Assoc. Professor Katie Flanagan Head of Infectious Diseases, Launceston General Hospital, Tasmania Clinical Associate Professor, University of Tasmania Adjunct Senior Lecturer, Monash University

The ‘omics’ revolution: How will it improve our understanding of infections and vaccines in the future?

‘Omics’ Technology or Systems Biology

Genomics

Transcriptomics

Proteomics

Metabolomics

Microbiomics

Epigenomics

Vaccinomics

Regulomics

Protectomics

Interactomics

Secretomics

Metagenomics

Immunomics

Fluxomics

Human Genome

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)

Transcriptomics or

Gene Expression Profiling

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

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

Transcriptome Response to Vaccines

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

Immune Response to Measles Vaccine

Flanagan et al., in preparation

Baseline 1 week 2 weeks 4 weeks 6 weeks

Gene Pathways Altered Post Measles Vaccine

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, αβ T cell activation & proliferation, T cell mediated cytotoxicity

Upregulated TGF-β signalling, NK cell cytotoxicity, oxidative phosphorylation

From Klein et al, Lancet Infect Dis 2010; 10: 338

Original paper: 594 genes differentially expressed between day 0 & 21 after YF (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 upregulated TLR-associated genes that activate the IFN pathway post YF

Yellow Fever Vaccine Transcriptome Profile Sex Differences

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 or Y 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

Measles Vaccine Study Analysis by Sex

Flanagan et al., in preparation

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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!A

!B

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

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 and Ab 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

Transcriptome Response to Natural Infection

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. J Virol 2014; 88(21): 12254-64)

Thus this methodology offers diagnostic and prognostic potential

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

Neonatal Transcriptome 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 networks using 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.

Proteomics

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

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,

J Transl Med 2015; 13:111)

Virulence factors

Host

Microbiomics

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

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

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 microbiota transplant

Faecal Microbiota Transplant

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

Metabolomics

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

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

Epigenomics

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

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

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”

Putting It All Together

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’

‘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

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

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

Infant Immunology Lab and Field Teams 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, My Thanh Li DPM, University of Edinburgh Peter Ghazal, Paul Dickinson, Thorsten Forster Funded by MRC(UK) Project Grant Number G0701291

Acknowledgements

Thank You