Project description:Meningitis is a complex disease which can be caused by infection with either viral or bacterial pathogens. Viral meningitis is usually a sterile self-limiting disease with a good clinical prognosis, while bacterial meningitis is a potentially more serious disease with a higher mortality rate. Early diagnosis of bacterial meningitis is of paramount importance, as intervention with antimicrobial therapy increases the likelihood of a favourable clinical outcome. Routine diagnosis in many laboratories is still dependent to some degree on traditional methods e.g. culture, though molecular methods have been developed which can give a shorter time to diagnosis. However, there is not as yet a single test format that can detect all bacterial pathogens capable of causing meningitis. In addition, many tests e.g. real-time PCR have a finite limit for multiplexing and do not provide additional information such as strain or serogroup which is useful during outbreaks and for retrospective epidemiological surveillance. To this end we have developed a microarray probe set for detection of meningitis-associated bacterial pathogens including those in the N. meningitidis serogroups. Here we demonstrate utility of this array in specific detection of represented bacterial species and strains and in detection of pathogen signals in cerebrospinal fluid samples from patients with suspected bacterial meningitis. This method shows promise for development as a diagnostic tool; however, we discuss the technical issues encountered and suggest mechanisms to improve resolution of pathogen-specific signals in complex clinical samples. We designed as part of a larger pan-pathogen microarray a sub-set of probes to meningitis-associated bacterial pathogens. We present here data confirming the pathogen-specificity of many of these probes and their potential use in clinical diagnosis through testing on a small number of patient clinical samples using human DNA and no added nucleic acid controls. These data are from single channel Cy3-labelled nucleic acids. Four technical replicates for each feature are included on the array.

Project description:Meningitis is a life-threatening condition characterized by the inflammation of the leptomeningeal membranes surrounding the brain and spinal cord. The term meningitis is an umbrella term and includes several different etiologies. The majorities of meningitis cases are caused by viruses (viral meningitis; VM) and are often associated with low mortality rates and low risk of developing neurological. In contrast, meningitis caused by some viral infections, such as tick-borne encephalitis (TBE), can be life-threatening when left untreated with increased risk of developing neurological sequelae. Acute bacterial meningitis (ABM), however, is one of the leading causes of death due to infectious diseases worldwide and is associated with rapid disease progression, high mortality rates and increased risk of long-term neurological sequelae in survivors. As meningitis is caused by numerous different pathogens, the host-response is typically highly variable and it is currently unknown if different pathogens can introduce specific proteome changes in the cerebrospinal fluid (CSF). In this study we applied DIA-MS to provide novel insights for in-depth understanding of central nervous system functioning and host response during meningitis in a cohort of patients with differential diagnosis of meningitis, to account for variability contributed by different disease-causing pathogens. The results reveal drastic changes in the CSF proteome during meningitis, where in particular a massive increase of neutrophil derived proteins in the CSF correlated with ABM, suggesting that activated neutrophils play a particular role in ABM. Additionally both ABM and VM result in marked reduction of brain-specific proteins in the CSF, which could be indicative of pathophysiological mechanisms leading to brain damage. Furthermore, generation of lasso regression model enables separation of ABM with high sensitivity and specificity, demonstrating that several proteins are required to confidently discriminate between ABM, VM and BM.

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Clinical metagenome

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