Phosphatidylcholine PC ae C44:6 in cerebrospinal fluid is a sensitive biomarker for bacterial meningitis.

BACKGROUND: The timely diagnosis of bacterial meningitis is of utmost importance due to the need to institute antibiotic treatment as early as possible. Moreover, the differentiation from other causes of meningitis/encephalitis is critical because of differences in management such as the need for antiviral or immunosuppressive treatments. Considering our previously reported association between free membrane phospholipids in cerebrospinal fluid (CSF) and CNS involvement in neuroinfections we evaluated phosphatidylcholine PC ae C44:6, an integral constituent of cell membranes, as diagnostic biomarker for bacterial meningitis. METHODS: We used tandem mass spectrometry to measure concentrations of PC ae C44:6 in cell-free CSF samples (n = 221) from patients with acute bacterial meningitis, neuroborreliosis, viral meningitis/encephalitis (herpes simplex virus, varicella zoster virus, enteroviruses), autoimmune neuroinflammation (anti-NMDA-receptor autoimmune encephalitis, multiple sclerosis), facial nerve and segmental herpes zoster (shingles), and noninflammatory CNS disorders (Bell's palsy, Tourette syndrome, normal pressure hydrocephalus). RESULTS: PC ae C44:6 concentrations were significantly higher in bacterial meningitis than in all other diagnostic groups, and were higher in patients with a classic bacterial meningitis pathogen (e.g. Streptococcus pneumoniae, Neisseria meningitidis, Staphylococcus aureus) than in those with less virulent or opportunistic pathogens as causative agents (P = 0.026). PC ae C44:6 concentrations were only moderately associated with CSF cell count (Spearman's ρ = 0.45; P = 0.009), indicating that they do not merely reflect neuroinflammation. In receiver operating characteristic curve analysis, PC ae C44:6 equaled CSF cell count in the ability to distinguish bacterial meningitis from viral meningitis/encephalitis and autoimmune CNS disorders (AUC 0.93 both), but had higher sensitivity (91% vs. 41%) and negative predictive value (98% vs. 89%). A diagnostic algorithm comprising cell count, lactate and PC ae C44:6 had a sensitivity of 97% (specificity 87%) and negative predictive value of 99% (positive predictive value 61%) and correctly diagnosed three of four bacterial meningitis samples that were misclassified by cell count and lactate due to low values not suggestive of bacterial meningitis. CONCLUSIONS: Increased CSF PC ae C44:6 concentrations in bacterial meningitis likely reflect ongoing CNS cell membrane stress or damage and have potential as additional, sensitive biomarker to diagnose bacterial meningitis in patients with less pronounced neuroinflammation.

Numerical constructions of optimal feedback in models of chemotherapy of a malignant tumor.

In this paper, a problem of chemotherapy of a malignant tumor is considered. Dynamics is piecewise monotone and a therapy function has two maxima. The aim of therapy is to minimize the number of tumor cells at the given final instance. The main result of this work is the construction of optimal feedbacks in the chemotherapy task. The construction of optimal feedback is based on the value function in the corresponding problem of optimal control (therapy). The value function is represented as a minimax generalized solution of the Hamilton-Jacobi-Bellman equation. It is proved that optimal feedback is a discontinuous function and the line of discontinuity satisfies the Rankin-Hugoniot conditions. Other results of the work are illustrative numerical examples of the construction of optimal feedbacks and Rankin-Hugoniot lines.

Kynurenine Is a Cerebrospinal Fluid Biomarker for Bacterial and Viral Central Nervous System Infections.

BACKGROUND: The tryptophan-kynurenine-nicotinamide adenine dinucleotide (oxidized; NAD+) pathway is closely associated with regulation of immune cells toward less inflammatory phenotypes and may exert neuroprotective effects. Investigating its regulation in central nervous system (CNS) infections would improve our understanding of pathophysiology and end-organ damage, and, furthermore, open doors to its evaluation as a source of diagnostic and/or prognostic biomarkers. METHODS: We measured concentrations of kynurenine (Kyn) and tryptophan (Trp) in 221 cerebrospinal fluid samples from patients with bacterial and viral (due to herpes simplex, varicella zoster, and enteroviruses) meningitis/encephalitis, neuroborreliosis, autoimmune neuroinflammation (due to anti-N-methyl-D-aspartate receptor [NMDA] encephalitis and multiple sclerosis), and noninflamed controls (ie, individuals with Bell palsy, normal pressure hydrocephalus, or Tourette syndrome). RESULTS: Kyn concentrations correlated strongly with CSF markers of neuroinflammation (ie, leukocyte count, lactate concentration, and blood-CSF-barrier dysfunction), were highly increased in bacterial and viral CNS infections, but were low or undetectable in NMDA encephalitis, multiple sclerosis, and controls. Trp concentrations were decreased mostly in viral CNS infections and neuroborreliosis. Multiple logistic regression analysis revealed that combinations of Kyn concentration, Trp concentration, and Kyn/Trp concentration ratio with leukocyte count or lactate concentration were accurate classifiers for the clinically important differentiation between neuroborreliosis, viral CNS infections, and autoimmune neuroinflammation. CONCLUSIONS: The Trp-Kyn-NAD+ pathway is activated in CNS infections and provides highly accurate CSF biomarkers, particularly when combined with standard CSF indices of neuroinflammation.

Identification of Cerebrospinal Fluid Metabolites as Biomarkers for Enterovirus Meningitis.

Enteroviruses are among the most common causes of viral meningitis. Enteroviral meningitis continues to represent diagnostic challenges, as cerebrospinal fluid (CSF) cell numbers (a well validated diagnostic screening tool) may be normal in up to 15% of patients. We aimed to identify potential CSF biomarkers for enteroviral meningitis, particularly for cases with normal CSF cell count. Using targeted liquid chromatography-mass spectrometry, we determined metabolite profiles from patients with enteroviral meningitis (n = 10), and subdivided them into those with elevated (n = 5) and normal (n = 5) CSF leukocyte counts. Non-inflamed CSF samples from patients with Bell's palsy and normal pressure hydrocephalus (n = 19) were used as controls. Analysis of 91 metabolites revealed considerable metabolic reprogramming in the meningitis samples. It identified phosphatidylcholine PC.ae.C36.3, asparagine, and glycine as an accurate (AUC, 0.92) combined classifier for enterovirus meningitis overall, and kynurenine as a perfect biomarker for enteroviral meningitis with an increased CSF cell count (AUC, 1.0). Remarkably, PC.ae.C36.3 alone emerged as a single accurate (AUC, 0.87) biomarker for enteroviral meningitis with normal cell count, and a combined classifier comprising PC.ae.C36.3, PC.ae.C36.5, and PC.ae.C38.5 achieved nearly perfect classification (AUC, 0.99). Taken together, this analysis reveals the potential of CSF metabolites as additional diagnostic tools for enteroviral meningitis, and likely other Central nervous system (CNS) infections.

Discovery of Leptospira spp. seroreactive peptides using ORFeome phage display.

BACKGROUND: Leptospirosis is the most common zoonotic disease worldwide. The diagnostic performance of a serological test for human leptospirosis is mainly influenced by the antigen used in the test assay. An ideal serological test should cover all serovars of pathogenic leptospires with high sensitivity and specificity and use reagents that are relatively inexpensive to produce and can be used in tropical climates. Peptide-based tests fulfil at least the latter two requirements, and ORFeome phage display has been successfully used to identify immunogenic peptides from other pathogens. METHODOLOGY/PRINCIPAL FINDINGS: Two ORFeome phage display libraries of the entire Leptospira spp. genomes from five local strains isolated in Malaysia and seven WHO reference strains were constructed. Subsequently, 18 unique Leptospira peptides were identified in a screen using a pool of sera from patients with acute leptospirosis. Five of these were validated by titration ELISA using different pools of patient or control sera. The diagnostic performance of these five peptides was then assessed against 16 individual sera from patients with acute leptospirosis and 16 healthy donors and was compared to that of two recombinant reference proteins from L. interrogans. This analysis revealed two peptides (SIR16-D1 and SIR16-H1) from the local isolates with good accuracy for the detection of acute leptospirosis (area under the ROC curve: 0.86 and 0.78, respectively; sensitivity: 0.88 and 0.94; specificity: 0.81 and 0.69), which was close to that of the reference proteins LipL32 and Loa22 (area under the ROC curve: 0.91 and 0.80; sensitivity: 0.94 and 0.81; specificity: 0.75 and 0.75). CONCLUSIONS/SIGNIFICANCE: This analysis lends further support for using ORFeome phage display to identify pathogen-associated immunogenic peptides, and it suggests that this technique holds promise for the development of peptide-based diagnostics for leptospirosis and, possibly, of vaccines against this pathogen.

Optimized leaf ordering with class labels for hierarchical clustering.

Hierarchical clustering is extensively used in the bioinformatics community to analyze biomedical data. These data are often tagged with class labels, as e.g. disease subtypes or gene ontology (GO) terms. Heatmaps in connection with dendrograms are the common standard to visualize results of hierarchical clustering. The heatmap can be enriched by an additional color bar at the side, indicating for each instance in the data set to which class it belongs. In the ideal case, when the clustering matches perfectly with the classes, one would expect that instances from the same class cluster together and the color bar consists of well-separated color blocks without frequent alteration of colors (classes). But even in the case when instances from the same class cluster perfectly together, the dendrogram might not reflect this important aspect due to the fact that its representation is not unique. In this paper, we propose a leaf ordering algorithm for the dendrogram that preserving the hierarchical clustering result tries to group instances from the same class together. It is based on the concept of dynamic programming which can efficiently compute the optimal or nearly optimal order, consistent with the structure of the tree.

HUM calculator and HUM package for R: easy-to-use software tools for multicategory receiver operating characteristic analysis.

UNLABELLED: Receiver operating characteristic (ROC) analysis is usually applied in bioinformatics to evaluate the abilities of biological markers to differentiate between the presence or absence of a disease. It includes the derivation of the useful scalar performance measure area under the ROC curve for binary classification tasks. As real applications often deal with more than two classes, multicategory ROC analysis and the corresponding hypervolume under the manifold (HUM) measure have become a topic of growing interest. To support researchers in carrying out multicategory ROC analysis, we have developed two tools in different programming environments which feature user-friendly, object-oriented and flexible interfaces and enable the user to compute HUM values and plot 2D- and 3D-ROC curves. AVAILABILITY: The software is freely available from our Web site http://public.ostfalia.de/∼klawonn/HUM.htm

Entropy-based cluster validation and estimation of the number of clusters in gene expression data.

Many external and internal validity measures have been proposed in order to estimate the number of clusters in gene expression data but as a rule they do not consider the analysis of the stability of the groupings produced by a clustering algorithm. Based on the approach assessing the predictive power or stability of a partitioning, we propose the new measure of cluster validation and the selection procedure to determine the suitable number of clusters. The validity measure is based on the estimation of the "clearness" of the consensus matrix, which is the result of a resampling clustering scheme or consensus clustering. According to the proposed selection procedure the stable clustering result is determined with the reference to the validity measure for the null hypothesis encoding for the absence of clusters. The final number of clusters is selected by analyzing the distance between the validity plots for initial and permutated data sets. We applied the selection procedure to estimate the clustering results on several datasets. As a result the proposed procedure produced an accurate and robust estimate of the number of clusters, which are in agreement with the biological knowledge and gold standards of cluster quality.

Global transcriptome analysis in influenza-infected mouse lungs reveals the kinetics of innate and adaptive host immune responses.

An infection represents a highly dynamic process involving complex biological responses of the host at many levels. To describe such processes at a global level, we recorded gene expression changes in mouse lungs after a non-lethal infection with influenza A virus over a period of 60 days. Global analysis of the large data set identified distinct phases of the host response. The increase in interferon genes and up-regulation of a defined NK-specific gene set revealed the initiation of the early innate immune response phase. Subsequently, infiltration and activation of T and B cells could be observed by an augmentation of T and B cell specific signature gene expression. The changes in B cell gene expression and preceding chemokine subsets were associated with the formation of bronchus-associated lymphoid tissue. In addition, we compared the gene expression profiles from wild type mice with Rag2 mutant mice. This analysis readily demonstrated that the deficiency in the T and B cell responses in Rag2 mutants could be detected by changes in the global gene expression patterns of the whole lung. In conclusion, our comprehensive gene expression study describes for the first time the entire host response and its kinetics to an acute influenza A infection at the transcriptome level.

Gene expression changes in the host response between resistant and susceptible inbred mouse strains after influenza A infection.

Inbred mouse strains exhibit differences in susceptibility to influenza A infections. However, the molecular mechanisms underlying these differences are unknown. Therefore, we infected a highly susceptible mouse strain (DBA/2J) and a resistant strain (C57BL/6J) with influenza A H1N1 (PR8) and performed genome-wide expression analysis. We found genes expressed in lung epithelium that were specifically down-regulated in DBA/2J mice, whereas a cluster of genes on chromosome 3 was only down-regulated in C57BL/6J. In both mouse strains, chemokines, cytokines and interferon-response genes were up-regulated, indicating that the main innate immune defense pathways were activated. However, many immune response genes were up-regulated in DBA/2J much stronger than in C57BL/6J, and several immune response genes were exclusively regulated in DBA/2J. Thus, susceptible DBA/2J mice showed a hyper-inflammatory response. This response is similar to infections with highly pathogenic influenza virus and may serve as a paradigm for a hyper-inflammatory host response to influenza A virus.