d4.18 cogwheel workshop 5 wp4 joint integrative projects · deliverable d4.18: 5th cogwheel...
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D4.18 Cogwheel Workshop 5
WP4 Joint integrative projects
Responsible Partner: SVA
Contributing partners: NVI
GENERAL INFORMATION European Joint Programme full title
Promoting One Health in Europe through joint actions on foodborne zoonoses, antimicrobial resistance and emerging microbiological hazards
European Joint Programme acronym
One Health EJP
Funding This project has received funding from the European Union’s Horizon 2020
research and innovation programme under Grant Agreement No 773830.
Grant Agreement Grant agreement n° 773830
Start Date 01/01/2018
Duration 60 Months
DOCUMENT MANAGEMENT Deliverable D4.18: 5th cogwheel workshop report with JPIAMR
WP and task WP4; Task 4.3 – Subtask 4.3.1
Author Karin Artursson (SVA)
Other contributors Marie Nykvist (SVA), Solveig Sølverød Mo (NVI) and Kristin Sæbø Pettersen (NVI)
Due month of the
report
M30
Actual submission
month
M30
Type
R: Document, report DEC: Websites, patent filings, videos, etc.; OTHER
R
Save date: 29-Jun-20
Dissemination level
PU: Public (default) CO: confidential, only for members of the consortium (including the Commission Services)
PU
Dissemination
Author’s suggestion to inform the following possible interested parties.
OHEJP WP 1 ☐ OHEJP WP 2 ☒ OHEJP WP 3 ☒
OHEJP WP 4 ☒ OHEJP WP 5 ☒ OHEJP WP 6 ☐
OHEJP WP 7 ☐ Project Management Team ☐
Communication Team ☐ Scientific Steering Board ☐
National Stakeholders/Program Owners Committee ☐
EFSA ☐ ECDC ☐
Other international stakeholder(s):
………………………………………………………………………
Social Media: .........................................................................................................
Other recipient(s): ...............................................................................................
Report from Cogwheel Workshop 5 One Health EJP / JPIAMR
Introduction In Work Package 4 (WP4) of the One Health European Joint Programme (OHEJP), EU initiatives that require strategic interaction with OHEJP are identified in collaboration with WP2 and WP5, to avoid redundancy and to further leverage the alignment at EU level. One of the instruments, so called cogwheel workshops (CWs), are being organised by WP4 to allow key actors and relevant partners within the OHEJP, typically Project leaders or WP leaders within Joint Research Projects (JRP:s) or Joint Integrative Projects (JIP:s), to identify synergies, joint priorities and opportunities for collaboration within the OHEJP or with other EU initiatives. Before contacting the relevant EU initiative/project, it is important to identify and collect (common) JRP and JIP needs and to define if the initiative complies with these needs. Furthermore, it is important to see if the initiative can complement or assist the OHEJP, for instance with database/repositories, protocols, advice, practical collaboration (sequencing, bioinformatics) and so on.
Eight CWs will be organised during the OHEJP. The reports from the CWs will be part of the input to the Strategic Research Agenda (SRA) of WP2, as well as the Strategic Research and Innovation Agenda (SRIA) of WP7.
The target for the fifth CW was five projects funded through the 9th call from the Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) on Diagnostics and Surveillance;
• MAGITICS: MAchine learning for diGItal diagnosTICS of antimicrobial resistance;https://www.jpiamr.eu/wp-content/uploads/2019/11/MAGITICS.pdf
• K-STaR: A K-mer Based Approach for Institutional AMR Surveillance, TransmissionMonitoring, and Rapid Diagnostics; https://www.jpiamr.eu/wp-content/uploads/2019/11/K-STaR.pdf
• OASIS: One Health AMR Surveillance through Innovative Sampling;https://www.jpiamr.eu/wp-content/uploads/2019/11/OASIS.pdf
• TRIuMPH: Improving the TRIcycle protocol: upscaling to national Monitoring, detection ofCPE and WGS pipelines for One Health Surveillance; https://www.jpiamr.eu/wp-content/uploads/2019/11/TRIuMPH.pdf
• IDx: An exploration of regulatory, corporate, relational, and technical barriers to uptake ofdiagnostics in the fight against AMR; https://www.jpiamr.eu/wp-content/uploads/2019/11/IDx.pdf
The CW will ensure that OHEJP development is complementary and that potential synergies are identified.
Practicalities • Information about the upcoming cogwheel workshop was sent to all OHEJP Project leaders and
deputies and members of the Scientific Steering Board (SSB) on 19 March 2020. Information aboutJPIAMR was included.
• Five OHEJP projects (two from the integrative category; Care and OH-Harmony Cap, and three fromthe foodborne zoonose category; TOXOSOURCE, MedVetKlebs and NOVA) responded and 10 outof 12 JPIAM projects in total were identified as relevant. Five of the JPIAMR projects were invited,ensuring all OHEJP projects having at least one project of interest. All five invited JPIAMR projectsattended the workshop.
• Invitation to the CW was sent on 3 April 2020 with deadline for registration on 21 April 2020.
• Seven OHEJP and the five invited JPIAMR projects signed up with presentations at the CW. TwelveOHEJP projects were represented in total.
• The CW was held as an online meeting with 38 participants using Adobe Connect on 28 April 2020.The agenda is included in the annex.
• The meeting was recorded until lunch, and a document with link to the recorded meeting, thegiven presentations and contact information for follow-up questions was distributed one weekafter the CW.
Attendance list
Project Representatives (institution)
FARMED Manal AbuOun (APHA) Kevin Vanneste (Sciensano) Carlus Deneke (BfR)
FULL- FORCE Pieter Jan Ceyssens (Sciensano) Henrik Hasman (SSI) Benoit Doublet (INRAE) Alma Brolund (Public Health Agency of Sweden)
LISTADAPT Yann Sevellec (ANSES)
MATRIX Esther Maria Sundermann (BfR)
OH-HARMONY-CAP Nadia Boisen ( also OHEJP administration, SSI)
ORION Estíbalís Lópes de Abuchuco (BfR)
TOXOSOURCES Pikka Jokelainen (also OHEJP administration, SSI)
ADONIS Ana Amaro (INIAV) Clara Samper (VISAVET-UCM) Angela Pista (INSA)
CARE Mia Torpdahl (SSI) Mery Pina (IP)
DiSCoVeR Vicente Lopez Chavarrias (VISAVET-UCM)
RaDar Kyrre Kausrud (NVI) Madelaine Norström (NVI)
OHEJP Administration
Solveig Sølverød Mo (NVI) Kristin Pettersen (NVI) Marie Nykvist (SVA) Karin Artursson (SVA) María Ugarte Ruiz (VISAVET-UCM) Cécile Boland (Sciensano) Racha El Mounaged (Sciensano)
Other Teresa Nogueira (INIAV)
Output All OHEJP projects interested in participating in the CW had the opportunity to do so. The relevant key elements in JPIAMR, as identified by each OHEJP project, are listed below. The proposed action points from each project are listed in a separate summary table below. Meeting summary, per project Key elements in JPIAMR relevant for each EJP project:
EJP Project FARMED and APHA
• The K-STaR project seems quite appropriate for FARMED, and sharing of practical bioinformatics ideas are of interest. Also the applicability of their currently available software could be tested. Their focus seemed to be on long read sequencing of isolates / targeted metagenomics of specific species. Therefore an unbiased metagenomic approach on a complex matrix might require a different analysis approach. The machine learning in MAGITICS could be useful for FARMED also.
• Publication https://www.nature.com/articles/s41564-019-0656-6 and Software https://github.com/c2-d2/rase were also mentioned.
EJP Project FULL-FORCE
• Although there is no immediate overlap between the presented projects and FULL_FORCE, the k-mer
approach pursued by K-STaR project sounds very attractive as a follow-up action after the MedVet
groups have established their capacity for long-read sequencing.
EJP Project LISTADAPT
• No overlap with the LISTADAPT project was reported
EJP Project MATRIX
• Surveillance in One Health was identified as relevant in general
• Relevant key elements in JPIAMR were knowledge, collaboration, ensuring non-overlapping activities and AMR priorities. These key elements were also relevant for ADONIS, DiSCoVeR and OH-HARMONY-CAP.
EJP Project OH-HARMONY-CAP
• We expect that there will be no or very little overlap between OH-Harmony-Cap and the JPIAMR projects. Notably, OH-Harmony-Cap is only focusing on zoonotic pathogens and AMR for Salmonella and Campylobacter in EU. Seeing that our aim is to harmonize protocols and propose a harmonised testing strategy for Europe, the output and deliverables will be designed in such a way that they can be used in laboratories, which might be beneficial for some JPIAMR projects.
• Relevant key elements as described for MATRIX
EJP Project ORION
• No overlap with the ORION project was identified.
EJP Project TOXOSOURCES
• Networking between projects
EJP Project ADONIS, MATRIX, MOMIR-PPC and DISCoVeR (VISAVET-UCM)
• Tools, omics knowledge and One Health approach (TRIuMPH) were identified as relevant key elements.
• Some of the JPIAMR projects using ‘omics’ as the main tool to study antimicrobial resistance, such as K-STaR, are very relevant to the developing work within bioinformatics in the VISAVET project.
• JPIAMR provides complementary background knowledge to DISCoVeR, which overall aims to provide the basis for future approaches for source attribution of zoonotic foodborne pathogens and AMR. Further and deeper research in these areas will bring good ideas, and hopefully, useful solutions to the challenges posed by AMR.
EJP Project CARE
• From the presentations made by the JPIAMR projects, both MAGITICS and K-STaR might be relevant for CARE. WP2 and WP3 in CARE will be working with creation of a EURO panel of One Health reference material and the access and sustainability of well defined microbial reference material.
• Direct action points and collaboration is not foreseen as the projects are running simultaneously and with different target organisms; CARE focuses in a one health zoonotic perspective and MAGITICS and K-STaR is oriented around developing databases and tools for prediction of resistance emergence.
EJP Project RaDAR
• The projects presented had little to no overlap with RADAR, possibly methodology used in MAGITICS could be of relevance for WP1 in RADAR. However, no participants from WP1 RADAR were present at the meeting so it’s hard to tell.
• Interesting workshop and even if synergy points to current projects we are involved in not was detected, it will certainly be beneficial to have the information of the projects provided for future studies.
EJP Project OHEJP WP3
• Networking between projects
EJP Project OHEJP WP5
• Networking between projects
• JPIAMR as OHEJP stakeholder – mutual added value is obvious
Summary table Action points
Project Action points Who, when
FARMED • Contact K-STaR group to discuss possible collaboration
• Test software
• WP2 leaders (Saria Otani, Kevin Vanneste, Carlus Deneke), in the next 12 months
FULL-FORCE • None identified at this point •
LISTADAPT • None identified at this point •
MATRIX • Contact for collaboration, also relevant for ADONIS and DiSCoVeR
• Leader and Deputy Leader of each project, when relevant.
OH-HARMONY-CAP • None identified at this point •
ORION • None identified at this point •
TOXOSOURCES • Continue following the other projects for points of shared interest/synergy
• Pikka Jokelainen, Continuing
ADONIS and DISCoVeR/VISAVET-UCM
• Following up on omics knowledge. • All partners. When was not identified.
CARE • None identified at this point •
RaDAR • None as we only participated to get some general information of the projects
•
OHEJP WP3 • Continue following the projects for points of shared interest/synergy
• Follow up how participation will be reported by the JRPs
• Pikka Jokelainen, Continuing
OHEJP WP5 • Continue following the projects for points of shared interest/synergy
• Mention/discuss this workshop at SCM
• Pikka Jokelainen, Continuing
Other comments
• Even though no collaboration opportunities were immediately identified for some of the projects, insight in the JPIAMR project was confirmed interesting and relevant in general for the OHEJP projects. Also, the presentations given by the OHEJP projects is a good opportunity for updates and looking for potential synergies within the OHEJP.
• JPIAMR will arrange a workshop in November on surveillance, and OHEJP was invited to initiate further collaboration.
• Some of the JPIAMR projects were in early stages, and some outside the EU, which may have challenged the identification of relevance.
• For detailed information, the presentations were not deep enough, and sharing of a document with contact information to the presenters was requested. This way, participants could contact the relevant projects directly and get more information if relevant.
• Feedback was given on the importance of also identifying projects that do not overlap, and that collaboration is not the only aim. Learning from each other is also important.
• Aspects of the JPIAMR projects covering economy was highly appreciated.
• Considering all new approaches to omics it is suggested and appreciated working together – enhancing the possibility to do “big things”.
• Participants confirmed that the format of the meeting was satisfying.
• The EU project SafeConsume was suggested by TOXOSOURCES as relevant for the CW#6
Concluding remarks Representatives from the OHEJP were content with the cogwheel workshop. In general, no major overlaps between JPIAMR and the OHEJP projects represented were identified. The participants were happy with the online format of the meeting.
Presentations given at the meeting See Annex. The recording of the cogwheel workshop can be accessed through the following link; https://svasweden.adobeconnect.com/p99orsayzc7o/
ANNEX Cogwheel Workshop 5
One Health EJP / JPIAMR
Item Page Agenda 2 JPIAMR introduction – Laura Plant 4 iDX – Olof Lindahl 22 MAGITICS – Juho Rousu 60 K-STaR – Derek MacFadden 69 OASIS – Frank van Leth 91 TRIuMPH – Heike Schmitt 117 FARMED – Manal AbuOun 136 FULL-FORCE – Pieter-Jan Ceyssens 142 LISTADAPT – Yann Sevellec 149 MATRIX – Esther M. Sundermann 163 OH-HARMONY-CAP – Nadia Boisen 168 ORION – Estibaliz Lopez de Abechuco
176
TOXOSOURCES – Pikka Jokelainen 184
This meeting is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
Cogwheel Workshop OHEJP and JPIAMR hosted by SVA and NVI, 28.04.2020
1/2
Date 28 April 2020
Venue Virtual meeting, https://svasweden.adobeconnect.com/ohejp-cogwheel-workshops/
Meeting Cogwheel Workshop OHEJP and JPIAMR
Meeting agenda
09:00-14:30 Cogwheel Workshop (28.04.2020)
09:00-09:10 Welcome and presentation of participants
9.10-11.50 Presentation of JPIAMR projects and discussion (30 min per project)
o Short introduction - Laura Plant (9.10-9.20)
o IDx – Olof Lindahl (9.20-9.50)
o MAGITICS – Jaques Corbeil (9.50-10.20)
o K-STaR – Derek MacFadden (10.20-10.50)
o OASIS – Frank van Leth (10.50-11.20)
o TRIuMPH – Heike Schmitt (11.20-11.50)
11.50 – 12.45 Presentation of OHEJP projects and discussion (5 min per project)
o FARMED – Fast Antimicrobial Resistance and Mobile-Element Detection using metagenomics for animal and human on-site tests (Manal AbuOun)
o FULL-FORCE – Full-length sequencing for an enhanced EFFORT to map and understand drivers and reservoirs of antimicrobial resistance (Pieter-Jan Ceyssens)
Agenda
One Health EJP
Grant Agreement number 773830
This meeting is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
Cogwheel Workshop OHEJP and InfAct hosted by SVA and NVI, 22.01.2020
2/2
o LISTADAPT – Adaptive traits of Listeria monocytogenes to its diverse ecological niches (Yann Sevellec)
o MATRIX – Connecting dimensions in One Health surveillance (Esther-Maria Sundermann)
o OH-HARMONY-CAP – One Health Harmonisation of Protocols for the Detection of Foodborne Pathogens and AMR Determinants (Nadia Boisen)
o ORION - One Health Surveillance Initiative on Harmonization of data collection and interpretation (Estibaliz Lopez de Abechuco Garrido)
o TOXOSOURCES – Toxoplasma gondii sources quantified (Pikka Jokelainen)
12.45-13.30 Break
13:30-14:30 Separate meeting with only OHEJP projects, summary of discussions and follow up
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMRwww.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
JPIAMR: Global coordination of AMR research funding and activitiesLaura PlantJPIAMR Secretariat
One Health EJP Cogwheel Workshop 28th April 2020
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
Uniting 28 countries to address AMR with a One Health perspective
JPIAMR: A global organisation
The EuropeanCommission (DG Research) is a full non-voting member
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
JPIAMR in action: achievements
Joint AMR Strategic Research and Innovation Agenda
Roadmap of Actions: calls, scientific workshops & JPIAMR-VRI activities
Identification of research priorities/gaps: R&D investment dashboard
Mapping: 1.8 B€ in AMR investments
JPIAMR Research Funding 80 M€ (61 research projects and 31 Networks)
JPIAMR-Virtual Research Institute
Global partnerships and collaborations
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
SRIA: Strategic Research and Innovation Agenda• Therapeutics: Discovery of new antibiotics and therapeutic
alternatives, and the improvement of current antibiotics and treatment regimens.
• Diagnostics: Development and improvement of diagnostics to improve use of antibiotics and alternatives to antibiotics.
• Surveillance: Optimisation of surveillance systems to understand the drivers and burden of AMR in a One Health perspective.
• Transmission: Understanding and preventing the transmission of AMR.
• Environment: The role of the environment in the selection and spread of AMR.
• Interventions: Investigation and improvement of infection prevention and control measures in One Health settings.
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
Roadmap of Actions 2019-2024
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
JPIAMR Open Calls
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
JPIAMR workshops
Transmission Workshop, September 2020
“JPIAMR Early Researchers Capacity Building Workshop”, October 2020
Final workshop for JPIAMR Networks, 4-5 November 2020
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
AMR Research Funding and Research Project Dashboard
58% of the total investmentis in Therapeutics
22 countries+ European Commission & Wellcome Trust
1 939Total projects
1 794 M€Total investment
87%Projects in antibiotic
resistanceDiagnostics Therapeutics Environment Transmission Surveillance Interventions
1033 M€
236 M€203 M€
134 M€ 120 M€68 M€
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
Basic, pre-clinical and phase 1 clinical trials
• Goal: to foster and support multi-national translational research collaborations that can accomplish more than individual countries working independently.
• 3-6 researchers per project receivingfunding from their own country committment.
• To date 61 projects awarded
JPIAMR Research Projects
Therapeutics34 %
Diagnostics10 %
Surveillance13 %
Transmission28 %
Environment9 %
Interventions6 %
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
JPIAMR has funded 20 Therapeutics research projects• 100 partners in 17 countries
• 25M€ invested
33 %
7 %
27 %
13 %
20 %
WHO priority pathogens
Medium (Priority 3)
Critical (Priority 1)
High (Priority 2)
TB
Others
0 1 2 3 4 5 6 7 8 9 10 11 12
S . M A L T O P H I L I A ( G - )
V . C H O L E R A E ( G - )
B . S U B T I L I S ( G + )
H . I N F L U E N Z A E ( G - )
S . P N E U M O N I A E ( G + )
C . J E J U N I ( G - )
H . P Y L O R I ( G - )
E . F A E C I U M ( G + )
S . A U R E U S ( G + )
M . T U B E R C U L O S I S
E N T E R O B A C T E R S P P ( G - )
A . B A U M A N N I I ( G - )
K . P N E U M O N I A E ( G - )
E . C O L I ( G - )
P . A E R U G I N O S A ( G - )
no. of projects investigating pathogens
Distribution of JPIAMR funded projects on the basis of pathogens studied
JPIAMR supported research projects addressing pathogens categorised under WHO Priority
Pathogens List
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
Overview of funded projects in JPIAMR 1st call on Therapeutics
Treat infections caused by Pseudomonas aeruginosa
Counteract β-lactam resistance Against TB
Under clinical
development
ReceivedCARB-X support
Patent pendingCollaboration
with TB Alliance
11
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
Human
Health care Community
Animal & Nutrition
Agriculture Pets
Environment
Water, Soil & Air
JPIAMR Transmission and Intervention funding
ONE HEALTH
Data/Advice for public healthpolicy makers and managers
Interventions & Intervention proposals
Antimicrobial Stewardship
Drug targets/drug combinations/ alternative treatments
Vaccine design
Diagnostic tests
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
Human
Health care Community
Animal & Nutrition
Agriculture Pets
Environment
Water, Soil & Air
BEAT-AMR
COLL.DAM.
EmerGE-NeT
Pneumospread
Res.SP_AMR
JumpAR
AB-assist.
ImpresU Pilgrim
MACO TRA
PET-Risk
TransComp-ESC-R
ExcludeMRSA
Resilience
ReduceAMU
TransPred
HECTORSpARK
ST_131 Transm. STARCSMOD
ERN
ASBOpen
Steward
PREPARE
AwareWWTP
DAR WIN
Gene-gas
INART ARMIS
JPIAMR Transmission and Intervention funding
Call 3: Transmission Dynamics
Call 5: Prevention, Control & Intervention Strategies29 projects with a focus
on transmission and prevention of AMR
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
Enhance resource alignment and maximise existing and future efforts to combat AMR
• One Coordinator receiving funding from their own country committment to facilitate Networking activities.
• To date 31 Networks funded
JPIAMR: Networks
Therapeutics20 %
Diagnostics11 %
Surveillance36 %
Transmission5 %
Environment8 %
Interventions20 %
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
JPIAMR Networks: Global distribution partners
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
The JPIAMR-Virtual Research Institute Digital Platform
Connection to data Connection to people Call to action• Aggregate data• Facilitate data and information mining• Connect users to resources• Hub-like functionalities
• Facilitate people-people interactions• Connect individuals to communities• Relationships and Networking building• Visibility
• Facilitate mobilization around issues• Achieve shared goals• Outcomes
Tools DataServices Information
Search
EngineOnline discussion
forumsCapacity
Building
www.jpiamr.eu twitter.com/JPIAMR facebook.com/JPIAMR
JPIAMR and global coordination - building partnerships
UNGA, IACG & Call for Action
G7/G20 ResolutionsEU Council Conclusions
ORGANISATIONS FUNDERS RESEARCH
INITIATIVES
The iDX Project- and some background
Olof Lindahl, Ph.D.Dept of Business Studies & Uppsala Antibiotic Center
Uppsala UniversitySweden
Overview• Who am I?
• My cross-disciplinary work in AMR– Previous research projects– Ongoing research projects– The iDX Project
• With the purpose of giving you an idea of what I/we do
• And hopefully inspire future collaboration
Antibiotic Resistance & Global Industry Decline
Innovation gap and discovery void
4L. Silver
A dying industry
Source: Graphic adapted by John Rex from Sharma & Towse, 2011
Typical view of costs and revenues in Pharmaceuticals & Diagnostics
Source: Graphic adapted by John Rex from Sharma & Towse, 2011
Reality in Antibiotics…
Previous & OngoingResearch Projects
The Beginning
DRIVE-AB (2014-2018)
Developing new economic models to stimulate
innovation, responsible use and sustainable
access of novel antibiotics to meet public health
needs.
10
The DRIVE-AB Consortium
11
The British Society for Antimicrobial
Chemotherapy
UK
Center for Anti-Infective Agents Austria
Chatham House UK
London School of Economics and Political Science UK
Norwegian Institute of Public Health Norway
Tel-Aviv Sourasky Medical Center Israel
Radboud University Nijmegen Medical Centre Netherlands
University of Antwerp Belgium
University of Geneva Switzerland
University of Heidelberg Germany
University of Lorraine France
University of Rijeka Croatia
University of Strathclyde UK
University of Tübingen Germany
Uppsala University Sweden
Wageningen University Netherlands
Astellas Pharma Europe Ltd
AstraZeneca
Cubist Pharmaceuticals
F. Hoffmann-La Roche Ltd
GlaxoSmithKline R&D
Pfizer Ltd
Sanofi-Aventis R&D
An Interdisciplinary Consortium• Medicine• Global Health• Microbiology• Pharmaceutics
• Business Studies• Innovation Management• Economics• Law
How to drive investments in antibiotic R&D?
The Solution
TARGETED GRANTS
PIPELINE COORDINATORS
MER
SUPPLY CM
Basic Science Phase I Phase II Phase III MarketPreclinical Generic market
Methods:• Model identification (n=35)• Internal evaluation• Stakeholder feedback
Companies won’t invest
In this project we tried to make antibiotics profitable
In essence: we tried to change the behavior of organizations
A “Swedish DRIVE-AB”
(2018)Evaluating incentive models for the development
of new antibiotics
E. Baraldi, F. Ciabuschi, O. Lindahl, S. Callegari
2018-2020(2021)
Value Creation and the roles of Business Actors in Pipeline Coordinators
F. Ciabuschi, E. Baraldi, O. Lindahl, S. Callegari
2018-2020(2021)Exploring the feasibility of an antibiotic susceptibility bonus for drugs to treat
Gram-negative infection
Developing New Incentives
A. Hollis, C. Morel, O. Lindahl
ASB Project Team
Calgary:
Geneva:
Uppsala:
Chantal MorelStephan Harbarthet al.
Aidan Holliset al.
Olof Lindahlet al.
An Antibiotic Susceptibility Bonus?
• AIM: Develop and evaluate a new policy incentive to support R&D in
ABx while ensuring long-term drug efficacy through sustainable use.
• Results: Strengths, weaknesses and implementability of an
“Antibiotic Susceptibility Bonus”
TimeAnnual sales revenues
R&D Cost
US$
Market authorisation
Patent expiry
PIIIPIIPI
TimeAnnual sales revenues
R&D Cost
US$
Market authorisation
Patent expiry
PIIIPIIPI
Main Questions• How measure susceptibility?
• How big a bonus?
• How ensure access to the antibiotic?
• How ensure that only useful products receive the bonus?
• Who finances it and who benefits from it?
• Is an Antibiotic Susceptibility Bonus feasible? – Or just a alluring economic construct?
2020-2023(2024)iDX: An exploration of regulatory, corporate, relational, and technical barriers to uptake of
diagnostics in the fight against AMR
Implementing Diagnostics
O. Lindahl, M. Mendelsohn, E. Dubé, V. Özenci, C. Morel,
iDX Consortium Partners
• Florence Séjourné
• Marc Mendelson
• Olof Lindahl• Chantal morel
• Volkan Özenci
• Eve Dubé
Innovation and Implementation Diagnostics for Bacterial Infection
• Combining two main strands of my previous research
• Implementation in the context of AMR – Diagnostics
• Challenging in both hospitals and clinics internationally– Problems in specific settings– But also for businesses developing them– And by extension for the Government
Innovation and Implementation Diagnostics for Bacterial Infection
• Incorrect prescription of antibiotics– Effects on patients– Effects on last-resort drugs
• Caused by unavailable or ineffective diagnostic tools
Innovation and Implementation Diagnostics for Bacterial Infection
Caused by unavailable or ineffective diagnostic tools?• Not necessarily…
• There is a void between what is developed and what is used in hospitals and clinics
• New diagnostic tools have been/are invented
• But their spread is alarmingly slow
• This is noted and is therefore affecting innovation
Innovation and Implementation Diagnostics for Bacterial Infection
Challenging in both hospitals and clinics internationally– But challenges can be expected to be both common
and different (context-specific)
Innovation and Implementation Diagnostics for Bacterial Infection
• “What are the barriers to implementation of new diagnostics for bacterial infection in different healthcare settings?”
• From both developer, regulator, and user standpoints
• Applying business and org. behavior perspectives.
Aims of the project• To identify obstacles relating to:
– The actions of firms inventing new diagnostics• E.g., the “technology-push” problem, implementation
– Differences in regulation and reimbursement• Cheaper to market follow-on products, unpredictability
of prices, hospital incentives– Culture and organizational factors in hospitals/clinics
• Importance of org. culture, attitudes, and values
– The specific context of LMICs• Laboratory infrastructure, above 3 in LMICs
Work Packages
WP1: Tech transfer and technology support– Firms, technologies, implementation
WP2: Regulation & Reimbursement– Formal rules and guidelines
WP3: Clinical Context
– Behavior in hospitals and clinics
WP4: Uptake in Rural South Africa
– Availability and use of technologies
iDX: A MAPPING EXERCISE – (UN)SUCCESSFUL CASES OF DX
Developing Firms
Regulation & Reimbursements
Hospitals & Clinics
In Europe and North America
In South Africa
Innovation and Implementation Diagnostics for Bacterial Infection
Purpose• To support the decision-making of
– businesses, – policy-makers, – hospital management and clinicians
• to remove obstacles, and• better implement new diagnostics for bacterial infection.
Innovation and Implementation Diagnostics for Bacterial Infection
Expected Findings• The business of diagnostics
– Reimbursement– Uncertainty– Markets
• Generic challenges– But also context-specific
• Implementation– Change management– Knowledge– Attitudes
Questions!
Thank you for listening!
MAGITICS - MAchine learning for diGItal diagnosTICS of antimicrobial resistance (2020-2022)
Jacques Corbeil, coordinatorCogwheel workshop One Health EJP & JPIAMRApril 28, 2020
Goal of MAGITICS
• To develop and apply new machine learning approaches for modelling AMR for faster diagnosis, better surveillance and prediction of resistance emergence
• To achieve a machine learning implementation that can• orient the selection of treatments by assessing the level of
resistance• provide rational for the generation of novel antibiotics, • and assist in the surveillance of human and livestock AMR around
the globe.
MAGITICS
The consortium bringstogether multi-disciplinaryexpertise• Machine Learning• NGS & Genomics• Metabolomics & mass
spectrometry• AMR mechanisms and
diagnostics• Experimental validation
MAGITICS
Jacques Corbeil
Medical genomics & metabolomics
AMRMachine Learning
Experimental validation
Jie FengAMR mechanisms
Evolution and epidemiology of
resistanceExperimental
validation
Veronique Dubois
AMRClinical diagnostics
Experimental validation
Macha Nikolski
BioinformaticsNGS
Machine learning
Juho RousuMachine Learning &
Metabolomics
Specific AimsAim 1: Generation of data sets to implement robust machine learning approaches• Next-generation sequencing of Pseudomonas aeruginosa
(Pa) and Streptococcus pneumoniae (Spn) with preciselydetermined Minimum inhibitory concentration (MIC). MIC isimportant for clinicians but often not present in current databasessuch as PATRIC
• Metabolomics of Pa and Spn and β-lactam resistance. Metabolomic responses to drug may reveal new mechanisms of resistance and thus aid more accurate diagnostics
Planned schedule: Q1/20-Q2/21
24.4.20204
Specific Aims
Aim 2: Creation and validation of AMR models using NGS and metabolite data sets• Predictive modelling of AMR using NGS and metabolomic
data. We will use multiple machine learning and data mining toolsto predict MIC and uncover the important variables for the prediction as well as elucidate the responding biologicalprocesses.
• Knowledge transfer and output with BacteriApps. The modelsand databases developed in the MAGITICS will be integrated to BacteriApps (https://bacteriapps.genome.ulaval.ca)
Planned schedule: Q1/20-Q4/21
24.4.20205
Specific Aims
Aim 3. Experimental validation of the models to drive amelioration of the predictive models• The predictions of the important variables for AMR
(genes,SNVs, metabolites) will be experimentally validated• The goal is to reconstruct the resistance phenotype of the
clinical isolates. • This will serve as a basis for optimisation of the ML
algorithms.
Planned schedule: Q3/20-Q4/22
24.4.20206
Proposed workflow
24.4.20207
Outputs and potential impact• Our global aim is to gain a deep understanding of AMR at a
level that will allow us to make predictions and mostimportantly, design and propose interventions.
• Our proposal is to use NGS and metabolomics data for predicting MIC instead of using standard techniques that willbe a paradigm shift if successfull.
• Our objective is to provide a data repository and analyticaltools where researchers can access our raw data and resultsin a way that conforms to the FAIR (Findable, Accessible, Interoperable, Reusable) principles.
24.4.20208
Thank you for your attention!
24.4.20209
A K-mer Based Approach for Institutional AMR Surveillance,
Transmission Monitoring, and Rapid Diagnostics
Derek MacFadden (Ottawa Hospital Research Institute, Canada)For the K-STaR Group
Allison McGeer, Mt. Sinai Hospital, Toronto;Hajo Grundmann, University of Freiburg, Germany;
Martin Antonio, MRC The Gambia.
Cogwheel Workshop – One Health EJP - April 28th, 2020
No conflicts of interest to declare.
Outline
•Background•AIMS•Methods•Potential Impact•Timeline•Questions
Background
Reardon, Nature. 2015 Olesen et al, elife. 2018
Antibiotic Resistance and Outcomes
Kumar et al. Crit Care Med 2006.
• Antibiotic resistance means delayed times to effective therapy.
• Some associations between antibiotic resistance and virulence.
• Antibiotic resistance is generally associated with:• Increased morbidity• Increased mortality• Increased hospital length of stay• Increased costs
Serra Burriell. PLOS One. 2020.
Background
A Big Issue, A One Health Issue
https://amr-review.org/
Background
Rapid Diagnostics
7
• Getting more rapid diagnostic information offers promise of:• Earlier effective treatment -> mortality/morbidity benefit.• Reduced (broad-spectrum) antibiotic use.
• Variety of different approaches: Culture, molecular, PCR, etc. • Often limited scope/limited pathogens/limited susceptibility.
• New sequencing-based approaches offer a one-stop shop.• BUT traditional NGS is too slow.• What about real-time long read sequencing?
Background
Rapid Diagnostics - MINION
8Brinda et al. Nature Microbiology. Accepted. 2020.
Real Time Long Read Sequencing with Rapid ID and Resistance Prediction
Background
Brinda et al. Nature Microbiology. 2020.
• Stable organism/genotype within 1-5 minutes
• Can predict antibiotic sensitivity• Works well for S.
pneumo• What about Gram - ?
• Can look at relatedness (infection control/outbreak management)
Background
Stewardship – Rapid Diagnostics
10
• We have a platform for rapid prediction (minutes), from primary specimens and isolates.• Can identify species, lineage/genotype, predict susceptibility,
and evaluate relatedness.• Potentially valuable tool for in/outpatient settings BUT: • The optimal use of this approach needs to be determined.• This approach needs to be validated across organisms, settings,
and regions.• This is what we have sought to do with this project.
Background
AIMSAIM1: To assemble local and international databases of whole genomes of Gram-negative organisms of global concern with associated phenotypic metadata.
AIM2: To generate local and international k-merdatabases of Gram-negative organisms of global concern that can be used for predicting antibiotic resistance phenotype as well as surveillance.
AIMSAIM3: To validate the performance of k-mer databases for predicting antibiotic resistance phenotype and genetic relatedness for infection control surveillance purposes compared to gold standard phenotype and phylogenetic relationships.
AIM4: To validate the usefulness of k-mer based approaches in the rapid identification of nosocomial transmission on high acuity units for next to real-time detection of transmission events and interventions.
Methods – Study DesignProspective Cohort Study• Samples from Colonized/Infected
Patients• High Acuity Wards/Units• Four Hospitals• Three Continents• Over 3 years
LocationsNorth America• Ottawa, Ontario• Toronto, OntarioEurope• Freiburg, GermanyAfrica• The Gambia
Methods – Sampling/Sequencing StrategyYear 1 (Database Generation): • Isolate Short Read Sequencing (Illumina) Year 2 (Validation): • Isolate Short Read Sequencing (Illumina)• Isolate Long Read Sequencing (Nano)• Specimen Metagenomic Short Read Sequencing (Illumina)• Specimen Metagenomic Long Read Sequencing (Nano)
Methods – Sampling/Sequencing StrategyGram-negative Sequencing Priority:
1. Escherichia coli2. Klebsiella pneumoniae3. Pseudomonas aeruginosa4. Enterobacter spp.5. Serratia spp.6. Acinetobacter spp.
Decreasing sample size
Methods – OutcomesSurveillance: •Maximum likelihood phylogenetic tree/distances• Pairwise genetic distances•MASH distances•MLST• cgMLST• k-mer best match/match metrics
Methods – OutcomesDiagnostics/Resistance: • Phenotypic antibiotic susceptibility• Antibiotic resistance genome loci•MLST• cgMLST• k-mer nearest neighbor• k-mer lineage prediction
Methods – AnalysisSurveillance: • Isolate and Metagenomic Short Read Sequencing (Illumina)• Construct Genetic Trees and Lineage Classifications• ML Phylogenetic tree/distances – Gold standard • MASH tree/distance• MLST• cgMLST
• Compare k-mer based placement of isolates within these genetic phylogenies/trees/lineage classifications.• Compare k-mer match metrics with quantitative measure genetic
distance.
Methods – AnalysisDiagnostics/Resistance: • Isolate and Metagenomic Short Read Sequencing (Illumina)
a) Compare AMR genotype (e.g. CARD) with phenotypeb) Compare k-mer lineage-based prediction with phenotypec) Compare k-mer nearest-neighbor prediction with phenotype
• Test characteristics of a-c. • Calculate posterior probabilities of susceptibility given prediction
results for a-c above.
Timeline
REB App ✓
Jan 2020 Jun 2020 Jan 2021 Jun 2021 Jan 2022 Jun 2022
Isolate Collection Specimen Collection
Reference DB Generation
Sequencing
Database Upload
Start Analysis
Dissemination
PENDING COVID
Knowledge Translation and Impact
21
• The results of this study should inform:• The use of our approach for predicting antibiotic resistance and
its viability as a tool for supporting antibiotic decision-making.• The use of this approach as a surveillance tool, and how it could
support hospital/community control efforts.• If both (or either) show further promise after this validation work,
this will form the basis for large prospective clinical trial(s). • Potential for significant impact and inversion of typical
microbiology workflow/clinical information stream.
QUESTIONS?
FRANK VAN LETH
ASSOCIATE PROFESSOR GLOBAL HEALTH
AMSTERDAM INSTITUTE FOR GLOBAL HEALTH AND DEVELOPMENT
28 April 2020 oasisresist.org 1
OASIS: project description
JPIAMR – OHEJP cogwheel workshop
oasisresist.org 2
• Aim and objectives project
• Rationale
• Concept of Lot Quality Assurance Sampling (LQAS)
• Project activities
• Envisioned outputs
Content
oasisresist.org 3
• Aim
– To provide evidence base for rapid AMR surveillance strategy
• Enabling transition to population-based surveillance
• In a One-Health context
• Widely applicable
• Objectives
– To test optimal implementation of dynamic LQAS-approach
– To validate LQAS-based AMR surveillance in veterinary domain
– To assess policy requirements for LQAS-based surveillance
– To build capacity for LQAS-based AMR surveillance
Aim and objectives
oasisresist.org 4
RATIONALE
oasisresist.org 5
Potential bias in laboratory-based surveillance data
Sugianli et al. PLoS One, 2020
oasisresist.org 6
Population-based surveillance: needs large sample size
oasisresist.org 7
Population-based surveillance: limited local relevance
Thor Alvis on Unsplash
oasisresist.org 8
LOT QUALITY ASSURANCE SAMPLING
oasisresist.org 9
• Derived from setting of manufacturing
– Quality assurance strategy
• Question posed:
– Is threshold for “adequate quality” met?
• Assessment on small number of samples
– From well-defined batches of goods
Lot Quality Assurance Sampling (LQAS)
oasisresist.org 10
Basic 2-way static LQAS approach
oasisresist.org 11
• Done in a classification framework
– Is the AMR prevalence above or below x%?
• Lot can be any well-defined grouping
– District
– Health facility
– Sub-population
• Classification ”high” AMR prevalence requires action
Lot Quality Assurance Sampling (LQAS)
oasisresist.org 12
• Threshold setting (what is high?)
– Setting
– Possibility to implement action
• Misclassification
– True “low” classified as “high” prevalence (and vice versa)
• How much is allowed?
• Equal weight?
Statistical parameters
oasisresist.org 13
Typical sample sizes
LQASdefinition
SampleSize
Decisionrule
MisclassL as H
MisclassH as L
MisclassTot
2 - 10 76 4 6.6 4.7 11.3
5 - 20 44 5 6.7 4.4 11.1
10 - 20 112 16 9.2 4.7 13.9
10 - 25 55 9 9.5 4.5 13.9
15 - 25 139 27 9.3 5.0 14.3
15 - 30 70 15 9.4 4.1 13.5
oasisresist.org 14
OASIS ACTIVITIES
oasisresist.org 15
• Population-based AMR-surveillance
– Participants suspected of UTI
– Primary care setting Togo / Burkina Faso
• Dynamic approach feasible?
– Rapid turn-around laboratory testing
– Rapid communication of results
• Extension of earlier work in Indonesia
– Static approach
1. Dynamic LQAS-based AMR surveillance human domain
oasisresist.org 16
Static versus dynamic LQAS-approach
oasisresist.org 17
• Conventional AMR survey as reference standard
– Broiler chickens
– Fattening pigs
• Lots defined by truck from single farm at slaughterhouse
– Batch sampling manure
– No mixing livestock
– No mixing farms
2. Validation static LQAS-approach veterinary domain
oasisresist.org 18
Lots in human and veterinary domain
oasisresist.org 19
Validation: combine lots in conventional estimate
1
2
3
4
5Estimated prevalence
Measured prevalence
oasisresist.org 20
• Qualitative research
– Stakeholder involvement
• Define parameters
– Knowledge utilization strategies
• LQAS-based AMR surveillance as policy of ASPs
3. Identify optimal implementation strategy
oasisresist.org 21
ENVISIONED OUTPUTS
oasisresist.org 22
• Validation of LQAS-based AMR surveillance
– Dynamic approach in human domain
– Static approach as proof-of-concept in veterinary domain
• Capacity for LQAS-based AMR surveillance
– Trained staff
– Laboratory infrastructure
• LQAS-based AMR surveillance as policy
– Stakeholder involvement
– Knowledge utilization
Outputs
oasisresist.org 23
CONSORTIUM
oasisresist.org 24
Partners
oasisresist.org 26
Funders
Building on the WHO tricycle project: JPI TRIuMPH
Heike Schmitt
This talk JPI TRIuMPH: ● building on the WHO Tricycle protocol ● Just started (not yet in all countries) ● Therefore: ● Background: WHO Tricycle project
● JPI TRIuMPH
2
Context – WHO AGISAR group
3
● WHO Advisory Group on Integrated Surveillance of Antimicrobial Resistance (AGISAR) – Support WHO on resistance from the food chain – Integrated surveillance – Capacity building (on surveillance) – Define critically important antimicrobials – Support WHO in FAO/OIE/WHO tripartite activities and Codex
WHO Tricycle project on One Health surveillance ● Started out of the AGISAR group
Idea: ● Develop a simplified, integrated surveillance system for bacterial
resistance to antibiotics, to be implemented on a global basis ● Simplified: Based on ESBL-producing E. coli as indicator ● Integrated: One health approach including humans, food chain and
the environment
4
Why ESBL E. coli?
● Simplification: one species / antibiotic resistance
● Large variations between regions and in time
Woerther, 2013
One Health surveillance
• Bloodstream infections
• Healthy adults: pregnant women
• Chicken
• Human wastewater • Animal (slaughter)
wastewater • Surface water (river
up- and downstream of the city)
Capacity building ● Training workshops:
– Netherlands 2017, 2018 / Indonesia 2017 / Jordan 2019
7
● Country visits
Results pilot testing Outcomes • Protocol implemented in all countries • Costs are moderate (30 000 $ reagents / year) • Led to inter-sectoral interaction, triggers One Health thinking
Requirements • WHO has critical coordination role
Opportunities • Tricycle to be linked to GLASS • Implementation in more countries
8
Outcomes pilot – building on.. Ideas of pilot countries: • Additional endpoints, depending on countries’ interests • Interpretation of WGS data • Making use of existing sampling networks
JPI TRIuMPh
• Building on the TRIcycle protocol: upscaling to national
Monitoring, detection of CPE and WGS pipelines for One Health Surveillance
• Joint initiative of pilot countries and labs involved in method establishment
9
• H Schmitt, National Institute for Public Health and the Environment (RIVM) (coordinator / methods – environment / link to wastewater surveillance)
• L Armand-Lefevre, University Paris-Diderot (methods – human) (and Fondation M’erieux)
• J Wagenaar, Utrecht University (methods – animals / WGS pipelines) • R Hashim, Institute for Medical Research, Malaysia • M Salman, National Institute of Health, Pakistan • L Samison, University of Antananarivo, Madagascar
• J Matheu Alvarez, WHO Headquarters • More institutions within Malaysia and Pakistan
Consortium
10
Project structure
11
CPE Protocol development Tasks: ● Method development for all compartments Principles: ● Unified across compartments (if possible) ● Globally applicable
12
CPE Protocol development Method ● Direct culture from swab (human, animal) vs enrichment ● Quantification by membrane filtration (environment) Culture medium – first choices: ● MacConkey (human / animal surveillance) ● TBX / EEC (water) CPE selection ● Stability matters ● Different antibiotics, also as disks
13
Protocol development – difficulties ● Global availability, price restrictions ● MacConkey instead of chromogenic selective media ● But: strong differences between suppliers
14
WGS pipelines Tasks ● Gathering suitable tools ● Developing training material, conducting
trainings ● Joint data analysis Principles ● Open source or freely available ● Stably maintained ● Web accessible resources ● So far, focus on E. coli ● E.g. cgMLST typing, resistance genes,
virulence genes, plasmid sequence recognition
15
National roll-out Tasks ● Repeat Tricycle protocol ● Add CPE protocol ● In 2 regions within the participating
countries ● study trends and regional
differences Principles ● Train-the-trainer approach ● Local languages where needed
16
Wastewater-based surveillance Tasks ● Include samples retrieved within other
monitoring networks in Tricycle / TRIuMPH
● Specifically, polio environmental surveillance samples
● Test ESBL and CPE in these samples ● Validate WBS study regional differences Principles ● Focus on larger communities
17
Future.. ● CPE protocol development ● WGS pipeline development
● Train-the-trainers for regional extension
● One year sampling campaign
● Joint data evaluation
18
Questions?
This presentation is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
ast Antimicrobial Resistance and Mobile-Element Detection using metagenomics for animal
and human on-site tests ( ARMED)
Project Lead: Manal AbuOun (APHA)
Deputy lead: Mike Brouwer (WVBR)
F
F
ARMED Consortium
Expertise within the consortium of
9 Institutes, 7 countries
• Public and Animal Health
• Antimicrobial resistance
• Plasmid Biology
• Whole genome sequencing
• Long read sequencing
• Metagenome sequencing
• Bioinformatics Analysis
F
Development of diagnostic tool for
early, real-time and on-site detection
of antibiotic resistant bacteria in
humans, animals, foodstuff or the
environment
WP 3 - Implementation of on-site protocols for long-read metagenomic DNA sequencing
• Develop harmonised protocols for ‘on-site’ extraction of DNA from sample matrices, to enable long-read sequencing• Test ONT automated DNA extraction device, VolTRAX system
• Develop a harmonised protocol for ‘on-site’ DNA sequencing library preparation.
• Deploy protocols developed in WP1, WP2, WP3 for their use on-site, within clinical and veterinary settings.
ARMED ObjectivesF
WP2 - Bioinformatics tools to analyse the sequencing data and define the characteristics within the sample.
• Establish best practice ‘off-site’ bioinformatics analyses.
• Develop efficient real-time mapping strategies to identify the origin of the genetic context • AMR genes, Microbial profiling and typing
WP1 - Assess feasibility of long-read metagenomic sequencing
• Compare metagenomic sequencing using long and short-read technologies from human, animal, food and environmental sample matrices.• Does long read sequencing (MinION) provide sufficient depth of sequencing to detect AMR and pathogen?
• Does combining Hi-C sequencing with long-read sequencing data to gain insights into AMR and bacterial species linkage, within the metagenome?
armed work plan
• Development of diagnostic tool for early and real-time detection of antibiotic
resistant bacteria in humans, animals, foodstuff or environment
• Evaluation of on-site testing
F
Feasibility of long-read
metagenome sequencing
(WP1 – T1, T2, T3)
Hi-C metagenomic
sequencing
(WP1-T4)
Literature review on DNA
extraction methods
(WP3 – T1)
Tool for establishing microbial
community composition
(WP2 – T1, T3)
Tool for establishing presence
of AMR genes
(WP2 – T2, T3)
Protocol for on-site DNA
extraction
(WP3 – T3)
Protocol for on-site Voltrax
DNA extraction
(WP3 – T2)
On-site DNA extraction and long-
read sequencing from
metagenomic samples
(WP3 – T4)
Protocol for on-site long-read,
sequencing and analysis
(WP3 – T2, T3)
Annual communication to stakeholders
&
OH EJP coordinators
(WP4 – T4)
Training workshop
(WP4 – T3)
• Bioinformatic analysis• Develop tools that can be used ‘on-site’
• Establishing ‘off-site’ bioinformatic standards for the analysis of short and long-read metagenomics sequencing data
ARMED: Expected One Health impactF
• Molecular methods• DNA extraction techniques and/or targeted enrichment of
metagenomic samples on-site
• Integration of these techniques into standard practice.
• Rapidly testing the metagenome from different settings
• On-site metagenomic sequencing will revolutionise the battle against pathogens and AMR
• Provide the clinician or veterinarian with real-time AMR profile to aid treatment options
This presentation is part of the European Joint Programme One Health EJP. This programme has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
Thank you for your attention!
This presentation is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
Full-length sequencing for an enhanced EFFORT to map and
understand drivers and reservoirs of antimicrobial
resistance(FULL_FORCE)
Pieter-Jan Ceyssens
JRP – AMR 2.2
[email protected], November 13, 2019
• Responsible Partner: SCIENSANO
• Contributing partners: ANSES, INSA, BfR, DTU, APHA, INIA, INRA, ISS, PIWET, PHAS, SSI, RIVM, SVA, WbvR, NVI (+ UU, IZSLT, UG)
Consortium
• More and more countries rely on WGS for surveillance of AMR
• Large sequence collection were generated in COMPARE, EFFORT, ENGAGE, and other projects
• Mobile Genetic Elements (MGEs) often encode resistance to critical antibiotics, and can spread within or across species and genera
• MGEs remain challenging to reconstitute due to their chimeric, modular and repetitive nature.
Background
Solution : Long-read sequencing & Hybrid assemblies
WHAT NATURE THINKS IT LOOKS LIKE
HOW REALITY LOOKS LIKEHOW THE ACTUAL DATA LOOKS LIKE
WHAT THE COMPANY SAYS IT LOOKS LIKE
Full Force : Objectives
Expected One Health impact
• Capacity building for SMRT sequencing across EU
• To dissect clonal from horizontal transmission of AMR genes by broadly introduce single
molecule real-time (SMRT) sequencing in EU veterinary and public health institutes
• To apply this knowledge on six study cases, incl. samples isolated in the context of
national surveillance programmes, EFFORT and ARDIG projects.
• Hopefully : levelling the field for a future strategy for EU AMR surveillance, including
effective integration of MGE tracking in veterinary and public health
This presentation is part of the European Joint Programme One Health EJP. This programme has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
Thank you for your attention!
This presentation is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
LISTADAPT- An update(Adaptative traits of Listeria monocytogenes to its diversse
ecological niches)
(Institute logo)
Listeria Monocytogenes
Ubiquitous saprophyte & facultative intracellular pathogen
Widely distributed in the environment Soil, water, vegetation
Manufacturing environment persistent strains that have found a
harborage site within the facility where it may reside for years
Capable of growing at T < 4°c
Still a problem for public health
High mortality rate (12,7%) (EFSA 2019)
EFSA’s opinion of the increase incidence of listeriosis at EU-level (EFSA
2018)
Performant epidemiological investigation (Moura et al., 2016)
Knowledge population structure in food (Henri et al. 2016, Maury et al., 2016) , in human (Maury et al., 2016)
Knowledge on virulence: role of CC (strain-specific virulence difference) (Maury et al.,
2016)
Far less is known on adaptation in environment, farm, industry (despite numerous
investigation on persistence in industry)
Partial knowledge about the genetic factor linked to adaptation to the different
ecological niches of Listeria
State of the art
4 Objectives
1. Decipher genetic traits linked to adaptation in various
ecological niches
2. Generate a database of 3000 genomes (mainly farms
environment, wild animal, food and human)
3. Develop and implement cutting-egde methodologies
(new phenotypic analysis, GWAS)
4. Make available common resources for European
research
LISTADAPT: Adaptive traits of Listeria monocytogenes to its
diverse ecological niches
LISTADAPT
Which genes are involved in the adaptation of
Listeria monocytogenes to
its diverse ecological niches
WP1-Constitution of
the straincollection
WP2-WGS Consitution of the genome
collection
WP3-Phenotypic
tests
WP4-Genetic traits
underlyingadaptation
WP5-Training, Dissemination
Valorisation
Sequences
Strains from farm- and- wide animals
,
)
)Bear (IT)
LISTADAPT consortium partners
6 European countriesAT, CZ, IT, FR, NO, SE
Search for externalpartners
8 European countriesCZ, IT, FR, CH, ES, FI, PL, SI
Strain collections published :
DE, ES, FI, FR SI
Strain collections not published :
BE, DE, CZ, EE, FI, LT, NL, PT, SK
Strains
• Wild and farm Animals (725 strains)• Wild and fariming environement (222 strains)• Food (776 strains)
)
)
MLST clonal complex
Animal and
environmental
strains
Food strain
Difference ENV-
FOOD
CC
121
1,3
26,2
-24,9
CC
9
2,6
14,4
-11,8C
C2
2,1
5,5
-3,4
CC
8
4,0
7,1
-3,0
CC
5
2,2
4,8
-2,6
CC
3
0,8
2,8
-2,0
CC
31
0,4
2,4
-2,0
CC
204
0,5
1,9
-1,5
CC
155
0,9
2,2
-1,4
CC
193
0,0
0,5
-0,5
CC
101
0,6
1,0
-0,4
CC
207
0,2
0,3
-0,1
CC
218
0,0
0,0
0,0
CC
88
0,0
0,0
0,0
ST38
2
0,0
0,0
0,0
ST83
9
0,0
0,0
0,0
CC
870,2
0,2
0,0
ST12
40,1
0,0
0,0
CC
226
0,1
0,0
0,1
ST51
7
0,1
0,0
0,1
CC
288
0,1
0,0
0,1
CC
195
0,1
0,0
0,1
ST30
7
0,1
0,0
0,1
ST11
10
0,1
0,0
0,1
CC
475
0,1
0,0
0,1
ST66
3
0,1
0,0
0,1
CC
363
0,1
0,0
0,1
ST42
9
0,1
0,0
0,1
CC
19
0,5
0,3
0,2
CC
90
0,2
0,0
0,2
CC
517
0,2
0,0
0,2
ST22
6
0,2
0,0
0,2
CC
663
0,2
0,0
0,2
CC
573
0,2
0,0
0,2
CC
570
0,2
0,0
0,2
ST66
6
0,2
0,0
0,2
CC
361
0,2
0,0
0,2
ST20
0
0,2
0,0
0,2
CC
379
0,2
0,0
0,2
ST48
9
0,2
0,0
0,2
ST58
5
0,2
0,0
0,2
CC
224
1,4
1,1
0,3
CC
415
0,4
0,1
0,3
ST32
0,3
0,0
0,3
CC
70
0,3
0,0
0,3
ST21
3
0,3
0,0
0,3
CC
403
0,4
0,1
0,3
CC
77
1,5
1,2
0,3
CC
26
1,1
0,8
0,3
CC
6
5,1
4,8
0,3
CC
217
0,4
0,0
0,4
CC
398
0,4
0,0
0,4
CC
54
0,7
0,2
0,5
CC
22
0,5
0,0
0,5
CC
177
0,5
0,0
0,5
CC
199
0,8
0,2
0,5
Un
kno
wn
0,5
0,0
0,5
ST19
1
0,5
0,0
0,5
CC
388
0,9
0,4
0,6
ST18
4
0,6
0,0
0,6
CC
124
0,6
0,0
0,6
CC
89
0,7
0,0
0,7
CC
220
0,9
0,1
0,7
ST36
0,8
0,0
0,8
CC
59
2,2
1,4
0,8
CC
315
1,0
0,0
1,0
CC
11
1,9
0,7
1,2
CC
14
3,7
2,4
1,3
CC
20
2,5
1,1
1,4
CC
412
1,7
0,3
1,4
CC
29
2,0
0,3
1,7
CC
18
3,3
1,5
1,8
CC
21
3,0
0,8
2,2
CC
7
3,7
1,3
2,3
CC
451
3,0
0,0
3,0
CC
37
7,2
2,6
4,6
CC
4
7,5
2,6
4,9
CC
1
18,3
6,1
12,2
Genetic diversity results
Distribution study of the CCs between food and animal and environment compartments
Marked distribution of CCs between both compartments
Higher diversity observed in the environment and animal compartment
Some CCs like CC6, CC26, CC77 and CC224 were observed in equivalent proportion (p-value > 0.1)
191 strains
Isolated from
1987 to 2018
C2 - 96 strains
RTE food
Fish food products (25)
CCs: 121, 9, 155, 2, 8, 6
Country: FR, SW, CZ
Year: 2002-2015
RTE meat products (23)
CCs: 121, 9, 1, 2, 8, 6
Country: FR
Year: 2005-2014
Dairy products (25)
CCs: 1, 2, 21, 217, 37, 4, 6, 7
Country: FR, SI
Year: 2005-2017
Other foods (including composite dished and
vegetables) (23)
CCs: 1, 121, 2, 37, 6
Country: FR, UK
Year: 2005-2017
C1 - 95 strains
Environmental/
animal
Cattle (healthy/ill) (14)
Farm Animals
CCs: 11, 412, 77, 54, 59, 1, 9, 315, 2, 14, 451, 8, 20, 29, 26, 220, 224, 5, 412, 36
Country: FR, CZ, LV, NL, SI, SP, SW, UK, FI
Year: 1987-2018
Sheep/goat (healthy/ill) (11)
Swine (2)
Game and wild animals (32)
Wild Animals
CCs: 1, 121, 7, 11, 21, 59, 4, 26, 412, 220, 31, 315, 14, 29, 36, 451, 4, 37, 9, 18, 184
Country: CZ, DE, FI, IT, NO, SE, SI
Year: 1998-2018
Farm and natural envs(water, meadow, soil)
(36)
CCs: 1, 11, 121, 14, 18, 20, 21, 220, 26, 37, 4, 451, 5, 54, 59, 6, 7, 77, 8
Country: CZ, DE, FR, IT, PL, SW, UK, FR, AT
Year: 2004-2017
From the top 6 CCs found in each category
From the most prevalent CCs in env
(WP4 task 1)
Strains selection from the LISTADAPT collection
Phenotypic characterization
1. Antibiotic and biocide resistance profiles
2. Adaptation to biocides
3. Strain adhesion and biofilm formation
4. The effects of biocides on strains in biofilm
5. Survival and persistence of strains in differentecological niches
-In food products and gastro-intestinalenvironment
-In soil microcosm
Strain1
Strain2
GWAS tools Assembly and annotation of the
genome (Artwork)
ROARY (V. 3.12.0)
Correlate genome annotation with
sequence
Gene presence absence matrix
Determination of the core and pan
genome
Differentiation of variants of a same
gene
Scoary (V. 1.6.16)
From the pan genome, calculate
the association of a gene to a
phenotype.
Pyseer (V. 1.3.4)
Association based on variable
length k-mer
No specific genetic factor can be associated to soil survivability in the whole collection
No specific factor in the lineage 1 and 2
Soil survivability in Listeria have multiple genetic factor that are associated with the different CC.
Strain of the phenotype 1 contain additional genes absent in phenotype 3, mostly phage related genes.
GWAS results on soil survivability
Phenotype 3: more than 5%
Phenotype 1: less than 2%
Phenotype 2: 2% < survival rate < 5%
Presence of a large phage insertion (17 genes) in the strain from phenotype 1.
this phage contain 7 proteins matching the phage LP-030-3.
This phage is absent from the strain of phenotype 3
This phage in integrated in the genome inside the comK locus. It has been
show to have significant impoact on stress resistance (rabinovitch, 2012)
The transcritionnal regulator BlgG is inactivated by an insertion of 8 “T” and
splinted into 2 separate genes (MtlR_4 and cmtB_2)
Vegetables: the phenotype 3 present several large cluster of genes
corresponding to transposon (plasmid and transposon TN3)
Gwas on soil survival
Investigate phage diversity at the comK locus
Functional analysis of associated CDSs
Detailed screening of plasmids and transposons
Test phenotype prediction based on phylogeny
Gwas on the biofilm formation data and intestinal tract survivability.
Perspectives
Special thanks to
Anses Salmonella & ListeriaBenjamin FelixYann Sevellec
LISTADPT consortium partnersAGES (AT)VRI (CZ)INRA (FR)NVI (NO)ANSES (FR)IZSAM (IT)SVA (SE)DTU (DK)
External partnersVetSuisse (CH)Munich University (DE)Freie Universität of Berlin IHMT (DE)VetLab (EE)NEIKER (ES)University of Helsinki (FI)French Institute for Pig Industry (FR)Veterinary faculty of Zaghreb (HR)BIOR (LT)VWA (NL)Szczecin University (PL)INIAV (PT)Veterinary faculty of Ljubjana (SI)Public Health England (UK)
Ploufragan laboratoryAnses genomic plateform
Yannick BlanchardPierrick Lucas
Fabrice TouzainAurélie Leroux
This presentation is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
MATRIX: CONNECTING DIMENSIONS in ONE-
HEALTH SURVEILLANCE
Esther M. Sundermann, BfR
(WP 5 leader)
• A Joint Integrative Project (JIP) under the EJP One Health
• Aims to:
Establish a practical roadmap for adopting One Health Surveillance
(OHS) by building onto existing surveillance structures or
implementing new structures, tailored to different levels of financial
and infrastructural conditions
• Does not aim to:
Describe OHS, harmonize OHS, propose ‘one-size-fits-all’ scenario,
collect or analyse data, work across borders…
MATRIX
• A roadmap to future develop or implement national OHS activities
• The roadmap considers different levels of infrastructural and
economic capacities
• The roadmap is based on a self-assesment of OHS capacities
(EUEpiCap tool)
Roadmap
Input & output
MATRIX
ECDC & EFSA
- Databases
- Projects
- Knowledge
- Guidelines
ORION
- Inventories
- Glossary
- Knowledge Hub
COHESIVE
- Data collection& analysisplatform
- Guidelines
National OHS (full structures/ components)
Overview of
surveillance frameworks
and identification of best
practicesEUEpiCap tool:
characterise OHS capacities
Roadmap to further develop or
implement OHS (at different
levels of infrastructure and
economy)
Pathogen-specific tracks: Campylobacter, Salmonella, Listeria, Emerging Threats (e.g. AMR)
THANK YOU FOR YOUR ATTENTION!
This presentation is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
One Health Harmonisation of Protocols for the Detection of Foodborne
Pathogens and AMR Determinants
OH-Harmony-CapHarmonised protocols and common best practice
Nadia Boisen
[email protected] April 2020
Cogwheel Workshop
• Project leader: Nadia Boisen (SSI)
• Deputy leader: Flemming Scheutz (SSI)
• 15 partners across 11 countries
• Project group: ANSES, INSA, ISS, RIVM, SSI, and Teagasc
Consortium
Overview
• Collect laboratory information on current capabilities,
capacities and interoperability
• Quantitative description of current and best practices and
the development of harmonised protocols
• Identify and possibly close the gaps and suggest future
studies of how best to detect and characterise food borne
pathogens across the OH sectors.
OHLabCap laboratory
interoperability
guidance
harmonised
protocols
Increase the
EU capacityWP2 WP3 WP4 WP5
Objectives
Develop a Benchmarking Instrument OHLabCap
• Overview and general description of the microbiology
system in the OH-field
• Survey OH laboratory capability, capacity and
interoperability
• National Reference Laboratories and Primary diagnostic
laboratories
• Detection and typing of priority foodborne bacterial
pathogens, parasites, and AMR for Salmonella and
Campylobacter
WP2
Objectives
One Health laboratory interoperability
guidance for model organisms
Propose new studies and methods
a) The best-practice in sampling and testing
b) Characterisation of methods
c) Data management of harmonised reporting
WP3
Objectives
Design harmonised protocols for model
organisms
• Collect and assess the laboratory protocols
• Design harmonised protocols upon review of the
collected protocols and literature
WP4
Objectives
Increase the EU capacity to deal with foodborne
zoonoses, antimicrobial resistance and emerging threats
across OH interface
• Workshop for communicating and discussing the
outcome of the OHLabCap survey
• A joint meeting between CARE, OH-Harmony-Cap, and
MATRIX
• Practical workshops and e-learning activities dedicated
to the harmonised protocols
WP5
This presentation is part of the European Joint Programme One Health EJP. This programme has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
Thank you for your attention!
This presentation is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and
innovation programme under Grant Agreement No 773830.
ORION Knowledge Hub
webinar
Estíbaliz López de Abechuco,
Matthias Filter
28.04.2020
The JIP Project “ORION”
One health surRveillance Initiative on
harmOnization of data collection and
interpretatioN (ORION)
3 years (2018 – 2020)
13 partners from 7 countries
ORION’s Mission Statement
The ORION project aims at establishing and
strengthening inter-institutional collaboration
and transdisciplinary knowledge transfer in the
area of surveillance data integration and
interpretation, along the One Health (OH)
objective of improving health and well-being.
ORION’s Work Plan
Animal health surveillance
Public health surveillance
Food safety surveillance
Shar
eK
NO
WLE
DG
E
DA
TA
inte
rop
era
bili
ty
REPORTING
The OHS Codex Framework
The OHS Codex „Umbrella“
Codex document: https://oh-surveillance-codex.readthedocs.io/en/latest/index.html
The OHS Codex – a living resource
Codex document: https://oh-surveillance-codex.readthedocs.io/en/latest/index.html
This presentation is part of the European Joint Programme One Health EJP. This programme has received funding from the European Union’s Horizon 2020 research and
innovation programme under Grant Agreement No 773830.
Thank you for your attention!
This presentation is part of the European Joint Programme One Health EJP. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
#TOXOSOURCES Toxoplasma gondii sources quantified
Pikka Jokelainen
DVM, PhD
Project leader
Twitter: @PikkaJokelainen
Statens Serum Institut,
Denmark
Parasitesand antimicrobial resistance
Toxoplasma gondii
High disease burden worldwide and in Europe (WHO-FERG; Havelaar et al, 2015)
North: Denmark (DTU, SSI) Sweden (SVA, SLV), Norway (NVI, FHI)
East: Poland (PIWet), Czech Republic (VRI, NIPH)
South: Italy (ISS), Spain (VISAVET-UCM), Portugal (INSA, INIAV)
West: The Netherlands (RIVM, WBVR), France (ANSES),
Germany (BfR, RKI, FLI), UK (UoS)
External: Latvia (BIOR), Ireland (CVRL), Norway (NMBU),
France (BRCT and NRCT), Greece (AUTh),
Romania (UASVMCN), China (HZAU, JLU)
TOXOSOURCES - Toxoplasma gondii sources quantified
Joint Research Project within H2020 OneHealth EJP
Project Leader: Pikka Jokelainen, SSI, Denmark; [email protected]
Co-Leader: Joke van der Giessen, RIVM, the Netherlands
#TOXOSOURCES: What are the relative contributions of the different sources of T. gondii infections?
Different approaches, including modelling and new methods
This presentation is part of the European Joint Programme One Health EJP. This programme has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No 773830.
Thank you for your attention!