No abstract

Epichloid endophytes are well known symbionts of many cool-season grasses that may alleviate environmental stresses for their hosts. For example, endophytes produce alkaloid compounds that may be toxic to invertebrate or vertebrate herbivores. Achnatherum robustum, commonly called sleepygrass, was aptly named due to the presence of an endophyte that causes toxic effects to livestock and wildlife. Variation in alkaloid production observed in two A. robustum populations located near Weed and Cloudcroft in the Lincoln National Forest, New Mexico, suggests two different endophyte species are present in these populations. Genetic analyses of endophyte-infected samples revealed major differences in the endophyte alkaloid genetic profiles from the two populations, which were supported with chemical analyses. The endophyte present in the Weed population was shown to produce chanoclavine I, paspaline, and terpendoles, so thus resembles the previously described Epichloë funkii. The endophyte present in the Cloudcroft population produces chanoclavineI, ergonovine, lysergic acid amide, and paspaline, and is an undescribed endophyte species. We observed very low survival rates for aphids feeding on plants infected with the Cloudcroft endophyte, while aphid survival was better on endophyte infected plants in the Weed population. This observation led to the hypothesis that the alkaloid ergonovine is responsible for aphid mortality. Direct testing of aphid survival on oat leaves supplemented with ergonovine provided supporting evidence for this hypothesis. The results of this study suggest that alkaloids produced by the Cloudcroft endophyte, specifically ergonovine, have insecticidal properties.

No abstract

This review covers the current and potential use of mass spectrometry-based metabolomics data mining in natural products. Public data, metadata, databases and data analysis tools are critical. The value and success of data mining rely on community participation.

Even though raw mass spectrometry data is information rich, the vast majority of the data is underutilized. The ability to interrogate these rich datasets is handicapped by the limited capability and flexibility of existing software. We introduce the Mass Spec Query Language (MassQL) that addresses these issues by enabling an expressive set of mass spectrometry patterns to be queried directly from raw data. MassQL is an open-source mass spectrometry query language for flexible and mass spectrometer manufacturer-independent mining of MS data. We envision the flexibility, scalability, and ease of use of MassQL will empower the mass spectrometry community to take fuller advantage of their mass spectrometry data and accelerate discoveries.