Detecting Antibiotic Resistance Proteins in Clinical Samples Using Proteomics

在英国，每年大约有4万人死于败血症，这是一种通常由身体对血液中细菌的反应引发的疾病。当血液中存在细菌时，这被称为菌血症。细菌可以来自各种各样的地方，可以是许多不同的物种。因此，败血症的诊断并不能告诉医生是哪种细菌引起的。抗生素能杀死细菌，所以抗生素治疗对治疗败血症至关重要。如果不消除潜在的病因——菌血症——败血症的治疗是不可能成功的。医生面临的困境是，因为他们不知道他们想要杀死的细菌的身份，他们不确定使用哪种抗生素。细菌中抗生素耐药性的增加使得这一选择更加困难。处理这种情况的一种方法是使用“经验性疗法”：尝试一种特定的抗生素，等着看病人是否好转，如果没有，再尝试另一种。但与此同时，病人的病情可能会越来越严重。另一种方法是，医生可能会用他们能找到的最新、最有效的抗生素开始治疗，这样他们就有最好的机会杀死细菌。这意味着这种“最后手段”的药物可能是在不需要的时候使用的。不恰当地使用最后一种药物是抗生素耐药性的主要驱动因素，并将不可避免地缩短其使用寿命。我们真正需要的是向医生提供有关感染患者血液的细菌的身份的信息，更重要的是，它对哪种抗生素敏感。然后他们就可以做出明智的抗生素处方选择。目前，从怀疑有菌血症的病人身上采集血液样本开始，仅仅证明有细菌存在就需要48小时。使用新的MALDI-TOF机器，可以在几小时后识别出细菌，但这并不能告诉你任何关于抗生素敏感性的信息。可能还需要24小时才能发现可以使用哪些抗生素。这意味着患者可能会在长达72小时的时间里服用错误的抗生素。如果这是一种无效的抗生素，病人的生命处于危险之中，如果这是一种不恰当的最后使用抗生素，抗生素的使用寿命正在缩短。每个人都同意，减少向医生提供抗生素敏感性数据所需的时间是关键，不仅是为了治疗病人，也是为了更好地保护我们日益减少的有用抗生素供应。我们认为，通过直接在从患者血液中分离出来的细菌中识别抗生素抗性蛋白（细菌用来抵抗抗生素的工具），有可能实现这一目标。如果存在特定的抗性蛋白，医生就会知道不要使用特定的药物。为了验证我们的假设，我们想测试我们是否可以在血液样本中识别出细菌中的抗性蛋白，这些样本已经被培养和处理，就像在医院诊断实验室一样。我们将发现是否有可能使用现有的MALDI-TOF机器比目前的情况提前24小时识别至少一些抗生素耐药蛋白。我们还将测试是否有可能使用更专业的LC-MS/MS机器将获得抗生素敏感性数据的时间缩短至多60小时，在采样后约12-15小时给出抗生素敏感性的阳性指示。没有必要提供一种诊断测试，在采样后几分钟就能有真正的临床效益。对于严重的脓毒症，每一小时没有抗生素起作用，患者死亡率就会增加6%，因此，即使将目前预测抗生素敏感性所需的最低时间缩短数十小时，也会改变患者的护理。

Approximately 40,000 people die in the UK every year as a result of Sepsis, which is a medical condition usually triggered by the body's reaction to bacteria in the blood. When bacteria are present in the blood this is called bacteraemia. The bacteria can come from all sorts of places and can be of many different species. So a diagnosis of Sepsis doesn't tell a doctor what bacterium is responsible. Antibiotics kill bacteria, and so antibiotic therapy is absolutely critical to treating Sepsis. Without removing the underlying cause - the bacteraemia - treatment of Sepsis is unlikely to succeed. The dilemma that doctors face is that because they don't know the identity of the bacterium they want to kill, they are not certain what antibiotics to use. The rise of antibiotic resistance in bacteria makes this choice even more difficult. One way of dealing with this is to use "empiric therapy": to try a particular antibiotic, wait to see if the patient improves and if they don't, try another. But in the meantime, the patient may be getting more and more ill. The alternative approach is that the doctor may start treatment with the latest, most broad acting antibiotic they can find to give them the best chance of killing the bacteria. This means that this "last resort" drug might have been used when it wasn't really needed. Inappropriate use of a last resort drug is the primary driver for antibiotic resistance and will inevitably shorten its useful life. What we really need is to give doctors information about the identity of the bacterium infecting a patient's blood and, more importantly, what antibiotics it is susceptible to. Then they can make informed antibiotic prescribing choices. At the moment, from the time a blood sample is taken from a patient where bacteraemia is suspected it can take 48 hours just to prove there are any bacteria present. Using new MALDI-TOF machines it is possible to identify the bacterium a few hours later, but that doesn't tell you anything about antibiotic susceptibility. It may take another 24 h to find out what antibiotics can be used. This means that patients can be on the wrong antibiotic for up to 72 hours. If that's a non-effective antibiotic, the patient's life is in danger, if it is an inappropriately used last resort antibiotic, the antibiotic's useful life is being shortened. Everyone agrees that reducing the time it takes to get antibiotic susceptibility data to doctors is the key, not just for the treatment of patients, but also to better protect our dwindling supply of useful antibiotics. We feel that it may be possible to achieve this by identifying antibiotic resistance proteins - the tools bacteria employ to resist antibiotics - directly in bacteria isolated from patients' blood. If a particular resistance protein is present, the doctor would know not to use a particular drug. To test our hypothesis we want to test whether we can identify resistance proteins in bacteria in blood samples that have been cultured and processed exactly as they would be in hospital diagnostic labs. We will find out whether it is possible to use existing MALDI-TOF machines to identify at least some antibiotic resistance proteins 24 h earlier than is currently the case. We will also test whether it is possible to use more specialised LC-MS/MS machines to reduce the time to get antibiotic sensitivity data by up to 60 hours, giving a positive indication of antibiotic susceptibility about 12-15 h after sampling. It is not necessary to provide a diagnostic test that works minutes after sampling to have real clinical benefit. For severe Sepsis, each hour without working antibiotics gives a 6% increase in patient mortality, so even shaving tens of hours off the current minimum time it takes to predict antibiotic susceptibility would transform patient care.