1.
WGS for food safety management: Perspectives from Kenya
John Kiiru PhD
Kenya Medical Research Institute, Nairobi

2.
There is a need to generated data related to microbial food quality in Kenya and Africa
Currently, food microbial safety assessment in Kenya is based on traditional isolation methods
Molecular methods including WGS could provide novel strategies for detection of food-borne
pathogens
Data from value chain studies in Kenya
show that
 contamination with pathogens is common
 Major pathogens include E. coli
pathotypes, Salmonella, and Campylobacter
Grains and cereals are contaminated with
aflatoxigenic fungal strains

3.
Salient features of food safety in Developing countries: Sub-Saharan Africa
Item Status
Poverty (funding, resource availability) Low
Food security (too little food, but often contaminated) Low
Food safety** Low
Pathogen diversity High
Food-borne diseases High
Capability of ID of pathogens Low
Reliability of current methods Low
Pathogen source-tracking difficult
Penetration of WGS and sequence analysis capability @ Low
Upload of WGS data to global database low
WGS is still expensive for developing countries especially if the sequence volumes are low
@ Minion is potentially suitable but it is still use-and-dispose gadgets
**No clear food-safety regulation mechanisms and testing is not properly coordinated

4.
Technique Status (capability)
PFGE Excellent
Classical Microbiology (isolation and
typing)
Excellent
PCR Excellent
Classical Epidemiology Excellent
WGS Poor, (collaborate with Sanger and
University of Oxford)
Bioinformatics Medium
Our laboratory capacity at CMR-KEMRI
PFGE of Salmonella in children and their contacts Clonal relatedness of Vibrio from 1992-2007

5.
E. coli contamination levels for poultry meat from retail outlets in Nairobi region
In total 78% (145/186) of samples had at least E. coli or coliforms detected, with a range from log -0.09 to 2.38 cfu/mL
of rinsate. Other contaminants included Campylobacter spp (52%) and Salmonella spp (4%).
Examples of studies dealing with food safety in Kenya: - contamination burden

6.
Kenya is one of the world’s hotspots for aflatoxins,
•Highest incidence of acute toxicity ever documented.
•Severe outbreaks in 2004 and 2010, more than 300 people poisoned, >100 died.
• Domestic animals have also died in outbreaks .
Examples of studies dealing
with food safety in Kenya

7.
Table 3.29. MICs of four antimicrobial agents for Campylobacter spp from poultry retail
samples from Thika region
(n=321)
________________________________________________________________________
Antimicrobial
agent
MIC range Mode MIC50 MIC90 % Resistant
(μg/ml)
_______________________________________________________________________
Tetracycline 0.25-256 64 128 256 65
Gentamicin 0.5-32 1 2 2 12
Erythromycin 0.5-8 4 4 8 6
Ciprofloxacin 0.125-4 0.125 0.125 0.25 5
__________________________________________________________________________
Campylobacter from poultry are significantly resistant
Examples of studies dealing with food safety in Kenya: - Antimicrobial resistances

8.
Status of WGS in food safety in Kenya
Level of technique penetration Low
Readiness of Kenya to take up WGS for food safety regulation? Not yet
low penetration?: Technique deemed too expensive and complicated for day-to-day application
Where is WGS most commonly used in Kenya Medical research on major pathogens
How improve penetration?:
Sensitize government on the potential benefit of the technique
Subsidized equipment and reagents for WGS
Increase collaboration with institutions and nations already applying WGS
Potential partners in improving use of WGS in food safety in Kenya
The government Agriculture institutions KARLO, KEPHIS, etc
CGIAR institutions such as ILRI, WAC (ICRAF), ICIPE, etc
Medical research institutes such as KEMRI, WRP, WT, and CDC
Universities faculties of food technology, Agriculture, Public health etc
Where is WGS available in Kenya ILRI

9.
Potential of applications of WGS in food safety in Kenya
Identification of major pathogens and pathotype burden in key food types (e.g. staple foods)
At what point the major pathogens enter the food/value chain
Geospatial/temporal distribution of major food-borne pathogens
Identification of new or unique markers for rapid detection of key pathogens in the food chain
Identification of unique species that may add value (e.g. increased shelve-life) to different
types of foods such fermented, dried and salted foods
Genetic and epidemic evolution of selected food-borne pathogens
Identification of major reservoirs for food-borne pathogens in different parts of Kenya

10.
Distribution of S. Typhi haplotypes identified in Kenya.
Samuel Kariuki et al. J. Clin. Microbiol. 2010;48:2171-2176
Examples of applications of WGS in Kenya: - Mapping pathogen evolution

11.
Examples of applications of WGS in Kenya: - Mapping pathogen evolution

12.
Kiiru J,, et al. (2013) A Study on the Geophylogeny of Clinical and Environmental Vibrio cholerae in Kenya. PLoS ONE 8(9): e74829.
doi:10.1371/journal.pone.0074829
Examples of applications of WGS in Kenya: - Geospatial distribution and pathogen evolution

13.
Examples of applications of WGS in Kenya: - Identification of novel strains

14.
π
ππ
π
Self or relative hospitalized in the last 6 months
ST131
Animal Strain with resistance to β-lactams+cipro+aminoglyc.
Human Strain with resistance to β-lactams+cipro+aminoglyc. Environmental Strain with resistance to β-lactams+cipro+aminoglyc
Homestead with a relative on antibiotic at sampling
WGS application in “zoonosis” Data suggest exchange of strains between people, animals, environment

15.
Examples of applications of WGS in Kenya: -

16.
Sample submitted
Enrichment
Isolations
DNA extraction
Shipment of DNA to the Sanger
WGS at the Sanger
Bioinformatics
KEMRI expert goes to the Sanger
Use the Sanger server and analyze from KenyaDATA output e.g. journals
Challenges
1. Data often overwhelming
2. Internet connection is a challenge
3. Few expert with Bioinformatics
Challenges
1. Only a fraction of data is published
2. Little uptake of the results in policy formulation
3. Bioinformatics analysis is a challenge
4. Data sharing
A sample flow-chart for WGS in KEMRI
Merge field metadata
with WGS
Basis of selection of
isolates for WGS??

17.
Priority organisms for WGS analysis for food safety in Kenya.
1. Aflatoxin-producing fungi***
2. E. coli pathotypes in food and water @
3. Campylobacter species in meats and other food and the environment @
4. Vibrio in water and in food during epidemics ***
5. Salmonella serotypes in food and water ***
6. Shigella species in food
7. Cryptosporidium in water (and food?) @
8. Enteric viruses?? @
*** recent outbreaks reported
@ poorly investigated (but research data show these are significant)

18.
Conclusions
There is a great potential for application of WGS strategy for improvement of food safety in Kenya
Cost and technology transfer barriers  the major hindrance for penetration & acceptance of this technique
The major barrier to WGS is the questions many policy makers ask “why do we need WGS?”
Success of this technique will depend on the collaboration between governmental and technology companies
WGS presents a novel strategy for mapping disease/pathogen distribution and evolution in developing countries
In order to promote use of WGS and to realize the benefits of this technology as far as food safety in Kenya is concerned,
various institutions must collaborate
WGS research in Kenya will be useful only if the data is vital for informing policy formulation

19.
KEMRI+ CDC+WT program
Core-mandate on human
infectious agents including
food-borne pathogens
Monitor and
inform policies
on food safety
standards
The WGS
available
Non-governmental and intergovernmental
Human health research
Teaching
Examples of institutions can can collaborate to promote WGS in Kenya

20.
Acknowledgements
KEMRI
Prof Sam Kariuki
Various authors referred to
in this presentation
FAO for facilitation