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.

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?

2. ‘Omics’Technology or Systems Biology
 Genomics
 Transcriptomics
 Proteomics
 Metabolomics
 Microbiomics
 Epigenomics
 Vaccinomics
 Regulomics
 Protectomics
 Interactomics
 Secretomics
 Metagenomics
 Immunomics
 Fluxomics

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)

4. Transcriptomics
or
Gene Expression Profiling

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

7. Transcriptome Response
toVaccines

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
!
!
A !
B

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

16. Transcriptome Response
to Natural Infection

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.

20. Proteomics

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

23. Microbiomics

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

28. Metabolomics

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

31. Epigenomics

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”

35. Putting It AllTogether

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

41. ThankYou