In short, what is your research project about? What are you researching and why?
Surgical site infections (SSIs) are serious post-operative complications that are caused mainly by colonisation and overgrowth of endogenous pathogens in the wound. SSIs occur in approximately 2 per cent of all surgical interventions and affect around 19,000 patients in Finland annually. SSIs are estimated to cause 150 preventable deaths and a cost to society of 200 million euros a year. In order to improve the treatment of patients with SSIs, and to improve our understanding of bacterial communities in surgical sites, we will survey more than 50 burn patients with infected and uninfected surgical wounds. We will employ cutting-edge sequencing methods and develop new computational solutions to identify bacteria found in infected samples and to predict the most effective antibiotic treatment. We expect that our project, Action on Human Surfaces: Wound-Metatranscriptomics (Bugsy), will produce a comprehensive map of the distribution of bacteria in surgical infections and lay a foundation for more precise and even life-saving practices in infection diagnostics.
There is much talk about multi-, trans- and interdisciplinarity nowadays. Are these merely trendy buzzwords for scientific collaboration, or do they serve an instrumental purpose in your project?
Interdisciplinarity is the heart of the Bugsy project. We have brought together top experts from microbiology, sequencing, surgery and computational biology. It is the combination of their expertise that enables the meaningful implementation of the project, spanning from the treatment of burn patients all the way through to optimisation of sample treatment and RNA sequencing libraries, as well as the development of parallel algorithms at the heart of the project. From the inception of the project, interdisciplinary communication has brought new perspectives to the optimisation of diagnostics for SSIs, and it has successfully directed the development of sample preparation and bioinformatics algorithms towards clinically relevant purposes. In addition to cooperation and a wider knowledge base, the interdisciplinary nature of the project has generated substantial synergies within the consortium and enabled the efficient use of research resources as the collaboration parties can focus on their strengths.
Research into personalised health involves an integrative “from-research-to-practice” mindset. Where do you place your own research in this context? Does your project have partners that are not research-related partners?
The central theme of the Bugsy project is to revolutionise routine diagnostics of infectious diseases by creating a sequencing protocol for analysing infectious samples in a clinically relevant time frame. Although the high cost of the technology is currently limiting its widespread use, we believe that innovations generated in the project and instrument suppliers’ own developments will make this qualitatively superior technology usable in large-scale daily diagnostics within a few years. Apropos future developments, since the project started in September 2015, we have recruited four more HUCS units to the project. This wider cooperation will enable the use of Bugsy technologies in the analysis of other infectious diseases and bring new skills and perspectives to our collaborative network. We are in negotiations to start cooperation with HUSLAB, which conducts more than 600,000 infection analyses annually, and we are discussing with several different business partners about potential cooperation related to the project.
A big fuss over nothing, or a major change in practices? In your estimation, how and when will the effects of the promotion of personalised health be evident in the healthcare system?
We believe that personalised healthcare will bring rapid changes to future patient care, particularly to those aspects in which improved care at a lower cost of treatment can be demonstrated. Encouraging examples of the benefits of personalised healthcare include the use of genomic sequencing of cancer tissues to predict effective medication, the use of genetic comparisons between donors and patients to find suitable organ donors, the use of molecular profiling methods to predict the risk of heart disease and to identify patients at risk, not to forget new infectious disease treatment and diagnostic methods created in the project.