10,000 TCID50 via intramuscular injection (Barrenas et al., 2015)

Validation in an independent arenavirus dataset

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



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


Understand the proteomic component of the host immune response

Future directions

Expand the analysis to include additional diseases like malaria and dengue fever


Understand the proteomic component of the host immune response

John Connor John Ruedas Erik Carter

Emily Speranza Kristen Peters

Jake Awtry Michelle Olsen

Acknowledgements




Ron Corley Tom Kepler

Evan Johnson Luis Carvalho

Acknowledgements






Acknowledgements

Judy Yen Claire Marie Filone






Acknowledgements


USAMRIID Anna Honko Arthur Goff Lisa Hensley

Whitehead Kate Rubins






Bioinformatics Program Department of Microbiology

Fulbright Comission Pasteur Institute French Guiana

Acknowledgements


USAMRIID Anna Honko Arthur Goff Lisa Hensley

Whitehead Kate Rubins

Science

Using the Host Immune Response to Hemorrhagic Fever Viruses to Understand Pathogenesis and Improve Diagnostics