Metabolomics of cerebrospinal fluid from humans treated for rabies.

Rabies is a rapidly progressive lyssavirus encephalitis that is statistically 100% fatal. There are no clinically effective antiviral drugs for rabies. An immunologically naïve teenager survived rabies in 2004 through improvised supportive care; since then, 5 additional survivors have been associated with use of the so-called Milwaukee Protocol (MP). The MP applies critical care focused on the altered metabolic and physiologic states associated with rabies. The aim of this study was to examine the metabolic profile of cerebrospinal fluid (CSF) from rabies patients during clinical progression of rabies encephalitis in survivors and nonsurvivors and to compare these samples with control CSF samples. Unsupervised clustering algorithms distinguished three stages of rabies disease and identified several metabolites that differentiated rabies survivors from those who subsequently died, in particular, metabolites related to energy metabolism and cell volume control. Moreover, for those patients who survived, the trajectory of their metabolic profile tracked toward the control profile and away from the rabies profile. NMR metabolomics of human rabies CSF provide new insights into the mechanisms of rabies pathogenesis, which may guide future therapy of this disease.

NMR metabolomics of cerebrospinal fluid differentiates inflammatory diseases of the central nervous system.

BACKGROUND: Myriad infectious and noninfectious causes of encephalomyelitis (EM) have similar clinical manifestations, presenting serious challenges to diagnosis and treatment. Metabolomics of cerebrospinal fluid (CSF) was explored as a method of differentiating among neurological diseases causing EM using a single CSF sample. METHODOLOGY/PRINCIPAL FINDINGS: 1H NMR metabolomics was applied to CSF samples from 27 patients with a laboratory-confirmed disease, including Lyme disease or West Nile Virus meningoencephalitis, multiple sclerosis, rabies, or Histoplasma meningitis, and 25 controls. Cluster analyses distinguished samples by infection status and moderately by pathogen, with shared and differentiating metabolite patterns observed among diseases. CART analysis predicted infection status with 100% sensitivity and 93% specificity. CONCLUSIONS/SIGNIFICANCE: These preliminary results suggest the potential utility of CSF metabolomics as a rapid screening test to enhance diagnostic accuracies and improve patient outcomes.