View
537
Download
2
Category
Preview:
DESCRIPTION
Presented by Steve Kemp and Vish Nene at a University of Nairobi seminar, Nairobi, 5 June 2013
Citation preview
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
3
4 Source: FAOSTAT, 2010 data
Four out of the five highest value global commodi.es are livestock
5 Source: FAOSTAT, 2010 data
% growth in demand for livestock products 2000 -‐ 2030
6
FAO, 2012
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.
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.
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
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
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.
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
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.
§ Biosciences infrastructure
§ 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!
§ Biorepository
§ 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 – $0.15/Mb – <150bp read lengths
§ Sequencing and bioinforma.cs
§ Trypanosomias research.
§ Vaccine research
African Trypanosomiasis • Caused by extracellular protozoan
parasites – Trypanosoma • Transmitted between mammals by Tsetse
flies (Glossina sp.) • Prevalent in 36 countries of sub-Sahara
Africa.
In cattle • A chronic debilitating and fatal disease. • A major constraint on livestock and
agricultural production in Africa. • Costs US$ 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.
Trypanosomias research
Trypanosomes cause fatal disease in humans and livestock.
T. congolense,
T. vivax
T brucei rhodesiense T brucei gambiense
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
Bovins
Bovins et GlossinesGlossines
Cattle Tsetse Cattle and tsetse
Origins of N’Dama and Boran cattle
N’Dama Boran
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)
-15-10-5051015
-15-10-5051015
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.
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.
N'Dama (n = 35) Boran (n = 28)282P-Allele 0.990 0.125282H-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 !
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.
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.
- + -
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.
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
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
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
Nuc
lear
Tra
nsfe
r
(Clo
ning
)
Electrofusion
278 days
Bovine Embryonic fibroblast
Oocyte
Oocyte-cell couplet
Blastocyst
Cloned calf born
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
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
Electrofusion
Line perpendicular to the electrodes
electrodes
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
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
At B
irth
6-
Mon
th
CL001 (Tumaini)
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.
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
Future Activities
Tumaini A cloned Kenya Boran calf made by SCNT from a Boran embryo fibroblast cell line Cloned NOT transgenic
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
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.
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
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 million
Theileria parva life cycle
R. appendiculatus
schizont-infected cells
sporozoites piroplasms
merogony
An infec.on and treatment vaccine
A live vaccine for the control of ECF
(Muguga cocktail)
Problems: Liquid nitrogen cold chain, cost, immunological types
Immune responses that contribute to immunity
Anti-sporozoite
Anti-schizont
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
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
Flowchart of CTL an.gen discovery
ACTGGTACGTAGGGCATCGATCGACATGATAGAGCATATAGCATGACGATGCGATCGACAGTCGACAGCTGACAGCTGAGGGTGACACCAGCTGCCAGCTGGACCACCATTAGGACAGATGACCACACACAAATAGACGATTAGGACCAGATGAGCCACATTTTAGGAGGACACACACCA
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
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)
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
Use of pep.de-‐MHC tetramers in ECF
CD
8+
Perforin+
Tp1+ cells
CTR
CTR
BB007
BB007
Diversity of BoLA MHC class I genes?
Cattle - multiplex
RNA isolation from PBMCs
454 pyrosequencing
RT-PCR
Full length cDNA Exon 2- Exon 3
• High throughput • Rare variants Nicholas Svitek –
post-doc
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
An.-‐schizont immunity: trial of Tp an.gens
Graham et al., PNAS, 2006: 30% vaccinated cattle were immune to ECF
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
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&
ACTGGTACGTAGGGCATCGATCGACATGATAGAGCATATAGCATGACGATGCGATCGACAGTCGACAGCTGACAGCTGAGGGTGACACCAGCTGCCAGCTGGACCACCATTAGGACAGATGACCACACACAAATAGACGATTAGGACCAGATGAGCCACATTTTAGGAGGACACACACCA
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 foundation for science”
§ Map immune responses to infection
§ Dissect pathogen biology & diversity
§ Study host-vector-pathogen interactions
§ Characterize pathogen virulence factors
§ Investigate the epidemiology of disease
§ Identify vaccine and diagnostic molecules
BASIC RESEARCH Increasing our knowledge base
“Knowledge lays the foundation for science”
§ Map immune responses to infection
§ Dissect pathogen biology & diversity
§ Study host-vector-pathogen interactions
§ Characterize pathogen virulence factors
§ Investigate the epidemiology of disease
§ Identify vaccine and diagnostic molecules
BASIC&RESEARCH&Increasing&our&knowledge&base&
&
“Knowledge*lays*the*founda2on*for*science”***
!
! Map&immune&responses&to&infec>on&
! Dissect&pathogen&biology&&&diversity&
! Study&hostDvectorDpathogen&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&hostDvectorDpathogen&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&
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
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, KenyaPhone: + 254 20 422 3000Fax: +254 20 422 3001Email: ILRI-Kenya@cgiar.org
Box 5689, Addis Ababa, EthiopiaPhone: +251 11 617 2000 Fax: +251 11 617 2001Email: ILRI-Ethiopia@cgiar.org
other offi cesChina • India • Mali Mozambique • Nigeria • TanzaniaThailand • Uganda • Vietnam
Better lives through livestockILRI is a member of the CGIAR Consortium
BeFer lives through livestock ilri.org
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, KenyaPhone: + 254 20 422 3000Fax: +254 20 422 3001Email: ILRI-Kenya@cgiar.org
Box 5689, Addis Ababa, EthiopiaPhone: +251 11 617 2000 Fax: +251 11 617 2001Email: ILRI-Ethiopia@cgiar.org
other offi cesChina • India • Mali Mozambique • Nigeria • TanzaniaThailand • Uganda • Vietnam
Better lives through livestockILRI is a member of the CGIAR Consortium
BeFer lives through livestock ilri.org
Recommended