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Project ID: 602860
Funded under: FP7-HEALTH
Country: Netherlands


Project Context and Objectives:
In the past 70 years, antibiotics have been one of the most important weapons against infectious diseases. Unfortunately, they are now one of the most misused drugs in the world. Importantly, this misuse has led to the development of a wide range of antibiotic resistances, representing one of the major threats to global health.
A major factor in helping to prevent the development and spread of antibiotic-resistant bacteria is appropriate antibiotic treatment that is tailored to the pathogen (if any) that is actually causing the disease. However, one of the major problems facing clinicians is deciding which (if any) antibiotic therapy should be prescribed in the 12–48-hour period before the causative agent of the infection is identified. Furthermore, patients may die if they are prescribed incorrect antibiotic therapy or if no antibiotic therapy is given. On the other hand, the indiscriminate use of antibiotics, for example in treating viral and fungal infections that do not respond to antibiotics, may lead to the development of antimicrobial resistance, as well as causing unnecessary side-effects. A method to help clinicians to tailor antimicrobial prescribing to individual patients would help reduce the development of antimicrobial resistance and unwanted side-effects associated with unnecessary treatment.
The European Union-funded, 4-year TAILORED-Treatment (TTT) project aims to establish a broad-based strategy (not limited to a particular infection) that can be used to increase the effectiveness of antibiotic treatment, reduce potential side-effects of therapy, and help to limit the emergence of antimicrobial resistance in hospitalised children and adults. At the heart of the TTT project is a clinical study that involves hospitalised patients with respiratory tract and/or bloodstream infections, including both children and adults. State-of-the-art diagnostic techniques will be utilised to generate transcriptomic, proteomic, genomic, and microbiome data, which will be subsequently assembled into a single database. This database will then be used to identify novel interactions that characterise both patients and their infections, in order to discover new biological markers of infection and to develop new computer tools that will enable clinicians to tailor in an appropriate and effective way antimicrobial therapy to the individual patient.

Project Results:
Workpackage 1. The management of all the managerial, financial, legal and organizational issues associated with the TTT Consortium is progressing smoothly.
Workpackage 2. Ethical approvals for the recruitment of both children and adult patients have been obtained from the Institutional Review Boards (IRBs) in Israel and the Netherlands. For the Israeli sites, no approval for the use of DNA for GWAS was obtained before the end of the patient recruitment. Therefore, due to the limited time period of the study, GWAS results are restricted to patients from the Netherlands. UMCU, Hadassah and MeMed have now shipped all of their collected samples to the respective partners. Hadassah has shipped part of their samples and will ship the other part in short term.
Workpackage 3. An initial set of 177 blood samples (EDTA tube collection) was obtained from UMCU. The standard (desalting) DNA isolation protocol resulted in 159 samples with high quality (A260/A280 between 1.6 and 2.1) and high yields (13 – 379 µg). The 18 samples that dropped out had a very small or absent volume of blood and were obtained from babies. Samples were run on Infinium Multi-Ethnic Global arrays and 150 passed QC (ongoing). A Haplotype Reference Consortium (HRC) imputed dataset will be generated. All GWAS samples have now arrived for GWAS processing at partner Erasmus MC.
Workpackage 4. MeMed developed a unique bioinformatic platform, which provides various computational tools required for the screening, downselection, and the combination of biomarkers. The new MATLAB-based platform is able to receive multiple datasets (clinical, proteomic, tanscriptomic), and offers a wide range of statistical tools for database interrogation (e.g., multi variable queries, feature selection algorithms, performance analysis).
Workpackage 5. Partner EMC has now received all nasal swab samples from partners UMCU, MeMed and HAD. An initial 16S rRNA gene sequencing run has been performed and also a protocol for sequencing low-density microbial communities published. In addition, we developed MYcrobiota, which is a standardized end-to-end analysis pipeline for 16S rRNA gene sequencing analysis using the mothur tool suite within Galaxy. The MYcrobiota workflow will be applied to analyse all 16S rRNA gene sequencing data obtained within the TTT project.

Workpackage 6. UGOT performed comprehensive literature and genomic database surveys and compiled an initial list of protein markers for: 1) species and strain identification; and 2) antimicrobial resistance; in relevant bacterial respiratory pathogens (i.e., S. pneumoniae, H. influenzae, M. catarrhalis, S. aureus, etc.). The MS-proteomics is being optimised for: 1) rapid pathogen identification; and 2) detection and analysis of expression of antimicrobial resistance factors (e.g., CTX-M/OXA/SHV/TEM, ampC, carbapenemases and porins). Pure culture strains and mixes of microorganisms have been used to establish and optimise the methodology before proceeding to analyses of clinical specimens. A database containing the proteomic markers of microbial pathogens of respiratory infections is being developed, based on an established database with more than 2,500 antibiotic resistance genes, representing all major resistance mechanisms. LC-MS/MS and LPI proteomics-based rapid pathogen detection is being optimized, with respect to sensitivity, specificity, etc., and applied directly to the analyses of TAILORED-Treatment clinical samples, i.e., without prior cultivation and isolation of microbial pathogens. Mock specimens have been provided to UGOT by partners UU and MeMed; analysis of clinical specimens is being performed with the objectives of achieving rapid identifications of microbial pathogens and relevant antimicrobial resistance factors.
Workpackage 7. NorayBio has developed both the database and a web based system to manage and exploit all of the data. The system developed, called HOPOIT, is made of 3 parts: 1) A central relational database that stores and serves all the data produced in the project, 2) A web platform that includes data management, exploration, analysis and visualization functionalities and 3) A local application that manages all the internal processes, like file parse and dump, quality control, dataset creation or analysis threads management. It integrates the analysis tools. We have developed three types of tools: 1) Variables selection tools, 2) Data mining algorithms and 3) Exploratory tools. The variable selection and data mining tools have been codified in C#, bundled in dlls (dynamic-link libraries) and tested individually. Next, they will be integrated in the web platform with the HoPOIT database to be used to mine the data produced in TAILORED-Treatment project. A web platform has been developed to integrate the previously developed database and the analysis tools. It includes user-friendly visualization tools to ease the use of the analysis tools and the interpretation of the results for physicians and data access control in order to protect the confidentiality of the data while enabling data sharing to the scientific and medical community.

Workpackage 8.
The objective of this task is the development of the algorithm that will guide the clinical decision on patients with respiratory diseases. In order to build this algorithm, the HoPOIT data base built in WP7 is populated with the data coming from WP2 to WP6 and then mined with the integrated data mining tools. Variables with predicted power, rules or patient groups will be selected. This information is used in this task to train models. We also designed and developed a modeling framework to allow building models, evaluate their accuracy and use them for prediction. This framework is made of the following components and algorithms: 1) Data Preparation module, 2) Modeling module, 3) Validation module and 4) a Prediction functionality. Finally, we have designed the application that will integrate the predictive algorithm. A module to manage previously obtained predictions has been also developed. The first version of the DSS has been integrated in the Modeling framework of HoPOIT.

Workpackage 9. Members of the SAG are attending the annual meetings to offer relevant suggestions and advice as to the direction of the project. At the Month 24 meeting held in Gothenburg, Prof. Eva Medina (Head of Infection Immunology Research Group, Helmholtz Center for Infection Research, Braunschweig, Germany) presented an interesting overview of the work of her research group on host-pathogen interactions and how it related to the work of the TTT consortium, and she provided helpful comments post-meeting. Dr. Zohar Yakhini (Department of Computer Science, Technion, Haifa, Israel) attended the Month 36 meeting in Utrecht and provided valuable inputs into several areas of interest to the TTT consortium. The TTT Project website contains a restricted portal for the Group Leaders to access the results deposited in the databases. Significant progress has been made in the standardization of protocols for the collection and processing of clinical samples across the consortium. Standardized eCRFs are used by all the clinical partners to enter the clinical data for the patients in the HoPOIT database. Planning for the International Workshop / Conference towards the end of the project (June or July 2017) is well underway. Various formats and venues have been discussed among the partners and a final decision was made in September 2016. During the reporting period, IBEX produced and distributed the TTT Newsletter, which serves as a source of information for the partners on what progress is being made and which problems have arisen that require actions. The Newsletter has also highlighted the individual achievements of the groups, in terms of awards received and papers accepted for publication.

Potential Impact:
The burden of antibiotic resistance due to bacterial infections can be measured in terms of the increased risk of death in patients infected with antibiotic resistant strains. The number of deaths of patients attributable to antibiotic resistant infections is estimated to be 25,000 per year in the EU. Other costs are measured in terms of longer stays in hospital for treatment, with associated costs and days lost to the global workforce. Overall societal costs are estimated at 900 M€ in additional hospital costs and more than 1.5 B€ overall, due to antibiotic-resistant infections. With the lack of development of new antimicrobial drugs, there is an urgent need for more effective diagnostic systems to be put into place for dealing with this epidemic. The novelty of the TTT project lies in the departure from the traditional diagnostic methodologies that rely upon microbial isolation and phenotypic analyses of antibiotic sensitivity. These methods currently require many hours until a final diagnosis is achieved. In contrast, though not requiring microbial culture, nucleic acid amplification technologies (NAAT) are limited due to their high levels of specificity, which means that a potential pathogen will not be detected unless a specific fragment characteristic of that species/strain can be amplified from the clinical sample.
A major innovative aspect of the TTT project is the identification and validation of unique protein- and RNA-based signatures for differentiating between patients who present with putative bacterial, viral or fungal infections. These unique signatures may or may not be combined with patient clinical information to generate potent tools for assisting the physician in making decisions regarding the type and timing of antibiotic administration. Such tailoring of antimicrobial treatment will reduce disease duration and potential complications in patients with bacterial infections and will also reduce antibiotic overuse, thereby reducing the risk of inducing antibiotic resistance. The end-result will be major savings in healthcare and an increase in the well-being of members of society, as the burden of treating severely ill patients with potentially life-threatening, drug-resistant disease will be alleviated and the number of days lost to hospitalisation decreased, providing economic benefits to both individuals and society in general.
Another important component of the proposed diagnostic approach is that MS-proteomics analyses will be developed to detect actual protein expression by bacterial pathogens, as NAAT technologies could cause confusion in the design of a treatment plan by the physician if bacterial strains carry antibiotic resistance genes without these genes being efficiently expressed. Furthermore, the MS-proteomics approach provides the potential for highly sensitive, rapid, and cost-effective diagnosis of infectious bacteria, including identification at the species level, as well as comprehensive antibiotic resistance and virulence assessments, directly from clinical samples, i.e., without time-consuming cultivation steps. Such an approach provides the possibility for routine ‘bed-to-lab-to-physician’ diagnostics within a period of hours rather than days.
Contact information: Dr. John Hays (Co-ordinator) Email: Tel: 031107032177

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