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Phenolic compounds are numerous and ubiquitous in the plant kingdom, being particularly present in health-promoting foods. Epidemiological evidences suggest that the consumption of polyphenol-rich foods reduces the incidence of cancer, coronary heart disease and inflammation. Chlorogenic acid (CGA) is one of the most abundant polyphenol compounds in human diet. Data obtained from in vivo and in vitro experiments show that CGA mostly presents antioxidant and anti-carcinogenic activities. However, the effects of CGA on the inflammatory reaction and on the related pain and fever processes have been explored less so far. Therefore, this study was designed to evaluate the anti-inflammatory, antinociceptive and antipyretic activities of CGA in rats. In comparison to control, CGA at doses 50 and 100 mg/kg inhibited carrageenin-induced paw edema beginning at the 2nd hour of the experimental procedure. Furthermore, at doses 50 and 100 mg/kg CGA also inhibited the number of flinches in the late phase of formalin-induced pain test. Such activities may be derived from the inhibitory action of CGA in the peripheral synthesis/release of inflammatory mediators involved in these responses. On the other hand, even at the highest tested dose (200 mg/kg), CGA did not inhibit the febrile response induced by lipopolysaccharide (LPS) in rats. Additional experiments are necessary in order to clarify the true target for the anti-inflammatory and analgesic effects of CGA.

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

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