Altered tissue specialized pro-resolving mediators in chronic rhinosinusitis

Current literature implicates arachidonic acid-derived leukotrienes and prostaglandins in the pathogenesis of chronic rhinosinusitis. However, other omega-3 and omega-6 derived lipid mediators, such as specialized pro-resolving mediators (SPMs), may also be important in chronic inflammatory disorders of the upper airway. We hypothesize that SPMs differ among CRS subtypes compared to controls and in relation to sinonasal microbiota. Ethmoid sinus tissue and middle meatal swabs were collected from a convenience sample of 66 subjects, including non-CRS controls, CRS with polyps (CRSwNP), and CRS without polyps (CRSsNP). Lipid mediator pathways were analyzed by liquid chromatography/tandem mass spectrometry. Bacterial taxa were profiled in parallel by 16S rRNA gene sequencing. Resolvin D2 was elevated in both CRSwNP (p = 0.00076) and CRSsNP (p = 0.030) compared with non-CRS controls. Lipoxin A4 was significantly increased in CRSwNP compared with CRSsNP (p = 0.000033) and controls (p = 0.044). Cigarette smoking was associated with significantly lower concentrations of several 15-lipoxygenase metabolites including resolvin D1 (p = 0.0091) and resolvin D2 (p = 0.0097), compared with never-smokers. Several of the lipid compounds also correlated with components of the sinonasal mucosal microbiota, including bacterial pathogens such as Pseudomonas aeruginosa. These data suggest that dysfunctional lipid mediator pathways in CRS extend beyond the traditional descriptions of leukotrienes and prostaglandins and include SPMs. Furthermore, dysregulated SPM signaling may contribute to persistent inflammation and bacterial colonization in CRS.

liputils: a Python module to manage individual fatty acid moieties from complex lipids

Lipidomic analyses address the problem of characterizing the lipid components of given cells, tissues and organisms by means of chromatographic separations coupled to high-resolution, tandem mass spectrometry analyses. A number of software tools have been developed to help in the daunting task of mass spectrometry signal processing and cleaning, peak analysis and compound identification, and a typical finished lipidomic dataset contains hundreds to thousands of individual molecular lipid species. To provide researchers without a specific technical expertise in mass spectrometry the possibility of broadening the exploration of lipidomic datasets, we have developed liputils, a Python module that specializes in the extraction of fatty acid moieties from individual molecular lipids. There is no prerequisite data format, as liputils extracts residues from RefMet-compliant textual identifiers and from annotations of other commercially available services. We provide three examples of real-world data processing with liputils, as well as a detailed protocol on how to readily process an existing dataset that can be followed with basic informatics skills.