GNPS is an open-access community-curated analysis platform for sharing natural product mass spectrometry data that enables continuous, automatic reanalysis of deposited 'living' data sets. The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu ), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.

Since secondary metabolites represent a chemical interface between plants and surrounding environment, their syntheses are frequently affected by environmental conditions. Thus, variations in the total content and/or of the relative proportions of secondary metabolites in plants can take place. We review the main environmental factors that can streamline or alter the production or concentration of secondary metabolites in plants. How seasonality, circadian rhythm, developmental stage and age, temperature, water availability, UV radiation, soil nutrients, altitude, atmospheric composition and tissue damage influence secondary metabolism are discussed

This review article explores the most common fragmentation reactions for ions generated by ESI in positive and negative modes.

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. For genome analysis it is commonly accepted that one can hypothesize the function of genes based on sequence similarity, using annotated reference sequences. Once a homology hypothesis has been made based on reference annotations, it allows one to build hypothesis in terms of function and ultimately understand the underlying biology. In contrast, mass spectrometry (MS) can detect many molecules, as ions, yet we often cannot link a MS signal to a molecule. The reference libraries to annotate fragmented molecular data only cover a small portion of the known molecular space. The use of computational (in silico) fragmentation predictions from structural libraries offers a promising alternative. One of the weaknesses of the molecular annotation using such in silico approaches is that they currently annotate the molecules individually. However, molecular relationships, based on spectral similarity, can be used to enhance the structural hypothesis inferred from the annotation of molecules detected by mass spectrometry. We introduce an online tool called “Network Annotation Propagation” that uses a combination of molecular networks, based on spectral similarity, from which we infer molecular similarity, together with in silico fragmentation, to enable the scientific community to strengthen their MS annotations.