Lucia Pappalardo

The NMR structure of the sensory domain of the membranous two-component fumarate sensor (histidine protein kinase) DcuS of Escherichia coli.

The structure of the water-soluble, periplasmic domain of the fumarate sensor DcuS (DcuS-pd) has been determined by NMR spectroscopy in solution. DcuS is a prototype for a sensory histidine kinase with transmembrane signal transfer. DcuS belongs to the CitA family of sensors that are specific for sensing di- and tricarboxylates. The periplasmic domain is folded autonomously and shows helices at the N and the C terminus, suggesting direct linking or connection to helices in the two transmembrane regions. The structure constitutes a novel fold. The nearest structural neighbor is the Per-Arnt-Sim domain of the photoactive yellow protein that binds small molecules covalently. Residues Arg107, His110, and Arg147 are essential for fumarate sensing and are found clustered together. The structure constitutes the first periplasmic domain of a two component sensory system and is distinctly different from the aspartate sensory domain of the Tar chemotaxis sensor.

NMR-Metabolomics Study on Falcons Affected by Aspergillosis

Respiratory diseases are a common cause of morbidity and mortality in raptors. Among these diseases, asper- gillosis is one of the most important causes of mortality of falcons in the Middle East. Falcon blood (plasma) has been in- vestigated for the first time by 1 H-NMR spectroscopy and multivariate statistics in order to identify and comparatively characterize the metabolic profile of aspergillosis. Clear differences exist between the profiles of healthy and diseased subjects and lead to clean clustering in statistics. Analysis of the orthogonal projection to latent structure discriminant analysis (O-PLS-DA) coefficient plots and statistical total correlation spectroscopy (STOCSY) traces helps to identify significant components that define the separation. We have observed that 3-hydroxybutyrate is greatly increased in the diseased cohort, among a variety of other metabolic differences. Also, there is a distinctively different behavior of the very low density (VLDL) and low density lipids/lipoproteins (LDL); the heavier lipid subfractions are significantly dimin- ished in the sick subjects. These findings serve as the first step towards developing a possible test for early diagnosis and may provide a better understanding of the underlying biochemistry of this disease.

Novel cutting-edge metabolite-based diagnostic tools for aspergillosis

Metabolite-based detection of aspergillosis Aspergillosis is a group of diseases caused by the inhalation of ubiquitous Aspergillus spores, generally Aspergillus fumigatus [1], that evade the host immune system [2]. Its most aggressive form, invasive aspergillosis (IA), carries a particularly grim prognosis in immunocompromised patients [2]. High mortality and the associated socioeconomic burden [3] necessitate early, accurate, and sensitive detection of Aspergillus infection. Many clinical diagnostic tests are available [4]; however, the identification of improved methods for the detection of aspergillosis is still an active field of research. Pathogens possess a rich metabolism and a vast array of secondary metabolites, many unique to their species, that constitute a pathogen “fingerprint” [5]. Pathogens can leave their imprint on the host in other ways; for instance, host–pathogen interactions can alter the host’s own metabolome, leaving a “signature” of the disease caused by the pathogen [6]. Detecting metabolic evidence of the pathogen’s presence underlies several emerging methods of disease diagnosis. Due to the absence of an ideal diagnostic test, we have seen new applications of metabolite detection for Aspergillus in recent years. Methods include detection of gliotoxin [7,8] and siderophores [9], siderophore uptake [10,11], volatile organic compounds (VOCs) [12–14], and changes to the host’s metabolome in serum [15]. Although still at the research stage, all of these techniques offer distinct advantages for aspergillosis detection (Table 1).