Presented by Steve Kemp and Vish Nene at a University of Nairobi seminar, Nairobi, 5 June 2013

1. Biosciences
research
at
Interna.onal
Livestock
Research
Ins.tute
(ILRI)
A
seminar
given
by
Steve
Kemp
and
Vish
Nene
at
University
of
Nairobi
5th
June
2013

2. 2

3. 3

4. 4
Source:
FAOSTAT,
2010
data

5. Four
out
of
the
five
highest
value
global
commodi.es
are
livestock
5
Source:
FAOSTAT,
2010
data

6. %
growth
in
demand
for
livestock
products
2000
-­‐
2030
6
FAO,
2012

7. ILRI
Mission
and
Strategy
§ ILRI envisions a world where all people have access to enough
food and livelihood options to fulfill their potential.
§ ILRI’s mission is to improve food and nutritional security and to
reduce poverty in developing countries through research for
efficient, safe and sustainable use of livestock— ensuring better
lives through livestock
§ ILRI works in partnerships and alliances with other organizations,
national and international, in livestock research, training and
information. ILRI works in all tropical developing regions of Africa
and Asia.
§ ILRI is a member of the CGIAR Consortium that conducts food
and environmental research to help alleviate poverty and increase
food security while protecting the natural resource base.

8. Strategic
objec.ves
§ ILRI
and
its
partners
will
develop,
test,
adapt
and
promote
science-­‐
based
prac%ces
that—being
sustainable
and
scalable—achieve
beXer
lives
through
livestock.
Ø ILRI
and
its
partners
will
provide
compelling
scien%fic
evidence
in
ways
that
persuade
decision-­‐makers—from
farms
to
boardrooms
and
parliaments—that
smarter
policies
and
bigger
livestock
investments
can
deliver
significant
socio-­‐economic,
health
and
environmental
dividends
to
both
poor
na.ons
and
households.
Ø ILRI
and
its
partners
will
work
to
increase
capacity
amongst
ILRI’s
key
stakeholders
and
the
ins.tute
itself
so
that
they
can
make
beXer
use
of
livestock
science
and
investments
for
beXer
lives
through
livestock.

9. ILRI’s
competencies
Integrated sciences Biosciences
Gender and equity Vaccines
Resilience Genomics
Value chains and innovation Breeding
Zoonotics and food safety BecA
Feeds Genomics and gene delivery
Livestock and environment (both
directions)
Feed biosciences
Policy, investment and trade Poultry genetics
Animal health delivery
Payment for ecosystem services
Conservation of indigenous animal
genetic resources
Ruminants and monogastrics

10. ILRI’s
research
teams
10
Integrated sciences Biosciences
Animal science for sustainable
productivity
BecA-ILRI hub
Food safety and zoonoses Vaccine platform
Livestock systems and the
environment
Animal bioscience
Livelihoods, gender and impact Feed and forage bioscience
Policy, trade, value chains Bioscience facilities

11. ILRI
Resources
• Staff:
700.
• Budget:
$60
million.
• 30+
scien.fic
disciplines.
• 130
senior
scien.sts
from
39
countries.
• 56%
of
interna.onally
recruited
staff
are
from
22
developing
countries.
• 34%
of
interna.onally
recruited
staff
are
women.
• Large
campuses
in
Kenya
and
Ethiopia.
• 70%
of
research
in
sub-­‐Saharan
Africa.

12. ILRI
Offices
Mali
Nigeria
Mozambique
Kenya
Ethiopia
India
Sri
Lanka
China
Laos
Vietnam
Thailand
Nairobi: Headquarters
Addis Ababa: principal campus
In 2012, offices opened in:
Kampala, Uganda
Harare, Zimbabwe
Office in Bamako, Mali
relocated to
Ouagadougou, Burkina Faso
Dakar, Senegal

13. Biosciences
eastern
and
central
Africa
–
ILRI
Hub
§ a
strategic
partnership
between
ILRI
and
NEPAD.
§ a
biosciences
plahorm
that
makes
the
best
lab
facili.es
available
to
the
African
scien.fic
community.
§ building
African
scien.fic
capacity.
§ iden.fying
agricultural
solu.ons
based
on
modern
biotechnology.
§ hosted
at
ILRI’s
headquarters,
Nairobi,
Kenya.

14. § Biosciences
infrastructure

15. § Biorepository
• Sampling is a very time-consuming and
expensive exercise.
• We have an ethical and scientific
responsibility to make the best use of that
effort and money!

16. § Biorepository

17. § Sequencing
and
bioinforma.cs
The Bioinformatics platform
has 88 compute cores, 31TB
of network-attached
GlusterFS storage and back
up systems.
• 454 GSFLX
– 500 Mbases in 7 hour run
– $10/Mb
– 500bp read lengths
– Homo-polymer problem
• Illumina MiSeq
– 1.5-2Gbases in 27 hour run
– $0.15/Mb
– <150bp read lengths

18. § Sequencing
and
bioinforma.cs

19. § Trypanosomias
research.
§ Vaccine
research

20. African Trypanosomiasis
• Caused by extracellular protozoan
parasites – Trypanosoma
• Transmitted between mammals by Tsetse
flies (Glossina sp.)
• Prevalent in 36 countries of sub-Sahara
Africa.
In cattle
• A chronic debilitating and fatal disease.
• A major constraint on livestock and
agricultural production in Africa.
• Costs US$ 1 billion annually.
In human (Human Sleeping Sickness)
• Fatal
• 60,000 people die every year
• Both wild and domestic animals are the
major reservoir of the parasites for human
infection.

21. Trypanosomias
research
Trypanosomes cause fatal
disease in humans and livestock.
T. congolense,
T. vivax
T brucei rhodesiense
T brucei gambiense

25. Control and Treatment of African Trypanosomiasis
Vector Control (Tsetse Fly)
• Using toxic insecticide
• Not sustainable
• Negative impacts on environment
Vaccine
• Tryps periodically change the major surface
antigen – variant surface glycoprotein (VSG) and
evade the host immune system.
• More than 2 decades, there is no effective
vaccine developed.
Drug
• Drug toxicity and resistance
• Expensive

27. Bovins
Bovins et Glossines
Glossines
Cattle
Tsetse
Cattle and tsetse
Origins of N’Dama and Boran cattle
N’Dama
Boran

28. Contribution of 10 genes from Boran and N’Dama
cattle to reduction in degree of trypanosomosis
Boran (relatively susceptible)
The N’Dama and Boran each contribute trypanotolerance alleles at 5
of the 10 most significant QTL, indicating that a synthetic breed could
have even higher tolerance than the N’Dama.
N’Dama (tolerant)
-15
-10
-5
0
5
10
15
-15
-10
-5
0
5
10
15

29. Studying the tolerant/susceptible phenotype has
problems:
• Separating cause from effect
• Separating relevant from irrelevant.
• Dominance of the ‘what is happening to this
weeks trendy gene/protein/cytokine?’
approach.

30. An EST Library screen identifies
ARHGAP15282H->P mutation in the Bta2
(anaemia) QTL
Ø Screened EST libraries made from four
tissues from N’Dama and Boran for SNP
within shortlisted genes.

31. N'Dama (n = 35) Boran (n = 28)
282P-Allele 0.990 0.125
282H-Allele 0.010 0.875
Gene frequency
H → P mutation at AA282
Alignment of N’Dama ARHGAP15 with
homologues
Cow NDama KFITRRPSLKTLQEKGLIKDQIFGSPLHTLCEREKSTVPRFVKQCIEAVEK !
Cow Boran KFITRRPSLKTLQEKGLIKDQIFGSHLHTLCEREKSTVPRFVKQCIEAVEK !
Human KFISRRPSLKTLQEKGLIKDQIFGSHLHTVCEREHSTVPWFVKQCIEAVEK !
Pig KFITRRPSLKTLQEKGLIKDQIFGSHLHTVCERENSTVPRFVKQCIEAVEK !
Chicken KFISRRPSLKTLQEKGLIKDQIFGSHLHLVCEHENSTVPQFVRQCIKAVER !
Salmon KFISRRPSMKTLQEKGIIKDRVFGCHLLALCEREGTTVPKFVRQCVEAVEK !

32. ARHGAP15 is a RAC binding protein and the mutation at the
proximal end of the RAC binding domain affects in vitro activity
The tolerant allele would be expected to inhibit RAC1 activity in
the MAPK pathway which plays a key role in regulating
inflammatory responses and could lead to the observed
differences in expression or amplify downstream expression
differences caused by other factors.

33. African Trypanosomes Infectivity
• T. congolense
• T. vivax
• T. brucei brucei
• T. brucei rhodesiense
T. brucei gambiense
Cattle Human Baboon (Papio papio)
+ - -
+ + -
Human and baboon resistance is due to innate Trypanosome
Lytic Factor (TLF) in serum which is a subclass of high density
lipoprotein (HDL) and can create pores in Tryps lysosome
membrane and kill the trypanosomes by loss of osmoregulation.
- + -

34. Can we construct a transgenic cow with resistance to
African Trypanosomiasis ?
• Establish a transgenic cattle model with African
Trypanosomiasis resistance using nuclear transfer (cloning).
• On the background of a Kenyan indigenous breed – Kenyan
Boran.
• Introduce the gene – apoL-I from Baboon into Boran, which
is the key trypanolytic component of Baboon’s protective
Trypanosome Lytic Factor (TLF) against both cattle and
human-infective trypanosomes.

35. Complete
protec%on
from
human
infec%ve
Trypanosomes
by
baboon
apoL-­‐I
in
transient
transgenic
mice
0 20 40 60 80 100 120 140
0
20
40
60
80
100
Vector (N=6)
apoL-I + Hpr (N=5)
apoL-I (N=5)
*
*
Days post infection
• P
=
<
0.01
• Vector
vs.
treatment
Thomson
et
al
PNAS
2009
106:19509-­‐19514

36. Apol-3
Construct with
Baboon ApoL-I Genomic
Sequence
Potential
regulator
y
Sequenc
e
Myh 9
(myosin heavy chain 9)
Chromosome 5
Cattle Apol Family Locus
(6, 2 like, 4 like, 3)
Targeting Strategy
Apol-6, 2 like, 4 like

37. Project Strategy
Genomic locus of
Baboon apoL-I gene
Vector construction
Validate the construct in
transgenic mouse
Bovine embryonic fibroblasts
(BEF) primary culture
Transfection & screening
apoL-I Transgenic BEFs
Nuclear Transfer
Transgenic calves
Phenotyping
Trypanosome resistant
transgenic Boran bull
ILRI
ILRI
Kenya
Boran
Roslin
Institute
New York
University
Michigan
State
University

38. NuclearTransfer
(Cloning)
Electrofusion
278 days
Bovine
Embryonic
fibroblast
Oocyte
Oocyte-cell couplet
Blastocyst
Cloned calf born

39. Enuclea.on
Polar body
Polar body
Polar body
MII
plate
UV+Transmitted light
Remove the PB and surrounding
cytoplasm, as little as possible
Check removal of MII plate
under UV light

40. Cell
Transfer
Fibroblast
Select the smallest, round cells with
smooth and shining edge
Inject the selected fibroblast into the
peri-vitelline space and push the cell in
touch with the oocyte cytoplasm.
Oocyte-cell couplet

41. Electrofusion
Line
perpendicular to
the electrodes
electrodes

42. Cell line: Kenya Boran, BEFs_E5_286, Male
No. of Oocytes
No. of
Reconstructed
Embryos
No. of
Blsts
No. of Blsts
transferred
No. of
Embryo
Transfer
Pregnancy
Abortion
No.
of
born
calves
1244
723
85
22
16
5
3
2
58.1%
11.8%
31.3%
60.0%
40%
Summary
of
Control
Nuclear
Transfer

43. Name: Tatu
Date of Birth:16 July 2012 (Kapiti)
Sex: Male
Birth Weight:46 kg
Date of Death: 19 July 2012 (74 hrs)
Cause of death: Low temperature,
low blood glucose …
ID: CL001 (Tumaini)
Date of Birth: 21 August 2012
(ILRI)
Sex: Male
Birth Weight: 35 kg
Current age: 7.5 months, healthy
Two Cloned Calves born through Caesarean Section

44. AtBirth6-Month
CL001 (Tumaini)

45. Identification of cloned calves with microsatellite markers
MS Marker ID
Chromosome
Alleles Size
E5
(Cell line)
231-F
(Tatu)
BH058
(Mother)
CL001
(Tumaini)
Comment
RM006
7
103.24
103.24
103.23
Calf same as E5
106.96
106.95
106.88
106.93
110.7
BM4440
2
123.69
Calf same as E5
No allele as dam
132.21
132.24
132.31
136.54
136.55
136.57
143.41
INRA053
7
90.96
90.92
90.86
Calf same as E5
102.69
102.7
102.7
102.7
110.14
BMS1116
7
141.67
Calf same as E5
143.87
143.77
143.83
146.03
145.93
145.96
145.96
ILST098
2
93.02
Calf same as E5
No allele as dam
101.08
101
101.08
104.77
104.73
104.79
110.45
Two born calves are the same as the cell line in 11 microsatellite markers.

46. Future Activities
Transfection of Boran BEFs line
(Roslin Institute, UK)
Establish Apol-I Transgenic Boran by Nuclear
Transfer with Transgenic Cells
Phenotyping (confirm Tryps resistance)
• Apol-I expression pattern
• Killing of Trypanosomes in vitro (serum) and in vivo
(challenge)
• Monitor the health conditions with growth
Increase Genetic Diversity
• Establish more transgenic cattle with
Kenya Boran BEFs lines
• Establish transgenic cattle with other
Kenyan indigenous breeds
Transgene Delivery
• Develop a breeding programme to
disseminate the transgene with farmers
Regulatory, legal, safety & public awareness issues

47. Future Activities
Tumaini
A cloned Kenya Boran calf
made by SCNT from a Boran
embryo fibroblast cell line
Cloned NOT transgenic

48. Current and future animal
vaccine research activities at
ILRI
Vaccine
Biosciences
Interna.onal
Livestock
Research
Ins.tute
Seminar
at
CAVS,
Kabete
Campus,
5th
June
2013

49. Importance
of
animal
health
research
in
the
developing
world
Ø Livestock offer a powerful pathway out of poverty for ~750
million poor farmers in South Asia and Africa by providing
nutritional and economic security.
Ø Infectious livestock diseases feature prominently among the
constraints faced by livestock agriculture.
• Endemic diseases
• Epidemic/pandemic diseases
• Trans-boundary diseases
• Emerging and re-emerging diseases
• Zoonotic diseases and food safety
Ø For many reasons diseases are neglected problems in affected
countries, a situation exacerbated by a general lack of
investment, vaccine R & D and manufacturing capacity.

50. List
of
current
ILRI
high
priority
diseases
targeted
for
control
Ø African swine fever (ASF) – swine
• African disease threatens the global $150 billion/year pig industry
Ø Contagious bovine pleuropneumonia (CBPP) – cattle
• Regional losses to CBPP amount to ~ $60 million/year
Ø East Coast fever (ECF) – cattle
• Regional losses exceed $300 million/year; kills ~ 1million cattle/year
Ø Peste de petits ruminants (PPR) – small ruminants
• Losses in Kenya alone amount to ~ $13 million/year
Ø Rift Valley Fever (RVF) – small ruminants, cattle and
human
• 2006/7 outbreak in Kenya cost ~ $30 million
• 309 human cases in Kenya, Somalia and Tanzania; 140 deaths
Vaccines save lives and livestock and contribute to food
security and poverty alleviation

51. Socio-­‐economic
impact
of
East
Coast
fever
in
sub-­‐Saharan
Africa
Ø ECF present in 11 countries; it could spread
to 8 more
Ø ~46 million cattle in region; ~28 million at risk
Ø ~1million deaths/year; losses > 300 $ million
Ø Small-holder farmers who would benefit: ~ 20

52. Theileria
parva
life
cycle
R. appendiculatus
schizont-infected cells
sporozoites
piroplasms
merogony

53. An
infec.on
and
treatment
vaccine
A live vaccine for the control of ECF
(Muguga cocktail)
Problems: Liquid nitrogen cold chain, cost, immunological
types

54. Immune
responses
that
contribute
to
immunity
Anti-sporozoite
Anti-schizont

55. An.-­‐sporozoite
immunity:
p67
can
induce
immunity
to
ECF
p67N
p67M
p67C
21 225
226 571
572 651
9 709
reduction in severe ECF by 50% in lab (25% immunity in field)
Average

56. A
classical
CD8+
cytotoxic
T
cell
response
to
the
schizont
stage
of
T.
parva
CTL
P
CTL
P
T cell receptor (TCR) on CTL recognizes
parasite peptide associated with MHC class I molecules

57. Flowchart
of
CTL
an.gen
discovery
ACTGGTACGTAGGGCATCGA
TCGACATGATAGAGCATATA
GCATGACGATGCGATCGACA
GTCGACAGCTGACAGCTGAG
GGTGACACCAGCTGCCAGCT
GGACCACCATTAGGACAGAT
GACCACACACAAATAGACGA
TTAGGACCAGATGAGCCACA
TTTTAGGAGGACACACACCA
Bioinformatics
tools
Predict ~ 5000
gene sequences
& list candidate
vaccine antigens
Clone genes of
vaccine interest
Filter genes via
immunological
assays
T. parva genome sequence
A
Random cDNA
library
B
Candidate CTL antigens
Map CTL epitopes

58. Mapped
parasite
CTL
an.gens/epitopes
CTL epitope Peptide sequence MHC class I gene BoLA sero-type
Tp1214-224 VGYPKVKEEML N*01301 A18 (HD6)
Tp227-37 SHEELKKLGML T2b~
Tp249-59 KSSHGMGKVGK N*01201 A10 (T2a)
Tp296-104 FAQSLVCVL T2c~
Tp298-106 QSLVCVLMK N*01201 A10 (T2a)
Tp4328-336 TGASIQTTL N*00101 A10 (5.1)
Tp587-95 SKADVIAKY T5~
Tp7206-214 EFISFPISL T7~
Tp8379-387 CGAELNHFL N*00101 A10 (5.1)

59. NetMHCpan
–
an
ar.ficial
neural
network
to
predict
CTL
an.gens/epitopes
Center for Biological Sequence Analysis at the Technical University of Denmark
Incorporates correlated effects
Morten Nielsen

60. Use
of
pep.de-­‐MHC
tetramers
in
ECF
CD8+
Perforin+
Tp1+ cells
CTR
CTR
BB007
BB007

61. Diversity
of
BoLA
MHC
class
I
genes?
Cattle -
multiplex
RNA isolation from PBMCs
454 pyrosequencing
RT-PCR
Full length cDNAExon 2- Exon 3
• High
throughput
• Rare
variants Nicholas Svitek –
post-doc

62. Genotypic
diversity
–
a
hallmark
of
T.
parva,
can
compara.ve
genomics
help?
Muguga, Marikebuni, Uganda ~ 64,000
SNPs
SNP distribution: ~ 65% exons, ~15% introns, ~ 20% inter-
genic
81/4076 genes under positive selection (includes Tp2)
[Henson et al., BMC Genomics 13: 503,
2012]
Joana da Silva – hybrid capture NGS
Sequencing more cattle and buffalo derived
parasites

63. An.-­‐schizont
immunity:
trial
of
Tp
an.gens
Graham et al., PNAS, 2006: 30% vaccinated cattle

64. We
need
beXer
methods
to
generate
immune
responses
in
caXle
Anti-sporozoite
Anti-schizont
Exploring vaccination
systems
New adjuvants
Viral vectored systems
Old & new antigens

65. A
porholio
of
innova.on
and
vaccine
related
technology
plahorms
Yeast&with&M.#myc&LC&
genome&
(Delete&puta5ve&&
virulence&factors)&
Less&virulent&M.#myc&LC&
ACTGGTACGTAGGGCATCGA
TCGACATGATAGAGCATATA
GCATGACGATGCGATCGACA
GTCGACAGCTGACAGCTGAG
GGTGACACCAGCTGCCAGCT
GGACCACCATTAGGACAGAT
GACCACACACAAATAGACGA
TTAGGACCAGATGAGCCACA
TTTTAGGAGGACACACACCA
Bioinformatics
tools
Predict gene
sequences and
list candidate
vaccine antigens
Test experimental vaccine
Clone genes of
vaccine interest
(100’s of genes)
Filter genes via
immunological
assays
Pathogen genome mining
(1000’s of genes)
Molecular immunology
tools to assess immune
responses in cattle
(10’s genes)
BASIC&RESEARCH&
Increasing&our&
knowledge&base&
&
“Knowledge*lays*the*
founda2on*for*science”***
!
! Map&immune&responses&to&
infec>on&
! Dissect&pathogen&biology&&&
diversity&
! Study&hostDvectorD
pathogen&interac>ons&
! Characterize&pathogen&
virulence&factors&
! Inves>gate&the&
epidemiology&of&disease&
! Iden>fy&vaccine&and&
diagnos>c&molecules&
&
&
&
&
&
APPLIED&RESEARCH&
Developing&new&
vaccines&&&diagnos>cs&
&
“Vaccines*are*cost8effec2ve*
an28disease*inven2ons”*
&
! Assess&candidate&subunit&
vaccines&
! Assess&aHenuated&
pathogen&vaccines&
! Assess&different&vaccina>on&
systems&
! Engineer&thermoDstable&
vaccine&formula>ons&
! Develop&smarter&easier&to&
use&diagnos>c&tests&
! Facilitate&transla>on&of&
outputs&to&products&
BASIC&RESEARCH&
Increasing&our&
knowledge&base&
&
“Knowledge*lays*the*
founda2on*for*science”***
!
! Map&immune&responses&to&
infec>on&
! Dissect&pathogen&biology&&&
diversity&
! Study&hostDvectorD
pathogen&interac>ons&
! Characterize&pathogen&
virulence&factors&
! Inves>gate&the&
epidemiology&of&disease&
! Iden>fy&vaccine&and&
diagnos>c&molecules&
&
&
&
&
&
APPLIED&RESEARCH&
Developing&new&
vaccines&&&diagnos>cs&
&
“Vaccines*are*cost8effec2ve*
an28disease*inven2ons”*
&
! Assess&candidate&subunit&
vaccines&
! Assess&aHenuated&
pathogen&vaccines&
! Assess&different&vaccina>on&
systems&
! Engineer&thermoDstable&
vaccine&formula>ons&
! Develop&smarter&easier&to&
use&diagnos>c&tests&
! Facilitate&transla>on&of&
outputs&to&products&

66. Acknowledgments
Large number of past and current scientists at ILRI
(Evans Taracha et al) and collaborators (LICR, Oxford Uni, Merial)
Immuno-informatics approach:
John Barlow – University of Vermont
Bill Golde – USDA-ARS (Plum Island)
Soren Buus – University of Copenhagen
Morten Nielsen - Technical University of Denmark
ILRI CRP funds
TIGR and Craig Venter
DFID
NSF-BMFG (BREAD program)
USAID – Feed the Future via USDA-ARS

67. The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is
given to ILRI.
ilri.org
Box 30709, Nairobi 00100, Kenya
Phone: + 254 20 422 3000
Fax: +254 20 422 3001
Email: ILRI-Kenya@cgiar.org
Box 5689,Addis Ababa, Ethiopia
Phone: +251 11 617 2000
Fax: +251 11 617 2001
Email: ILRI-Ethiopia@cgiar.org
other offices
China • India • Mali
Mozambique • Nigeria • Tanzania
Thailand • Uganda • Vietnam
Better lives through livestock
ILRI is a member of the CGIAR Consortium
BeFer
lives
through
livestock
ilri.org

68. The presentation has a Creative Commons licence. You are free to re-use or distribute this work, provided credit is
given to ILRI.
ilri.org
Box 30709, Nairobi 00100, Kenya
Phone: + 254 20 422 3000
Fax: +254 20 422 3001
Email: ILRI-Kenya@cgiar.org
Box 5689,Addis Ababa, Ethiopia
Phone: +251 11 617 2000
Fax: +251 11 617 2001
Email: ILRI-Ethiopia@cgiar.org
other offices
China • India • Mali
Mozambique • Nigeria • Tanzania
Thailand • Uganda • Vietnam
Better lives through livestock
ILRI is a member of the CGIAR Consortium
BeFer
lives
through
livestock
ilri.org