1. 7th East African Health & Scientific Conference
27th - 29th March, 2019
Dar Es Salaam, Tanzania
Whole genome sequence-based characterisation of clinical Escherichia coli isolates
Tolbert Sonda,
Data Management/Bioinformatics Unit

2. Background E. coli is an important causative agent for Diarrhoea
Neonatal meningitis
Hemolytic uremic syndrome outbreaks (China, German)
UTI 64.5% in Kenya, up to 79% in Tanzania
ESBL global estimates by 2030 in E. coli will be 77.0%
Critical priority, AMR in E. coli a threat to economy, health (WHO, 2018)

3. Background Economic loss
Lots of health-care resources e.g. 500mg Ampicillin for Sh500 (0.2 USD) vs. 1g Meropenem for Sh25,250 (11.5 USD)
% reduction of a country’s GDP equivalent to USD 100 trillion of global cost
Loss of life
Increased risk of treatment failure and death
10 million deaths/year of AMR associated infections
Psychosocial effects
Sufferings due to prolonged hospitalisation and medication

4. Gap in diagnostic tools, data
High AMR but scarce data in Africa
Only 23 (42.6 %) African countries had AMR data (Tadesse et al., 2017)
Lack of key tools to tackle AMR in Africa
Laboratory capacity, surveillance, IPC
Antibiotic name
E. coli
Penicillins
Median (IQR)
Amoxicillin
88.1 (81.4–92.6)
Ampicillin
86.7 (69.2–96.7)
Amoxi-clavu
43.5 (30.8–61.9)
Cephalosporins
Ceftriaxone
31.5 (6.9–47.7)
Cefotaxime
26.8 (8.3–64.5)
Carbapenems
Meropenem
0 (0–5.7)
Imipenem
0.2 (0–5.5)

5. A need for robust tools For diagnostics
Inform clinical therapy/treatment within a short TAT
Early detection of source strains in hospital outbreaks
For surveillance investigations
Impact assessment of AMR stewardships or interventions
Data creation for epidemic preparedness/response
NextGen Sequencing and bioinformatics present potentials for microbial investigations and diagnostics
Next Gen sequencing/Bioinformatics holds the current and future potentials for microbial investigations and diagnostics

6. Objectives
To establish NGS infrastructure for microbial investigations and diagnostics
To determine sequence types, virulence genes in E. coli
To identify resistance genes encoding ESBL (and others) in E. coli
To determine the molecular relatedness of E. coli using phylogenetic trees

7. Design, recruitment, data
Wards
Clinics
Identification
Inclusion
KCRI Data archive
Micro unit
Data unit
Analysis
Data
Genomics unit
Bioinformatics unit
Data storage
Sharing
Global Repositories
Biotech. repository

8. Bioinformatics STUFF Assembly (de novo) MiSeq MAGIC ↓ images Classify
call bases
demultiplex
trim …
Generate FASTQ
READS
Assembly (de novo)
Mapping
(on reference)
Classify
(typing)
Species typing
Variant finding
STUFF
DATA
MiSeq
Phylogeny
construction
Phenotype prediction
K-merise
(multi-purpose)
Functional annotation
MLST
Identification
Drug
resistance
Virulence
SNP/Indels
Diagnostics
Transmission
control
Outbreak
investigation
Comparative
genomics
ANALYSIS
Prediction

9. Genomics/Bioinfo infrastracture

10. Findings, discussion ST-131 2007 MDR outbreak in USA, Johnson et al.
ESBL=73.7%%
ST-131
2007 MDR outbreak in USA, Johnson et al.
Sonda et al., 2018

11. Findings, discussion ST-10 complex (ST617) 1991 UTI outbreak in DKK
CTX-M predominance
Sonda et al., 2018

12. Findings, discussion High clonal diversity of E. coli
21 different STs circulating
O25:H4-ST131 and O89:H10-ST617 predominant
ESBL genes >70%, O25:H4-ST131, major cause of MDR E. coli infections
Multiple virulence found
iha in 22 (57.9%) for host cells adherence
iss in 25 (65.7% for immune evasion
Multiple virulence underlines the exceptional ability of E. coli

13. Conclusion, Implications
NGS is an effective early warning system/tool:
Resistance, virulence and emerging pathogens
Outbreak source tracing
Hospital surveillance
(Commensal) E. coli are an important arsenal to dealt with
Heavily loaded with AMR and virulence genes
NGS/bioinformatics are promising tools

14. Stop AMR Simple practices can stop AMR spread
Visit hospital premises only when needed
Hygiene eg hands, clinical coats
Use antimicrobials only when needed
Finish antimicrobial treatment courses

15. Supervision, acknowledgements
Kilimanjaro Clinical Research Institute
Prof Blandina Mmbaga
East Africa Health Research Commission
Prof Gibson Kibiki
Technical Univ. of Denmark
Prof Frank Aarestrup
Prof Ole Lund
University of Copenhagen
Prof Michael Alifrangis