This slideset explains the ‘Omics’ technology and its role in the study of infections and vaccination. It is a revolution as it offers powerful tools to interrogate the animal / human immune response to vaccines and infections.
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The 'omics' revolution: How will it improve our understanding of infections and vaccines in the future? - Slideset by Professor Katie Flanagan
1. Assoc. Professor Katie Flanagan
Head of Infectious Diseases, Launceston General Hospital,Tasmania
Clinical Associate Professor, University ofTasmania
Adjunct Senior Lecturer, Monash University
The ‘omics’ revolution:
How will it improve our understanding of
infections and vaccines in the future?
3. HumanGenome
The ‘omics’ era began with the completion of the human genome project
in April 2003
Our entire genetic blueprint was characterised in an international
collaborative effort (3 billion base pairs)
5. Transcriptomics
Simultaneous unbiased interrogation of expression of the entire human
genome
Provides a “snapshot” of the entire RNA response and all its
coordinated immunological pathways
A very powerful tool for studying responses to vaccines and infections
DNA Microarray
Technology
Most widely used technology,
cheaper than sequencing
Microscopic DNA spots of
defined sequence attached to a
solid surface (eg Affymetrix) or
tagged to beads on glass slides
(eg Illumina) that can be used to
interrogate RNA samples
More expensive but likely to replace
microarray technology
Various methods but RNA-seq is latest
technology and covers far more sequence
Can analyse the RNA sequence at single
cell level
Allowing new discoveries at cell level
Novel cell types identified
Sequencing
6. Data mining tools are becoming more sophisticated and able to handle the
complex data generated in these studies allowing functional analysis of
immune response pathways
RNA events are not independent but represent a coordinated response
Interrogation of the biology / pathways can be done using proprietary and
open sources and bioinformatics tools eg DAVID, Onto-Express, KEGG, GO,
STRING, Bioconductor platform for R, Ingenuity Pathway Analysis
This requires a bioinformaticist and a lot of time
Must allow for multiple testing using false discovery rate modifications
Analysis pipeline key to the quality of the results
Still not been widely used in the fields of infectious diseases and vaccines
despite becoming cheaper and more accessible
Data Analysis
8. Seminal paper demonstrating that this technology can
be used to predict vaccine efficacy
2 trials with n=15 and n=10 subjects (PBMC)
65 differentially expressed genes common to both groups
Mainly innate immune response genes upregulated
regulators of innate sensing and type 1 IFN production
Gene signature identified that correlated with and predicted CD8+T cell responses
with up to 90% accuracy
Another signature predicted the neutralising Ab response with up to 90% accuracy
9. Immune Response to MeaslesVaccine
Flanagan et al., in preparation
Baseline 1 week 2 weeks 4 weeks 6 weeks
10. Gene Pathways Altered Post MeaslesVaccine
One week after MV
Innate immune response genes predominantly upregulated including RIG-
I-like receptor signalling pathway,Toll-like receptor (TLR) signalling
pathway
IFN induced genes, IRF-7,TLR7
Flanagan et al., in preparation
96 clusters of genes upregulated 1 week
after MV. All relationships with Pearson
correlation >0.75 shown. Clusters
indicated by different colours.
Six Weekes After MV
No innate genes/pathways represented
Upregulated pathways for regulation of adaptive immunity, αβT cell
activation & proliferation, T cell mediated cytotoxicity
Upregulated TGF-β signalling, NK cell cytotoxicity, oxidative
phosphorylation
11. From Klein et al, Lancet Infect Dis 2010; 10: 338
Original paper: 594 genes
differentially expressed between day
0 & 21 afterYF (17D) vaccination
(Querec et al, Nat Imm 2009)
Re-analysis by sex 660 genes
differentially expressed in women and
67 in men.Women had more
upregulatedTLR-associated genes
that activate the IFN pathway postYF
Yellow FeverVaccine
Transcriptome Profile Sex Differences
12. Underpowered to analyse by sex so not possible to draw conclusions about
specific genes / pathways
30 of 84 sex comparisons (36%) had significant loci at stringent adjusted
p<0.0001 representing 388 array features
– 21 on X orY chromosomes
– 367 autosomal
There was no overlap between the 75 array features identified in the
32 sex-independent comparisons and the 367 identified in the
84 sex-dependent comparisons
Females differentially expressed many more genes than males
Strongly supports marked sex differences in RNA response to MV
MeaslesVaccine Study Analysis by Sex
Flanagan et al., in preparation
13. Females Males
Diphtheria-tetanus-whole cell pertussis (DTwP)
Vaccinated 9 month old Gambians
Vaccinated at 9 months and bled on day of DTP and 4 weeks later
No differential expression seen unless groups separated by sex
Females have many more differentially expressed genes but mostly
down-regulated
Males have less but most upregulated
Flanagan et al., in preparation
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14. Systems analysis of responses to
meningococcal (MPSV4, MCV4),YF and
influenza vaccines (LAIV,TIV)
To see if a ‘universal signature’ could predict Ab
response to immunisation
Analysis by enrichment of differentially
expressed genes by
Interactome (collection of gene-gene interactions) and
bibliome (pairs of genes assoc to publications in
PubMed) analysis
Blood transcription modules
Constructed from public transcriptome data from
healthy humans
Molecular signatures of antibody responses derived
from a systems biological study of 5 human vaccines
S. Li et al. Nat Immunol 2014; 15(2): 195-204
15. BTM analysis showed distinct mechanisms for
Ab responses to the different vaccines
(a) Modules common between vaccines linked
by a coloured curve in the centre.
(b) Heat maps showing relationships between
gene modules andAb response
Conclude that the different types of vaccines
have different mechanisms of Ab induction
Even 2 different molecular mechanisms for
different components of the same vaccine
Molecular signatures of antibody responses derived
from a systems biological study of 5 human vaccines
S. Li et al. Nat Immunol 2014; 15(2): 195-204
17. Application of transcriptomic studies to
human infectious diseases
Animal challenge models with human pathogens to interrogate RNA responses
(bacteria, viruses, parasites)
Can be used to model predictors of pathogenicity and then validated in infected
humans e.g. sepsis models
In vitro work with human cells or tissues to predict response to infection (bacteria,
viruses, parasites)
Transcription profiling of the pathogens themselves (bacteria, viruses, parasites)
including during biofilm formation
Studies now emerging of naturally infected humans:
Childhood TB – RNA signature that could predict TB from other infections in African children
(Anderson et al, NEJM 2014; 370(18):1712-23)
H7N9 specific signatures (Mei et al, Gene 2014; 551(2): 255-60).
Malaria infected patients – clinical correlates with expression of certain genes (parasite profiled in same
study) (Yamagishi et al, Genome Res 2014; 24(9): 1433-44)
Profiling in HIV LTNPs identified candidate genes associated with lack of progression
(Luque et al, Mol Immunol 2014; 62(1): 63-70)
Profiling in chronic active EBV infected patients (Murakami et al, Microbes Infect 2014; 16(7): 581-6)
HepC - set of genes that predict recurrent infection after Rx (Hou et al. JVirol 2014; 88(21): 12254-64)
Thus this methodology offers diagnostic and prognostic potential
18. Hierarchical clustering analysis
Whole blood RNA analysis at first
signs of clinical infection (n=121
cases and controls)
Found an invariant 52-gene cluster
that predicts bacterial, but not
viral, infection with high accuracy
Cluster consisted of innate,
metabolic and adaptive immune
pathways could identify bacterially,
but not virally, infected neonates.
Found a link with gut microbiota
anti-inflammatory regulators
19. NeonatalTranscriptome Response to Sepsis
A. Network relationships
visualised with Cytoscape show
genes upregulated in neonatal
sepsis (in red) & all interacting
molecules (in grey)
B & C. Top hub nodes in red
A B C
D
New insights into homeostatic control mechanisms in neonatal sepsis
Diagnostic and prognostic implications for this technology
D.Visualisation of networksusing
Biolayout Express 3D. 3 groups of
genes identified corresponding to
those identified in hierarchical
clustering.Co-expressed genes
identified and visualised. 12
clusters were patient specific for
bacterial infection.
21. Proteomics
High throughput highly sensitive analysis of all proteins in any biological
sample – blood, plasma, body fluid, cell cultures
Methodology includes
Gel electrophoresis – older methodology
Mass spectrometry – several different types – particularly useful for biomarker studies
Reverse phase protein array
Multiple web resources for analysis and published standards for reporting
Used to study multiple
infections including HIV,
malaria, TB, measles and
hepatitis
Identify new vaccine antibody
targets
Analyse the immune response
to vaccination
22. Pathogen
Diagnosis MALDI-TOF for diagnosis of
multiple organisms in clinical isolates –
the technology is evolving with
enormous future potential
Virulence factors Classical studies for
virulence factors have analysed for
single substances. Proteomics can
analyse every protein in a sample.
Pathogenesis Compare proteome of
isolates of differing pathogenicity, or
when cultured in different conditions
NB For pathogens grown in host cells
the bacterial proteins need to be
isolated first
Prognostic biomarkers (e.g.
associated with death)
Sepsis (DeCoux et al, Crit Care Med
2015 Epub)
Therapeutic targets (e.g. those
associated with survival or less
severe infection)
Diagnostic markers
Clinical diagnosis e.g. UTIs (Yu et al,
JTransl Med 2015; 13:111)
Virulence factors
Host
24. Microbiomics
The collective genomes of the entire ecosystem of bacteria, viruses, fungi
and other microbes that an organism carries.
Humans are complete ecosystems consisting of trillions of microbes
There are more microbial genomes in us than human cells (100 trillion microbial
cells in human body = several kilos)
Multiple unculturable micro-organisms can be sequenced
Ribosomal RNA sequencing
Amplify 16S ribosomal RNA which is highly conserved and acts as a proxy
for the number of species in the sample. Can ignore host DNA. Well
established databases of rRNA sequences.
Shotgun or metagenomic sequencing
Sequence short random pieces of all genomes which are then pieced
together.
Long sequences are better than short ones e.g. 300, 600, 800 base pairs
25. Microbiomics
Inherent biases (nature of the biological sample is critical to obtain useful
results)
Exposure to O2 eliminates obligate anaerobes
Sequencing DNA ignores RNA viruses
Gentle extraction may not lyse more durable organisms
Still not clear what constitutes a healthy microbiome
The microbiome has multiple effects on innate and adaptive immunity
Thought to play a critical role in maintaining health and inducing disease
26. Microbiomics
Human Microbiome Project commenced 2007 and had published >350 papers
All antibiotics alter the human microbiome
Loss of diversity correlates with disease
Disordered microbiome linked with DM, obesity, inflammatory bowel diseases,
C. diff, colorectal cancer, chronic fatigue, metabolic syndrome, MS, rheumatoid
arthritis – but not known if is a cause or an effect
Microbiome may affect vaccine responses e.g. in settings with malnutrition and
poor diet the microbiome may cause poorer vaccine efficacy
Vaccines may affect the microbiome
Human microbiome can be altered / manipulated:
Prebiotics – fermented substance that alters microbiome e.g. lactulose / inulin
Probiotics – live bacteria
Faecal microbiotatransplant
27. Faecal MicrobiotaTransplant
4th Century AD Bedouins used camel faeces to treat diarrhoea
Donor screening essential – single donor, multiple donors, ‘stool banks’,
autologous faecal transplant
Used successfully to treat CDI and ABx associated diarrhoea
Also used to treat IBS, cause remission of UC, treat metabolic and
cardiovascular diseases, allergy, chronic fatigue
Large scale RCTs are lacking
Seems safe but long term effects unknown
Regulatory aspects not yet clear – classified as a drug in USA, not in
Europe / Australia
29. Metabolome
The complete set of small molecule chemicals in a biological sample
Endogenous – belonging to the system
Primary – directly involved in growth, development, reproduction
Secondary – not involved e.g. waste, pigments
Exogenous – toxins, food additives, drugs
Can be measured by spectroscopy or spectrometry (as with proteome)
Changes dramatically in minutes / seconds
Human Metabolome Database (HMDB) – open access freely available
>40,000 metabolites
30. Metabolome
Can be applied in much the
same way as proteomics
Host and microbe responses
can be studied to identify
biomarkers in response to
infection, examine pathogenic
and non-pathogenic organisms
to identify virulence factors,
therapeutic targets, prognostic
markers etc.
Can then reprogram the
metabolome e.g. with drugs,
vaccines and immunotherapy
32. Epigenomics
The epigenome comprises all the chemical compounds added to DNA
(genome) that regulate its activity e.g. methylation, acetylation,
phosphorylation
These determine which genes are expressed
High throughput technology is emerging to analyse the epigenome
33. Epigenomics
Evidence emerging that infections can alter the epigenome and therefore
gene expression of the host
Bacteria can affect the chromatin structure and transcriptional program of the
host cells via multiple mechanisms – DNA methylation, histone modification,
noncoding RNAs, chromatin associated complexes
Toxoplasma alters histone acetylation
M tuberculosis controls chrmatin complex downstream from IFN-gamma
Salmonella alters expression of a subset of miRNAs
Legionella pneumophila alters histone acetylation in lung epithelial cells
Listeria – acetylation at the IL-8 promoter
Effects are long lasting causing imprinting of different behaviours in the
affected cell
34. Epigenetic Effects of BCG on
Innate Immune Responses
Mechanism shown to be a reprogramming of
innate inflammatory responses via a modification
of the NOD2 receptor on mononuclear phagocytes
Epigenetic change at the level of histone
methylation
Process has been called “trained immunity”
36. The results from
each of these
‘omics’
technologies are
complementary
but do not
necessarily give the
same answer
Ideally they should
be used together to
get the ‘global’
picture of the
immune response
profile
This is expensive
and time
consuming
‘Systems Biology’
37. ‘Systems Vaccinology’ or Vaccinomics
The approach whereby transcriptome data are combined with in vitro analyses
e.g. cytokine multiplex, tetramer, flow cytometry; plus proteomics,
metabolomics, microbiomics providing a very powerful tool to study vaccines
From Pulendran PNAS 2014; 111: 12300-06
Correlates of protection and
immunogenicity
Predict vaccine safety / AEs/
reactogenicity
Vaccine and adjuvant
development and testing
Vaccine mechanisms and
interactions
May identify unsuspected
novel pathways and disease
links e.g. induction of
oncogenes
38. The “omics” technologies offer powerful tools to interrogate the animal /
human immune response to vaccines and infections
Systems level approaches often an unbiased panoramic view of host-
pathogen interplay and complement traditional reductionist approaches
They will revolutionise our understanding of the global immune resp0nse to
immune challenges
Future potential:
Personalised medicine
• Personalised treatments for infections
• Personalised vaccines
Diagnostics / Prognostics
• Vaccine and infection ‘chips’
• Rapid diagnostic test for biomarkers of infections or vaccine take
Overall Conclusions
39. Biological samples
Sample collection and storage methods critical to the quality of the
study (RNA degradation, protein integrity)
Data storage
Creates enormous amounts of data so storage requires huge databases
and issues with transfer
Data analysis
Highly complex and time consuming, therefore a bottleneck at the point
of analysis and interpretation
Need a bioinformaticist but they need to understand the biology
Need to understand limitations and assumptions of data analysis
techniques
Very expensive technology although prices are coming down
Much of this technology is not being exploited to its full capability
Caveats
40. Infant Immunology Lab and FieldTeams
In particular Jane Adetifa, Ebrima Touray,
Fatou Noho Konteh,Ya Jankey Jagne
MRC Programme Heads/Mentors
Sarah Rowland-Jones, Hilton Whittle
Manchester University
Fran Barker, MyThanh Li
DPM, University of Edinburgh
Peter Ghazal, Paul Dickinson,Thorsten Forster
Funded by MRC(UK) Project Grant Number G0701291
Acknowledgements