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VSEARCH is an open source and free of charge multithreaded 64-bit tool for processing and preparing metagenomics, genomics and population genomics nucleotide sequence data. It is designed as an alternative to the widely used USEARCH tool (Edgar, 2010) for which the source code is not publicly available, algorithm details are only rudimentarily described, and only a memory-confined 32-bit version is freely available for academic use. When searching nucleotide sequences, VSEARCH uses a fast heuristic based on words shared by the query and target sequences in order to quickly identify similar sequences, a similar strategy is probably used in USEARCH. VSEARCH then performs optimal global sequence alignment of the query against potential target sequences, using full dynamic programming instead of the seed-and-extend heuristic used by USEARCH. Pairwise alignments are computed in parallel using vectorisation and multiple threads. VSEARCH includes most commands for analysing nucleotide sequences available in USEARCH version 7 and several of those available in USEARCH version 8, including searching (exact or based on global alignment), clustering by similarity (using length pre-sorting, abundance pre-sorting or a user-defined order), chimera detection (reference-based or ), dereplication (full length or prefix), pairwise alignment, reverse complementation, sorting, and subsampling. VSEARCH also includes commands for FASTQ file processing, i.e., format detection, filtering, read quality statistics, and merging of paired reads. Furthermore, VSEARCH extends functionality with several new commands and improvements, including shuffling, rereplication, masking of low-complexity sequences with the well-known DUST algorithm, a choice among different similarity definitions, and FASTQ file format conversion. VSEARCH is here shown to be more accurate than USEARCH when performing searching, clustering, chimera detection and subsampling, while on a par with USEARCH for paired-ends read merging. VSEARCH is slower than USEARCH when performing clustering and chimera detection, but significantly faster when performing paired-end reads merging and dereplication. VSEARCH is available at https://github.com/torognes/vsearch under either the BSD 2-clause license or the GNU General Public License version 3.0. VSEARCH has been shown to be a fast, accurate and full-fledged alternative to USEARCH. A free and open-source versatile tool for sequence analysis is now available to the metagenomics community.

Salvia hispanica L., commonly known as chía, and its seeds have been used since ancient times to prepare different beverages. Due to its nutritional content, it is considered a dietary ingredient and has been reported with many health benefits. Chia seed components are helpful in cardiovascular disease (CVD) by reducing blood pressure, platelet aggregation, cholesterol, and oxidation. Still, its vasodilator effects on the vascular system were not reported yet. The hexanic (HESh), dichloromethanic (DESh), and methanolic (MESh) extracts obtained from chía seeds were evaluated on an aortic ring ex-vivo experimental model. The vasorelaxant efficacy and mechanism of action were determined. Also, phytochemical data was obtained through 13C NMR-based dereplication. The MESh extract showed the highest efficacy (Emax = 87%), and its effect was partially endothelium-dependent. The mechanism of action was determined experimentally, and the vasorelaxant curves were modified in the presence of L-NAME, ODQ, and potassium channel blockers. MESh caused a relaxing effect on KCl 80 mM-induced contraction and was less potent than nifedipine. The CaCl2-induced contraction was significantly decreased compared with the control curve. Phytochemical analysis of MESh suggests the presence of mannitol, previously reported as a vasodilator on aortic rings. Our findings suggest NO-cGMP pathway participation as a vasodilator mechanism of action of S. hispanica seeds; this effect can be attributed, in part, to the mannitol presence. S. hispanica could be used in future research focused on antihypertensive therapies.

The development of new drugs will certainly benefit from an ever improving knowledge of the living beings chemistry. However, identification of drugable molecules within the immense biodiversity of forests, soils or oceans still requires considerable investments in technical equipments, time and human resources. An important part of this process is the quick identification of known substances in order to concentrate the efforts on the discovery of new ones. A range of “dereplication” procedures are currently emerging to meet this challenge as key strategies to improve the performance of natural product screening programs. Initially defined in 1990 as “a process of quickly identifying known chemotypes”, dereplication is today a not so univocal concept and has evolved over the last years in different ways. The present review covers all dereplication-related sudies in natural product research from 1990 to 2014. Its writing brought to light five distinct dereplication workflows that can be characterized by the nature of starting materials, by the selected analytical technique, and above all by the final objective. Dereplication can be used as an untargeted workflow for the rapid identification of the major compounds whatever their chemical class in a single sample or for the acceleration of bioactivity-guided fractionation procedures. In other cases dereplication is fully integrated in metabolomic studies for the untargeted chemical profiling of natural extract collections or for the targeted identification of a predetermined class of metabolites. Finally a quite distinct dereplication approach mainly based on gene-sequence analyses is frequently used for the taxonomic identification of microbial strains.

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Common spruce (Picea abies L.) is a fast-growing coniferous tree, widely used in several countries for the production of sawn wood, timber and pulp. During this industrial exploitation, large quantities of barks are generated as waste materials. The aim of this study was the bio-guided investigation and the effective recovery of methanol-soluble metabolites of common spruce bark for the development of new dermo-cosmetic agents. The active methanol extract was initially fractionated by Centrifugal Partition Chromatography (CPC) using a triphasic solvent system in a step-gradient elution mode. All resulting fractions were evaluated for their antibacterial activity, antioxidant activity and their capability to inhibit tyrosinase, elastase and collagenase activity. In parallel, the chemical composition of each fraction was established by combining a 13C-NMR dereplication approach and 2D-NMR analyses. As a result, fourteen secondary metabolites corresponding to stilbene, flavonoid and phenolic acid derivatives were directly identified in the CPC fractions. A high amount (0.93 g) of E-astringin was recovered from 3 g of crude extract in a single 125 min run. E-Astringin significantly induced the tyrosinase activity while E-piceid, taxifolin, and taxifolin-3′-O-glucopyranoside exhibited significant anti-tyrosinase activity. The above compounds showed important anti-collagenase and antimicrobial activities, thus providing new perspectives for potential applications as cosmetic ingredients.

In the arid region Bou-Saâda at the South of Algeria, durum wheat Triticum durum L. cv Waha production is severely threatened by abiotic stresses, mainly drought and salinity. Plant growth-promoting rhizobacteria (PGPR) hold promising prospects towards sustainable and environmentally-friendly agriculture. Using habitat-adapted symbiosis strategy, the PGPR Pantoea agglomerans strain Pa was recovered from wheat roots sampled in Bou-Saâda, conferred alleviation of salt stress in durum wheat plants and allowed considerable growth in this unhostile environment. Strain Pa showed growth up to 35 °C temperature, 5–10 pH range, and up to 30% polyethylene glycol (PEG), as well as 1 M salt concentration tolerance. Pa strain displayed pertinent plant growth promotion (PGP) features (direct and indirect) such as hormone auxin biosynthesis, production of 1-aminocyclopropane-1-carboxylate (ACC) deaminase, and ammonia and phosphate solubilization. PGPR features were stable over wide salt concentrations (0–400 mM). Pa strain was also able to survive in seeds, in the non-sterile and sterile wheat rhizosphere, and was shown to have an endophytic life style. Phylogenomic analysis of strain Pa indicated that Pantoea genus suffers taxonomic imprecision which blurs species delimitation and may have impacted their practical use as biofertilizers. When applied to plants, strain Pa promoted considerable growth of wheat seedlings, high chlorophyll content, lower accumulation of proline, and favored K+ accumulation in the inoculated plants when compared to Na+ in control non-inoculated plants. Metabolomic profiling of strain Pa under one strain many compounds (OSMAC) conditions revealed a wide diversity of secondary metabolites (SM) with interesting salt stress alleviation and PGP activities. All these findings strongly promote the implementation of Pantoea agglomerans strain Pa as an efficient biofertilizer in wheat plants culture in arid and salinity-impacted regions.

A dereplication strategy was developed for the screening of secondary metabolites from Sophora flavescens . The strategy consisted of 4 procedures. First, the extract of the Sophora flavescens root was subjected to LC–MS/MS analysis with both data-independent acquisition (DIA) mode and data-dependent acquisition (DDA) mode. Then the DIA results were used to construct a molecular networking (MN) according to the GNPS workflow and consequently obtain annotations. In parallel, the DDA results were projected to both MN analysis and direct databases matching to obtain annotations. Finally, the isomers were discriminated and annotated by their extracted ion chromatogram. Through the combination of these approaches, a total of 51 compounds were annotated and dereplicated in the Sophora flavescens samples. The annotation results showed DIA and DDA approach are complementary to each other. MN on GNPS can overcome the challenges of trace compound identification compared to direct DB matching. This strategy provides a powerful tool for the dereplication study in plant chemistry.

Dereplicating by standalone LC-MS remains challenging for low-abundance biflavonoids (BFVs) in Selaginella extracts, as structural isomers share identical molecular weights, possess diverse interflavonoid linkages, and exhibit highly similar fragmentation patterns, thereby increasing the risk of misannotation. Here, an integrated high-performance liquid chromatography (HPLC)-statistical total correlation spectroscopy (STOCSY) workflow was introduced to enable cross-identification of BFVs across partially resolved fractions in two Selaginella extracts, namely Selaginella plana , and S. padangensis . In the workflow, initial screening of crude extracts was performed by matching HPLC retention time against an in-house BFV library. Subsequent STOCSY analysis of partially resolved fractions obtained from S. plana , enabled orthogonal validation of known BFVs. Crucially, unmatched correlated signals guided the selection of target fractions for purification and characterization, leading to the discovery of planaflavone A, an undescribed lanaroft-type BFV. A comparable HPLC RT observed in S. padangensis further directed the isolation of an additional lanaroft-type BFV, padangeflavone A. Collectively, this integrative workflow accelerated compound discovery by enabling discrimination of BFVs with diverse linkage types that are indistinguishable by LC–MS.

Advanced techniques can accelerate the pace of natural product discovery from microbes, which has been lagging behind the drug discovery era. Therefore, the present review article discusses the various interdisciplinary and cutting-edge techniques to present a concrete strategy that enables the high-throughput screening of novel natural compounds (NCs) from known microbes. Recent bioinformatics methods revealed that the microbial genome contains a huge untapped reservoir of silent biosynthetic gene clusters (BGC). This article describes several methods to identify the microbial strains with hidden mines of silent BGCs. Moreover, antiSMASH 5.0 is a free, accurate, and highly reliable bioinformatics tool discussed in detail to identify silent BGCs in the microbial genome. Further, the latest microbial culture technique, HiTES (high-throughput elicitor screening), has been detailed for the expression of silent BGCs using 500–1000 different growth conditions at a time. Following the expression of silent BGCs, the latest mass spectrometry methods are highlighted to identify the NCs. The recently emerged LAESI-IMS (laser ablation electrospray ionization-imaging mass spectrometry) technique, which enables the rapid identification of novel NCs directly from microtiter plates, is presented in detail. Finally, various trending ‘dereplication’ strategies are emphasized to increase the effectiveness of NC screening.

Actinomycetes have been proven to be an excellent source of secondary metabolites for more than half a century. Exhibiting various bioactivities, they provide valuable approved drugs in clinical use. Most microorganisms are still untapped in terms of their capacity to produce secondary metabolites, since only a small fraction can be cultured in the laboratory. Thus, improving cultivation techniques to extend the range of secondary metabolite producers accessible under laboratory conditions is an important first step in prospecting underexplored sources for the isolation of novel antibiotics. Currently uncultured actinobacteria can be made available by bioprospecting extreme or simply habitats other than soil. Furthermore, bioinformatic analysis of genomes reveals most producers to harbour many more biosynthetic gene clusters than compounds identified from any single strain, which translates into a silent biosynthetic potential of the microbial world for the production of yet unknown natural products. This review covers discovery strategies and innovative methods recently employed to access the untapped reservoir of natural products. The focus is the order of actinomycetes although most approaches are similarly applicable to other microbes. Advanced cultivation methods, genomics- and metagenomics-based approaches, as well as modern metabolomics-inspired methods are highlighted to emphasise the interplay of different disciplines to improve access to novel natural products.