Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery

The role of artificial intelligence in the development of anticancer therapeutics from natural polyphenols: Current advances and future prospects

Natural polyphenols, abundant in the human diet, are derived from a wide variety of sources. Numerous preclinical studies have demonstrated their significant anticancer properties against various malignancies, making them valuable resources for drug development. However, traditional experimental methods for developing anticancer therapies from natural polyphenols are time-consuming and labor-intensive. Recently, artificial intelligence has shown promising advancements in drug discovery. Integrating AI technologies into the development process for natural polyphenols can substantially reduce development time and enhance efficiency. In this study, we review the crucial roles of natural polyphenols in anticancer treatment and explore the potential of AI technologies to aid in drug development. Specifically, we discuss the application of AI in key stages such as drug structure prediction, virtual drug screening, prediction of biological activity, and drug-target protein interaction, highlighting the potential to revolutionize the development of natural polyphenol-based anticancer therapies.

Advancing cyanobacterial biotechnology: new frontiers in natural product discovery and production

Cyanobacteria, as oxygenic phototrophs, offer significant potential for sustainable biotechnology applications. Cyanobacterial natural products, with antimicrobial, anticancer, and plant growth-promoting properties, hold promise in pharmaceuticals, agriculture, and environmental remediation. By leveraging advanced technologies, cyanobacteria can significantly impact various industries, supporting the green biotechnology agenda. Recent advancements in integrated omics, orphan gene cluster activation, genetic manipulation, and chemo-enzymatic methods are expanding their biotechnological relevance. Omics technologies revolutionize cyanobacterial natural product research by facilitating biosynthetic gene cluster identification. Heterologous expression and pathway reconstitution enable complex natural product production, while high-titer strategies like metabolic engineering enhance yields. Interdisciplinary research and technological progress position cyanobacteria as valuable sources of bioactive compounds, driving sustainable biotechnological practices forward.

Unveiling biosynthetic potential of an Arctic marine-derived strain Aspergillus sydowii MNP-2

BACKGROUND

A growing number of studies have demonstrated that the polar regions have the potential to be a significant repository of microbial resources and a potential source of active ingredients. Genome mining strategy plays a key role in the discovery of bioactive secondary metabolites (SMs) from microorganisms. This work highlighted deciphering the biosynthetic potential of an Arctic marine-derived strain Aspergillus sydowii MNP-2 by a combination of whole genome analysis and antiSMASH as well as feature-based molecular networking (MN) in the Global Natural Products Social Molecular Networking (GNPS).

RESULTS

In this study, a high-quality whole genome sequence of an Arctic marine strain MNP-2, with a size of 34.9 Mb was successfully obtained. Its total number of genes predicted by BRAKER software was 13,218, and that of non-coding RNAs (rRNA, sRNA, snRNA, and tRNA) predicted by using INFERNAL software was 204. AntiSMASH results indicated that strain MNP-2 harbors 56 biosynthetic gene clusters (BGCs), including 18 NRPS/NRPS-like gene clusters, 10 PKS/PKS-like gene clusters, 8 terpene synthse gene clusters, 5 indole synthase gene clusters, 10 hybrid gene clusters, and 5 fungal-RiPP gene clusters. Metabolic analyses of strain MNP-2 grown on various media using GNPS networking revealed its great potential for the biosynthesis of bioactive SMs containing a variety of heterocyclic and bridge-ring structures. For example, compound G-8 exhibited a potent anti-HIV effect with an IC50 value of 7.2 nM and an EC50 value of 0.9 nM. Compound G-6 had excellent in vitro cytotoxicities against the K562, MCF-7, Hela, DU145, U1975, SGC-7901, A549, MOLT-4, and HL60 cell lines, with IC50 values ranging from 0.10 to 3.3 µM, and showed significant anti-viral (H1N1 and H3N2) activities with IC50 values of 15.9 and 30.0 µM, respectively.

CONCLUSIONS

These findings definitely improve our knowledge about the molecular biology of genus A. sydowii and would effectively unveil the biosynthetic potential of strain MNP-2 using genomics and metabolomics techniques.

Harnessing the potential: advances in cyanobacterial natural product research and biotechnology

Covering: 2000 to 2023Cyanobacteria produce a variety of bioactive natural products that can pose a threat to humans and animals as environmental toxins, but also have potential for or inspire pharmaceutical use. As oxygenic phototrophs, cyanobacteria furthermore hold great promise for sustainable biotechnology. Yet, the necessary tools for exploiting their biotechnological potential have so far been established only for a few model strains of cyanobacteria, while large untapped biosynthetic resources are hidden in slow-growing cyanobacterial genera that are difficult to access by genetic techniques. In recent years, several approaches have been developed to circumvent the bottlenecks in cyanobacterial natural product research. Here, we summarize current progress that has been made in unlocking or characterizing cryptic metabolic pathways using integrated omics techniques, orphan gene cluster activation, use of genetic approaches in original producers, heterologous expression and chemo-enzymatic techniques. We are mainly highlighting genomic mining concepts and strategies towards high-titer production of cyanobacterial natural products from the last 10 years and discuss the need for further research developments in this field.

Artificial intelligence for natural product drug discovery

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Metabolome-guided genome mining of RiPP natural products

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are a chemically diverse class of metabolites. Many RiPPs show potent biological activities that make them attractive starting points for drug development. A promising approach for the discovery of new classes of RiPPs is genome mining. However, the accuracy of genome mining is hampered by the lack of signature genes shared across different RiPP classes. One way to reduce false-positive predictions is by complementing genomic information with metabolomics data. In recent years, several new approaches addressing such integrative genomics and metabolomics analyses have been developed. In this review, we provide a detailed discussion of RiPP-compatible software tools that integrate paired genomics and metabolomics data. We highlight current challenges in data integration and identify opportunities for further developments targeting new classes of bioactive RiPPs.

iPRESTO: Automated discovery of biosynthetic sub-clusters linked to specific natural product substructures

Microbial specialised metabolism is full of valuable natural products that are applied clinically, agriculturally, and industrially. The genes that encode their biosynthesis are often physically clustered on the genome in biosynthetic gene clusters (BGCs). Many BGCs consist of multiple groups of co-evolving genes called sub-clusters that are responsible for the biosynthesis of a specific chemical moiety in a natural product. Sub-clusters therefore provide an important link between the structures of a natural product and its BGC, which can be leveraged for predicting natural product structures from sequence, as well as for linking chemical structures and metabolomics-derived mass features to BGCs. While some initial computational methodologies have been devised for sub-cluster detection, current approaches are not scalable, have only been run on small and outdated datasets, or produce an impractically large number of possible sub-clusters to mine through. Here, we constructed a scalable method for unsupervised sub-cluster detection, called iPRESTO, based on topic modelling and statistical analysis of co-occurrence patterns of enzyme-coding protein families. iPRESTO was used to mine sub-clusters across 150,000 prokaryotic BGCs from antiSMASH-DB. After annotating a fraction of the resulting sub-cluster families, we could predict a substructure for 16% of the antiSMASH-DB BGCs. Additionally, our method was able to confirm 83% of the experimentally characterised sub-clusters in MIBiG reference BGCs. Based on iPRESTO-detected sub-clusters, we could correctly identify the BGCs for xenorhabdin and salbostatin biosynthesis (which had not yet been annotated in BGC databases), as well as propose a candidate BGC for akashin biosynthesis. Additionally, we show for a collection of 145 actinobacteria how substructures can aid in linking BGCs to molecules by correlating iPRESTO-detected sub-clusters to MS/MS-derived Mass2Motifs substructure patterns. This work paves the way for deeper functional and structural annotation of microbial BGCs by improved linking of orphan molecules to their cognate gene clusters, thus facilitating accelerated natural product discovery.

Enhanced correlation-based linking of biosynthetic gene clusters to their metabolic products through chemical class matching

BACKGROUND

It is well-known that the microbiome produces a myriad of specialised metabolites with diverse functions. To better characterise their structures and identify their producers in complex samples, integrative genome and metabolome mining is becoming increasingly popular. Metabologenomic co-occurrence-based correlation scoring methods facilitate the linking of metabolite mass fragmentation spectra (MS/MS) to their cognate biosynthetic gene clusters (BGCs) based on shared absence/presence patterns of metabolites and BGCs in paired omics datasets of multiple strains. Recently, these methods have been made more readily accessible through the NPLinker platform. However, co-occurrence-based approaches usually result in too many candidate links to manually validate. To address this issue, we introduce a generic feature-based correlation method that matches chemical compound classes between BGCs and MS/MS spectra.

RESULTS

To automatically reduce the long lists of potential BGC-MS/MS spectrum links, we match natural product (NP) ontologies previously independently developed for genomics and metabolomics and developed NPClassScore: an empirical class matching score that we also implemented in the NPLinker platform. By applying NPClassScore on three paired omics datasets totalling 189 bacterial strains, we show that the number of links is reduced by on average 63% as compared to using a co-occurrence-based strategy alone. We further demonstrate that 96% of experimentally validated links in these datasets are retained and prioritised when using NPClassScore.

CONCLUSION

The matching genome-metabolome class ontologies provide a starting point for selecting plausible candidates for BGCs and MS/MS spectra based on matching chemical compound class ontologies. NPClassScore expedites genome/metabolome data integration, as relevant BGC-metabolite links are prioritised, and researchers are faced with substantially fewer proposed BGC-MS/MS links to manually inspect. We anticipate that our addition to the NPLinker platform will aid integrative omics mining workflows in discovering novel NPs and understanding complex metabolic interactions in the microbiome. Video Abstract.

Bioassay-Guided Fractionation Leads to the Detection of Cholic Acid Generated by the Rare Thalassomonas sp

Bacterial symbionts of marine invertebrates are rich sources of novel, pharmaceutically relevant natural products that could become leads in combatting multidrug-resistant pathogens and treating disease. In this study, the bioactive potential of the marine invertebrate symbiont Thalassomonas actiniarum was investigated. Bioactivity screening of the strain revealed Gram-positive specific antibacterial activity as well as cytotoxic activity against a human melanoma cell line (A2058). The dereplication of the active fraction using HPLC-MS led to the isolation and structural elucidation of cholic acid and 3-oxo cholic acid. T. actiniarum is one of three type species belonging to the genus Thalassomonas. The ability to generate cholic acid was assessed for all three species using thin-layer chromatography and was confirmed by LC-MS. The re-sequencing of all three Thalassomonas type species using long-read Oxford Nanopore Technology (ONT) and Illumina data produced complete genomes, enabling the bioinformatic assessment of the ability of the strains to produce cholic acid. Although a complete biosynthetic pathway for cholic acid synthesis in this genus could not be determined based on sequence-based homology searches, the identification of putative penicillin or homoserine lactone acylases in all three species suggests a mechanism for the hydrolysis of conjugated bile acids present in the growth medium, resulting in the generation of cholic acid and 3-oxo cholic acid. With little known currently about the bioactivities of this genus, this study serves as the foundation for future investigations into their bioactive potential as well as the potential ecological role of bile acid transformation, sterol modification and quorum quenching by Thalassomonas sp. in the marine environment.

Inventa: A computational tool to discover structural novelty in natural extracts libraries

Collections of natural extracts hold potential for the discovery of novel natural products with original modes of action. The prioritization of extracts from collections remains challenging due to the lack of a workflow that combines multiple-source information to facilitate the data interpretation. Results from different analytical techniques and literature reports need to be organized, processed, and interpreted to enable optimal decision-making for extracts prioritization. Here, we introduce Inventa, a computational tool that highlights the structural novelty potential within extracts, considering untargeted mass spectrometry data, spectral annotation, and literature reports. Based on this information, Inventa calculates multiple scores that inform their structural potential. Thus, Inventa has the potential to accelerate new natural products discovery. Inventa was applied to a set of plants from the Celastraceae family as a proof of concept. The Pristimera indica (Willd.) A.C.Sm roots extract was highlighted as a promising source of potentially novel compounds. Its phytochemical investigation resulted in the isolation and de novo characterization of thirteen new dihydro-β-agarofuran sesquiterpenes, five of them presenting a new 9-oxodihydro-β-agarofuran base scaffold.

Integrated Metabolomic-Genomic Workflows Accelerate Microbial Natural Product Discovery

The pairing of analytical chemistry with genomic techniques represents a new wave in natural product chemistry. With an increase in the availability of sequencing and assembly of microbial genomes, interrogation into the biosynthetic capability of producers with valuable secondary metabolites is possible. However, without the development of robust, accessible, and medium to high throughput tools, the bottleneck in pairing metabolic potential and compound isolation will continue. Several innovative approaches have proven useful in the nascent stages of microbial genome-informed drug discovery. Here, we consider a number of these approaches which have led to prioritization of strain targets and have mitigated rediscovery rates. Likewise, we discuss integration of principles of comparative evolutionary studies and retrobiosynthetic predictions to better understand biosynthetic mechanistic details and link genome sequence to structure. Lastly, we discuss advances in engineering, chemistry, and molecular networking and other computational approaches that are accelerating progress in the field of omic-informed natural product drug discovery. Together, these strategies enhance the synergy between cutting edge omics, chemical characterization, and computational technologies that pitch the discovery of natural products with pharmaceutical and other potential applications to the crest of the wave where progress is ripe for rapid advances.

NP Analyst: An Open Online Platform for Compound Activity Mapping

Few tools exist in natural products discovery to integrate biological screening and untargeted mass spectrometry data at the library scale. Previously, we reported Compound Activity Mapping as a strategy for predicting compound bioactivity profiles directly from primary screening results on extract libraries. We now present NP Analyst, an open online platform for Compound Activity Mapping that accepts bioassay data of almost any type, and is compatible with mass spectrometry data from major instrument manufacturers via the mzML format. In addition, NP Analyst will accept processed mass spectrometry data from the MZmine 2 and GNPS open-source platforms, making it a versatile tool for integration with existing discovery workflows. We demonstrate the utility of this new tool for both the dereplication of known compounds and the discovery of novel bioactive natural products using a challenging low-resolution antimicrobial bioassay data set. This new platform is available at www.npanalyst.org.

Advancements in capturing and mining mass spectrometry data are transforming natural products research

Covering: 2016 up to 2021Mass spectrometry (MS) is an essential technology in natural products research with MS fragmentation (MS/MS) approaches becoming a key tool. Recent advancements in MS yield dense metabolomics datasets which have been, conventionally, used by individual labs for individual projects; however, a shift is brewing. The movement towards open MS data (and other structural characterization data) and accessible data mining tools is emerging in natural products research. Over the past 5 years, this movement has rapidly expanded and evolved with no slowdown in sight; the capabilities of today vastly exceed those of 5 years ago. Herein, we address the analysis of individual datasets, a situation we are calling the '2021 status quo', and the emergent framework to systematically capture sample information (metadata) and perform repository-scale analyses. We evaluate public data deposition, discuss the challenges of working in the repository scale, highlight the challenges of metadata capture and provide illustrative examples of the power of utilizing repository data and the tools that enable it. We conclude that the advancements in MS data collection must be met with advancements in how we utilize data; therefore, we argue that open data and data mining is the next evolution in obtaining the maximum potential in natural products research.