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The world demands new solutions and products to be used as dyes for industrial applications. Microbial pigments represent an eco-friendly alternative as they can be produced in large amounts through biotechnological processes and do not present environmental risks, as they are easily decomposable. Moreover, some of these metabolites are recognized for their biological activities, which qualify them for potential uses as food colorants and nutraceuticals, protecting against degenerative diseases related with oxidative stress. Because of their genetic simplicity as compared with plants, microorganisms may be a better source to understand biosynthetic mechanisms and to be engineered for producing high pigment yields. Despite the origin of the pigmented microorganism, it seems very important to develop protocols using organic industrial residues and agricultural byproducts as substrates for pigment production and find novel green strategies for rapid pigment extraction. This review looks for the most recent studies that describe microbial pigments from microalgae, fungi, and bacteria. In particular, the underexploited tools of omics science such as proteomics and metabolomics are addressed. The use of techniques involving mass spectrometry, allows to identify different protein and metabolite profiles that may be associated with a variety of biotechnologically-relevant pathways of pigment synthesis.

The identity and biological activity of most metabolites still remain unknown. A bottleneck in the exploration of metabolite structures and pharmaceutical activities is the compound purification needed for bioactivity assignments and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we develop a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry and detection of protein binding via native mass spectrometry. A native metabolomics screen for protease inhibitors from an environmental cyanobacteria community reveals 30 chymotrypsin-binding cyclodepsipeptides. Guided by the native metabolomics results, we select and purify five of these compounds for full structure elucidation via tandem mass spectrometry, chemical derivatization, and nuclear magnetic resonance spectroscopy as well as evaluation of their biological activities. These results identify rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency, highlighting native metabolomics as a promising approach for drug discovery, chemical ecology, and chemical biology studies.

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The identity and biological activity of most metabolites still remain unknown. A key bottleneck in the full exploration of this tremendous source of new structures and pharmaceutical activities is the compound purification needed for bioactivity assignments of individual compounds and downstream structure elucidation. To enable bioactivity-focused compound identification from complex mixtures, we developed a scalable native metabolomics approach that integrates non-targeted liquid chromatography tandem mass spectrometry, and simultaneous detection of protein binding via native mass spectrometry. While screening for new protease inhibitors from an environmental cyanobacteria community, native metabolomics revealed 30 cyclodepsipeptides as chymotrypsin binders. Mass spectrometry-guided purification then allowed for full structure elucidation of the most prevalent compounds via nuclear magnetic resonance spectroscopy, as well as orthogonal bioactivity studies. Together, these results identified the rivulariapeptolides as a family of serine protease inhibitors with nanomolar potency.

Non-targeted liquid chromatography−tandem mass spectrometry (LC−MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.