Title: Application of molecular typing in a routine clinical setting for the detection of cross transmission events linked to Gram-negative bacteria
Funder: Health Education England

Abstract: Infections caused by multi drug resistant (MDR) Gram-negative bacteria lead to poor clinical outcomes and death. The frequency of antibiotic resistant organisms is increasing and they represent a growing challenge to current healthcare systems. The need for successful control of these organisms was mandated in the Health and Social Care Act 2008, however, the need to control these organisms has taken on even greater importance due to the emergence of organisms that are resistant to all antibiotics and the absence of new antibiotics currently being developed. A range of infection prevention and control activities have been employed to combat their selection and spread; however these interventions are expensive, disruptive, and difficult to monitor in real time. In addition as a number of interventions are available it is difficult to know how to best focus interventions within increasingly resource limited healthcare settings. In order to properly control and focus infection control interventions to prevent the spread of MDR Gram-negative bacteria, it is essential to know when cross transmission has occurred and which organisms are associated with invasive disease. This will provide information as to whether containment measures are effective and when to implement them. Therefore the availability of robust typing methods when bacterial cross-transmission is suspected is of critical importance. The methods used to do this currently often only look at fragments of the genome, such as PFGE or the size/number of tandem repeats i.e. VNTR. Additionally, further testing is required in order to identify genetic determinants of antibiotic resistance. High throughput sequencing (HTS) is currently a research technique which can be used for bacterial genomic sequencing, but it may represent a single and rapid approach that can provide epidemiological, virulence and resistance based upon information from a single test. For common Enterobacteriaceae that cause outbreaks, such as Klebsiella spp, there is currently insufficient information to interpret cross transmission and phylogenetic relationships based upon HTS data. Information required includes data on molecular clocks for both invasive and non-invasive isolates and definitions for how these should be interpreted and linked to cross transmission in order to permit their use in outbreak investigations. This project will begin to address this issue by undertaking high throughput sequencing of Klebsiella species linked to outbreaks, colonisation, invasive disease and antibiotic resistance to gather the data required to develop both data interpretation criteria and bioinformatics tools for a clinical infection control setting. Sequencing data will also be made available to other centres via an on-line database. Once developed the bioinformatics tools will be available to other centres for use in investigating cases of cross transmission as the tools will be open source. Protocols utilised in processing samples within the study will also made available. This will help to aid dissemination of this technique and encourage use by other centres. This is important as prevention of the spread of Gram-negative species is crucial in improving the quality of patient stays by decreasing cases of hospital acquired infection. By making both the bioinformatics system open access and the database openly available it is hoped that the data can be used more widely than for infection control purposes as there isn't currently a resource like this for Klebsiella spp.

Lay abstract: Infections within hospitals can be caused by bugs known as bacteria that can already be present within the patient and have previously done them no harm or by bacteria that have spread from one patient to another. When bacteria are present on a patient and causing no harm these bacteria are called colonising bacteria. Colonising bacteria live in different places when causing no harm to when they are causing infection. This project will focus on bacteria, known as Klebsiella species, that normally live within the intestines of patients and may cause them no harm, but can also cause illness if they get into other areas of the body, such as in the blood or lungs. When these bacteria get into parts of the body where they don't normally live and cause infection they are treated with antibiotics to kill them. Many Klebsiella bacteria are now immune to antibiotics however and are very difficult to kill when they make patients unwell, this can result in the patient dying. It is very important therefore to make sure that these bacteria do not spread from patient to patient. One way to do this is to make sure that we know when patients have them and to regularly check our patients to make sure that when we find the bug in new patients it is not because they have got it from someone else. This is done by looking at the genes of the bacteria to see if they are the same. Currently most of the ways to do this look at only a few genes to see if they are the same or not, if they are all the same it is decided that the bacteria has spread from one patient to another and infection control intervene to stop it happening again. Often however as only a few genes are looked at cases of spread can be missed and further cases can occur. This project will use a new technology, known as high throughput sequencing, which will enable all of the genes to be looked at and then matched between bacteria. This new approach means that spread of bacteria will be detected more rapidly and so less patients will be affected. However this means that compared to other methods there is a lot more data to look at to see if there is a match. New computer programmes need to be written to make this happen. To design these new computing programmes a lot of information need to be gathered on patients who are unwell with Klebsiella and patient who are colonised with Klebsiella. This data will be used to teach the computing system when there has been spread of the bacteria and it will then be able to do this on its own when it gets new information from the hospital. In addition to making a new computing programme all of the information gathered on bacterial genes will be made available through a database on the internet so hospitals and universities can use it to understand the bacteria better. This will include information on how the bacteria are able to survive despite patients being given antibiotics. This may help other people make new antibiotics in the long term. Once this has been done for Klebsiella the same process can be used for other similar bacteria.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: INFECTION