Genome Mining Tools Research Articles

Identification of clusters of secondary metabolite biosynthetic genes in the Camelina sativa genome

Multidrug-resistant pathogens pose an earnest risk to human health. Therefore, new antibiotics need to be developed quickly. Most of the antibiotics we use today are derived from secondary metabolites, which are produced by plants. Genome mining tools allow us to detect biosynthetic gene clusters (BGCs) responsible for the production of secondary metabolites. Focusing on the most promising BGCs-coding antibiotics with unique pathways is currently a challenge. In silico approach like genome mining are used to visualise the action of these bioactive chemicals. Camelina sativa is a well-known medicinal plant and it would be interesting to study its secondary metabolites. In this work, we found seven bioactive compounds in this plant using the genome mining approach. Further, the clusters of genes involved in the biosynthesis of these compounds were analysed with their metabolic pathways. This work illuminates new ground on the evolution of BGCs for the nutritional improvement of C. sativa.

Whole-genome sequencing of Pseudoalteromonas piscicida 2515 revealed its antibacterial potency against Vibrio anguillarum: a preliminary invitro study.

Pseudoalteromonas piscicida 2515, isolated from Litopenaeus vannamei culture water, is a potential marine probiotic with broad anti-Vibrio properties. However, genomic information on P. piscicida 2515 is scarce. In this study, the general genomic characteristics and probiotic properties of the P. piscicida 2515 strain were analysed. In addition, we determined the antibacterial mechanism of this bacterial strain by scanning electron microscopy (SEM). The results indicated that the whole-genome sequence of P. piscicida 2515 contained one chromosome and one plasmid, including a total length of 5,541,406bp with a G + C content of 43.24%, and 4679 protein-coding genes were predicted. Various adhesion-related genes, amino acid and vitamin metabolism and biosynthesis genes, and stress-responsive genes were found with genome mining tools. The presence of genes encoding chitin, bromocyclic peptides, lantibiotics, and sactipeptides showed the strong antibacterial activity of the P. piscicida 2515 strain. Moreover, in coculture with Vibrio anguillarum, P. piscicida 2515 displayed vesicle/pilus-like structures located on its surface that possibly participated in its bactericidal activity, representing an antibacterial mechanism. Additionally, 16 haemolytic genes and 3 antibiotic resistance genes, including tetracycline, fluoroquinolone, and carbapenem were annotated, but virulence genes encoding enterotoxin FM (entFM), cereulide (ces), and cytotoxin K were not detected. Further tests should be conducted to confirm the safety characteristics of P. piscicida 2515, including long-term toxicology tests, ecotoxicological assessment, and antibiotic resistance transfer risk assessment. Our results here revealed a new understanding of the probiotic properties and antibacterial mechanism of P. piscicida 2515, in addition to theoretical information for its application in aquaculture.

Discovery of fungal onoceroid triterpenoids through domainless enzyme-targeted global genome mining

Genomics-guided methodologies have revolutionized the discovery of natural products. However, a major challenge in the field of genome mining is determining how to selectively extract biosynthetic gene clusters (BGCs) for untapped natural products from numerous available genome sequences. In this study, we developed a fungal genome mining tool that extracts BGCs encoding enzymes that lack a detectable protein domain (i.e., domainless enzymes) and are not recognized as biosynthetic proteins by existing bioinformatic tools. We searched for BGCs encoding a homologue of Pyr4-family terpene cyclases, which are representative examples of apparently domainless enzymes, in approximately 2000 fungal genomes and discovered several BGCs with unique features. The subsequent characterization of selected BGCs led to the discovery of fungal onoceroid triterpenoids and unprecedented onoceroid synthases. Furthermore, in addition to the onoceroids, a previously unreported sesquiterpene hydroquinone, of which the biosynthesis involves a Pyr4-family terpene cyclase, was obtained. Our genome mining tool has broad applicability in fungal genome mining and can serve as a beneficial platform for accessing diverse, unexploited natural products.

Discovery of Borosin Catalytic Strategies and Function through Bioinformatic Profiling.

Borosins are ribosomally synthesized and post-translationally modified peptides (RiPPs) containing backbone α-N-methylations. These modifications confer favorable pharmacokinetic properties including increased membrane permeability and resistance to proteolytic degradation. Previous studies have biochemically and bioinformatically explored several borosins, revealing (1) numerous domain architectures and (2) diverse core regions lacking conserved sequence elements. Due to these characteristics, large-scale computational identification of borosin biosynthetic genes remains challenging and often requires additional, time-intensive manual inspection. This work builds upon previous findings and updates the genome-mining tool RODEO to automatically evaluate borosin biosynthetic gene clusters (BGCs) and identify putative precursor peptides. Using the new RODEO module, we provide an updated analysis of borosin BGCs identified in the NCBI database. From our data set, we bioinformatically predict and experimentally characterize a new fused borosin domain architecture, in which the modified natural product core is encoded N-terminal to the methyltransferase domain. Additionally, we demonstrate that a borosin precursor peptide is a native substrate of shewasin A, a reported aspartyl peptidase with no previously identified substrates. Shewasin A requires post-translational modification of the leader peptide for proteolytic maturation, a feature not previously observed in RiPPs. Overall, this work provides a user-friendly and open-access tool for the analysis of borosin BGCs and we demonstrate its utility to uncover additional biosynthetic strategies within the borosin class of RiPPs.

BGCFlow: systematic pangenome workflow for the analysis of biosynthetic gene clusters across large genomic datasets.

Genome mining is revolutionizing natural products discovery efforts. The rapid increase in available genomes demands comprehensive computational platforms to effectively extract biosynthetic knowledge encoded across bacterial pangenomes. Here, we present BGCFlow, a novel systematic workflow integrating analytics for large-scale genome mining of bacterial pangenomes. BGCFlow incorporates several genome analytics and mining tools grouped into five common stages of analysis such as:(i) data selection, (ii) functional annotation, (iii) phylogenetic analysis, (iv) genome mining, and (v) comparative analysis. Furthermore, BGCFlow provides easy configuration of different projects, parallel distribution, scheduled job monitoring, an interactive database to visualize tables, exploratory Jupyter Notebooks, and customized reports. Here, we demonstrate the application of BGCFlow by investigating the phylogenetic distribution of various biosynthetic gene clusters detected across 42 genomes of the Saccharopolyspora genus, known to produce industrially important secondary/specialized metabolites. The BGCFlow-guided analysis predicted more accurate dereplication of BGCs and guided the targeted comparative analysis of selected RiPPs. The scalable, interoperable, adaptable, re-entrant,and reproducible nature of the BGCFlow will provide an effective novel way to extract the biosynthetic knowledge fromthe ever-growing genomic datasets of biotechnologically relevant bacterial species.

Biosynthetic potential of the sediment microbial subcommunities of an unexplored karst ecosystem and its ecological implications.

Microbial communities from various environments have been studied in the quest for new natural products with a broad range of applications in medicine and biotechnology. We employed an enrichment method and genome mining tools to examine the biosynthetic potential of microbial communities in the sediments of a coastal sinkhole within the karst ecosystem of the Yucatán Peninsula, Mexico. Our investigation led to the detection of 203 biosynthetic gene clusters (BGCs)and 55 secondary metabolites (SMs)within 35 high-quality metagenome-assembled genomes (MAGs)derived from these subcommunities. The most abundant types of BGCs were Terpene, Nonribosomal peptide-synthetase, and Type III polyketide synthase. Some of the in silico identified BGCs and SMs have been previously reported to exhibit biological activities against pathogenic bacteria and fungi. Others could play significant roles in the sinkhole ecosystem, such as iron solubilization and osmotic stress protection. Interestingly, 75% of the BGCs showed no sequence homology with bacterial BGCs previously reported in the MiBIG database. This suggests that the microbial communities in this environment could be an untapped source of genes encoding novel specialized compounds. The majority of the BGCs were identified in pathways found in the genus Virgibacillus, followed by Sporosarcina, Siminovitchia, Rhodococcus, and Halomonas. The latter, along with Paraclostridium and Lysinibacillus, had the highest number of identified BGC types. This study offers fresh insights into the potential ecological role of SMs from sediment microbial communities in an unexplored environment, underscoring their value as a source of novel natural products.

In Silico Screening of Bacteriocin Gene Clusters within a Set of Marine Bacillota Genomes.

Due to their potential application as an alternative to antibiotics, bacteriocins, which are ribosomally synthesized antimicrobial peptides produced by bacteria, have received much attention in recent years. To identify bacteriocins within marine bacteria, most of the studies employed a culture-based method, which is more time-consuming than the in silico approach. For that, the aim of this study was to identify potential bacteriocin gene clusters and their potential producers in 51 marine Bacillota (formerly Firmicutes) genomes, using BAGEL4, a bacteriocin genome mining tool. As a result, we found out that a majority of selected Bacillota (60.78%) are potential bacteriocin producers, and we identified 77 bacteriocin gene clusters, most of which belong to class I bacteriocins known as RiPPs (ribosomally synthesized and post-translationally modified peptides). The identified putative bacteriocin gene clusters are an attractive target for further in vitro research, such as the production of bacteriocins using a heterologous expression system.

Machine Learning-Enabled Genome Mining and Bioactivity Prediction of Natural Products.

Natural products (NPs) produced by microorganisms and plants are a major source of drugs, herbicides, and fungicides. Thanks to recent advances in DNA sequencing, bioinformatics, and genome mining tools, a vast amount of data on NP biosynthesis has been generated over the years, which has been increasingly exploited to develop machine learning (ML) tools for NP discovery. In this review, we discuss the latest advances in developing and applying ML tools for exploring the potential NPs that can be encoded by genomic language and predicting the types of bioactivities of NPs. We also examine the technical challenges associated with the development and application of ML tools for NP research.

Probing the Phycosphere: Techniques to Study Bacteria-Phytoplankton Interactions

Synopsis The cumulative outcome of bacteria-phytoplankton cell-cell interactions has global-scale consequences that necessitate a more comprehensive understanding of the species that form these relationships, the chemical exchanges that govern them, and the chemical cues that trigger them. However, the diffuse liquid environment supporting these exchanges is inherently difficult to interrogate, which has moved researchers to combine multi-omics analyses, genome mining tools, genetic probes, and mathematical models to gain insight into the species and chemical networks existing around individual phytoplankton cells. Yet, fundamental questions still remain about these micro-scale interactions, creating an opportunity for innovating new methods where biology and chemistry interface with engineering and mathematics.

FunARTS, the Fungal bioActive compound Resistant TargetSeeker, an exploration engine for target-directed genome mining in fungi.

There is an urgent need to diversify the pipeline for discovering novel natural products due to the increase in multi-drug resistant infections. Like bacteria, fungi also produce secondary metabolites that have potent bioactivity and rich chemical diversity. To avoid self-toxicity, fungi encode resistance genes which are often present within the biosynthetic gene clusters (BGCs) of the corresponding bioactive compounds. Recent advances in genome mining tools have enabled the detection and prediction of BGCs responsible for the biosynthesis of secondary metabolites. The main challenge now is to prioritize the most promising BGCs that produce bioactive compounds with novel modes of action. With target-directed genome mining methods, it is possible to predict the mode of action of a compound encoded in an uncharacterized BGC based on the presence of resistant target genes. Here, we introduce the 'fungal bioactive compound resistant target seeker' (FunARTS) available at https://funarts.ziemertlab.com. This is a specific and efficient mining tool for the identification of fungal bioactive compounds with interesting and novel targets. FunARTS rapidly links housekeeping and known resistance genes to BGC proximity and duplication events, allowing for automated, target-directed mining of fungal genomes. Additionally, FunARTS generates gene cluster networking by comparing the similarity of BGCs from multi-genomes.

Evaluation of the safety and probiotic properties of Lactobacillus gasseri LGZ1029 based on whole genome analysis

Lactobacillus gasseri is a kind of facultative anaerobic lactic acid bacterium, and has been defined as GRAS microorganism by the US Food and Drug Administration. We reported the whole genome sequence of L. gasseri LGZ1029. The complete genome of L. gasseri LGZ1029 was 2,027,325 bp encoding 2005 genes with an average G + C content of 35.06%. Based on the analysis of genes involved in antibiotic resistance, virulence factor, and production of harmful metabolites, we have speculated that L. gasseri LGZ1029 is safe. By genome mining tool, a complete plnABCD operon was present in the genome of L. gasseri LGZ1029, indicating that it has the potential to generate antimicrobial bacteriocin. Stress-related genes speculated that L. gasseri LGZ1029 had good gastrointestinal tolerance, bacteriostatic ability, and antioxidant activity with excellent probiotic characteristics, indicating that it can be a probiotic candidate with great potential.

Assessment of the safety and probiotic characteristics of Lactobacillus salivarius CGMCC20700 based on whole-genome sequencing and phenotypic analysis.

Lactic acid bacteria are generally regarded as alternatives to antibiotics in livestock and poultry farming, especially Lactobacillus strains, which are safe and have probiotic potential. Although Lactobacillus salivarius has long been proposed to be a probiotic, the understanding of the roles of this species is still in its infancy. Here, a strain of L. salivarius CGMCC20700 isolated from the intestinal mucosa of Yunnan black-bone chicken broilers was investigated in the context of its safety and probiotic characteristics by whole-genome sequencing in parallel with phenotypic analysis. Whole-genome sequencing results showed that L. salivarius CGMCC20700 has a single scaffold of 1,737,577 bp with an average guanine-to-cytosine (GC) ratio of 33.51% and 1,757 protein-coding genes. The annotation of Clusters of Orthologous Groups (COG) classified the predicted proteins from the assembled genome as possessing cellular, metabolic, and information-related functions. Sequences related to risk assessment, such as antibiotic resistance and virulence genes, were identified, and the strain was further confirmed as safe according to the results of antibiotic resistance, hemolytic, and acute oral toxicology tests. Two gene clusters of antibacterial compounds and broad-spectrum antimicrobial activity were identified using genome mining tools and antibacterial spectrum tests. Stress resistance genes, active stressor removal genes, and adhesion related genes that were identified and examined with various phenotypic assays (such as stress tolerance tests in acids and bile salts and auto aggregation and hydrophobicity assays). The strain showed a high survival rate in the presence of bile salts and under acidic conditions and exhibited significant auto aggregation capacity and hydrophobicity. Overall, L. salivarius CGMCC20700 demonstrated excellent safety and probiotic potential at both the genomic and physiological levels and can be considered an appropriate candidate probiotic for livestock and poultry farming.

Synergy between Genome Mining, Metabolomics, and Bioinformatics Uncovers Antibacterial Chlorinated Carbazole Alkaloids and Their Biosynthetic Gene Cluster from Streptomyces tubbatahanensis sp. nov., a Novel Actinomycete Isolated from Sulu Sea, Philippines.

In this study, a novel actinomycete strain, DSD3025T, isolated from the underexplored marine sediments in Tubbataha Reefs Natural Park, Sulu Sea, Philippines, with the proposed name Streptomyces tubbatahanensis sp. nov., was described using polyphasic approaches and characterized using whole-genome sequencing. Its specialized metabolites were profiled using mass spectrometry and nuclear magnetic resonance analyses, followed by antibacterial, anticancer, and toxicity screening. The S. tubbatahanensis DSD3025T genome was comprised of 7.76 Mbp with a 72.3% G+C content. The average nucleotide identity and digital DNA-DNA hybridization values were 96.5% and 64.1%, respectively, compared with its closest related species, thus delineating the novelty of Streptomyces species. The genome encoded 29 putative biosynthetic gene clusters (BGCs), including a BGC region containing tryptophan halogenase and its associated flavin reductase, which were not found in its close Streptomyces relatives. The metabolite profiling unfolded six rare halogenated carbazole alkaloids, with chlocarbazomycin A as the major compound. A biosynthetic pathway for chlocarbazomycin A was proposed using genome mining, metabolomics, and bioinformatics platforms. Chlocarbazomycin A produced by S. tubbatahanensis DSD3025T has antibacterial activities against Staphylococcus aureus ATCC BAA-44 and Streptococcus pyogenes and showed antiproliferative activity against colon (HCT-116) and ovarian (A2780) human cancer cell lines. Chlocarbazomycin A exhibited no toxicity to liver cells but moderate and high toxicity to kidney and cardiac cell lines, respectively. IMPORTANCE Streptomyces tubbatahanensis DSD3025T is a novel actinomycete with antibiotic and anticancer activities from Tubbataha Reefs Natural Park, a United Nations Educational, Scientific and Cultural Organization World Heritage Site in Sulu Sea and considered one of the Philippines' oldest and most-well-protected marine ecosystems. In silico genome mining tools were used to identify putative BGCs that led to the discovery of genes involved in the production of halogenated carbazole alkaloids and new natural products. By integrating bioinformatics-driven genome mining and metabolomics, we unearthed the hidden biosynthetic richness and mined the associated chemical entities from the novel Streptomyces species. The bioprospecting of novel Streptomyces species from marine sediments of underexplored ecological niches serves as an important source of antibiotic and anticancer drug leads with unique chemical scaffolds.

Navigating and expanding the roadmap of natural product genome mining tools.

Natural products are structurally highly diverse and exhibit a wide array of biological activities. As a result, they serve as an important source of new drug leads. Traditionally, natural products have been discovered by bioactivity-guided fractionation. The advent of genome sequencing technology has resulted in the introduction of an alternative approach towards novel natural product scaffolds: Genome mining. Genome mining is an in-silico natural product discovery strategy in which sequenced genomes are analyzed for the potential of the associated organism to produce natural products. Seemingly universal biosynthetic principles have been deciphered for most natural product classes that are used to detect natural product biosynthetic gene clusters using pathway-encoded conserved key enzymes, domains, or motifs as bait. Several generations of highly sophisticated tools have been developed for the biosynthetic rule-based identification of natural product gene clusters. Apart from these hard-coded algorithms, multiple tools that use machine learning-based approaches have been designed to complement the existing genome mining tool set and focus on natural product gene clusters that lack genes with conserved signature sequences. In this perspective, we take a closer look at state-of-the-art genome mining tools that are based on either hard-coded rules or machine learning algorithms, with an emphasis on the confidence of their predictions and potential to identify non-canonical natural product biosynthetic gene clusters. We highlight the genome mining pipelines' current strengths and limitations by contrasting their advantages and disadvantages. Moreover, we introduce two indirect biosynthetic gene cluster identification strategies that complement current workflows. The combination of all genome mining approaches will pave the way towards a more comprehensive understanding of the full biosynthetic repertoire encoded in microbial genome sequences.

Mining elements of siderophore chirality encoded in microbial genomes.

The vast majority of bacteria require iron to grow. A significant iron acquisition strategy is the production of siderophores, which are secondary microbial metabolites synthesized to sequester iron(III). Siderophore structures encompass a variety of forms, of which highly modified peptidic siderophores are of interest herein. State-of-the-art genome mining tools, such as antiSMASH (antibiotics & Secondary Metabolite Analysis SHell), hold the potential to predict and discover new peptidic siderophores, including a combinatoric suite of triscatechol siderophores framed on a triserine-ester backbone of the general class, (DHB-l/d CAA-l Ser)3 (CAA, cationic amino acid). Siderophores with l/d Arg, l/d Lys and l Orn, but not d Orn, were predicted in bacterial genomes. Fortuitously the d Orn siderophore was identified, yet its lack of prediction highlights the limitation of current genome mining tools. The full combinatoric suite of these siderophores, which form chiral iron(III) complexes, reveals stereospecific coordination chemistry encoded in microbial genomes. The chirality embedded in this suite of Fe(III)-siderophores raises the question of whether the relevant siderophore-mediated iron acquisition pathways are stereospecific and selective for ferric siderophore complexes of a defined configuration.

Genome-guided purification and characterization of polymyxin A1 from Paenibacillus thiaminolyticus SY20: A rarely explored member of polymyxins.

Polymyxin A1 was a rarely investigated member in the polymyxins family produced by Bacillus aerosporus. As a cyclic non-ribosomal lipopeptide, it was purified from Paenibacillus thiaminolyticus for the first time. The producing strain SY20 was screened from Chinese natural fermented bamboo shoots and identified as P. thiaminolyticus SY20 using 16S rRNA homology along with whole genome sequencing. The optimum incubation time was 32 h by the growth kinetics of antimicrobial agent production. The proteinaceous nature of antimicrobial agents was characterized according to the physicochemical properties of the cell-free supernatant. Subsequently, the active antimicrobial agent was purified from the supernatant using ammonium sulfate-graded precipitation, ion-exchange chromatography, and C18-H chromatography. The active agent was identified as polymyxin A1 with a molecular weight 1156.7 Da and antimicrobial activity mainly against Gram-negative bacteria. The molecular structure, a cyclic heptapeptide and a tripeptide side chain acylated by a fatty acid at the amino terminus, was elucidated using the combination of liquid chromatography-tandem mass spectrometry (LC-MS/MS), matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS), amino acid analysis, and whole genome mining tool. Meanwhile, the biosynthetic gene cluster of polymyxin A1 including five open reading frames (ORFs) was demonstrated in the genome. The compound should be further explored for its efficacy and toxicity in vivo to develop its application.

Trichoderma - genomes and genomics as treasure troves for research towards biology, biotechnology and agriculture.

The genus Trichoderma is among the best studied groups of filamentous fungi, largely because of its high relevance in applications from agriculture to enzyme biosynthesis to biofuel production. However, the physiological competences of these fungi, that led to these beneficial applications are intriguing also from a scientific and ecological point of view. This review therefore summarizes recent developments in studies of fungal genomes, updates on previously started genome annotation efforts and novel discoveries as well as efforts towards bioprospecting for enzymes and bioactive compounds such as cellulases, enzymes degrading xenobiotics and metabolites with potential pharmaceutical value. Thereby insights are provided into genomes, mitochondrial genomes and genomes of mycoviruses of Trichoderma strains relevant for enzyme production, biocontrol and mycoremediation. In several cases, production of bioactive compounds could be associated with responsible genes or clusters and bioremediation capabilities could be supported or predicted using genome information. Insights into evolution of the genus Trichoderma revealed large scale horizontal gene transfer, predominantly of CAZyme genes, but also secondary metabolite clusters. Investigation of sexual development showed that Trichoderma species are competent of repeat induced point mutation (RIP) and in some cases, segmental aneuploidy was observed. Some random mutants finally gave away their crucial mutations like T. reesei QM9978 and QM9136 and the fertility defect of QM6a was traced back to its gene defect. The Trichoderma core genome was narrowed down to 7000 genes and gene clustering was investigated in the genomes of multiple species. Finally, recent developments in application of CRISPR/Cas9 in Trichoderma, cloning and expression strategies for the workhorse T. reesei as well as the use genome mining tools for bioprospecting Trichoderma are highlighted. The intriguing new findings on evolution, genomics and physiology highlight emerging trends and illustrate worthwhile perspectives in diverse fields of research with Trichoderma.

Distribution of biosynthetic gene clusters for complestatin and related compounds in Streptomyces spp.

Aim. In current work we have analyzed 2664 genomes of Streptomyces spp. (publically available from GenBank) searching for complestatin-like biosynthetic gene clusters (BGCs). Then we aimed to reconstruct in silico putative biosynthetic pathways, encoded within these BGCs, estimating the chemical variability of complestatin-like compounds from natural sources. Methods. Widely accepted genome-mining tools and approaches for phylogenetic reconstruction were utilized in this work. Results. 53 genomes of Streptomyces spp. were found to contain complestatin-like BGCs, although only 33 BGCs were found within one contig – others were partial or highly fragmented. Reconstruction of multi-locus phylogeny for 33 found BGCs and complestatin BGC allowed to divide all these BGCs into five phylogenetic subgroups. Representatives of each subgroup exhibited characteristic organization of corresponding BGCs. Reconstruction of putative biosynthetic pathways allowed us to predict that discovered BGCs might potentially code the biosynthesis of new complestatin derivatives: norcomplestatin, N-malonyl-norcomplestatin, and N-acetyl-norcomplestatin. Conclusions. Complestatin-like BGCs are widely distributed among Streptomyces spp. and might encode novel complestain derivatives, which merits further experimental investigation.

How to Completely Squeeze a Fungus-Advanced Genome Mining Tools for Novel Bioactive Substances.

Fungal species have the capability of producing an overwhelming diversity of bioactive substances that can have beneficial but also detrimental effects on human health. These so-called secondary metabolites naturally serve as antimicrobial “weapon systems”, signaling molecules or developmental effectors for fungi and hence are produced only under very specific environmental conditions or stages in their life cycle. However, as these complex conditions are difficult or even impossible to mimic in laboratory settings, only a small fraction of the true chemical diversity of fungi is known so far. This also implies that a large space for potentially new pharmaceuticals remains unexplored. We here present an overview on current developments in advanced methods that can be used to explore this chemical space. We focus on genetic and genomic methods, how to detect genes that harbor the blueprints for the production of these compounds (i.e., biosynthetic gene clusters, BGCs), and ways to activate these silent chromosomal regions. We provide an in-depth view of the chromatin-level regulation of BGCs and of the potential to use the CRISPR/Cas technology as an activation tool.

Genome Mining as an Alternative Way for Screening the Marine Organisms for Their Potential to Produce UV-Absorbing Mycosporine-like Amino Acid.

Mycosporine-like amino acids (MAAs) are small molecules with robust ultraviolet (UV)-absorbing capacities and a huge potential to be used as an environmentally friendly natural sunscreen. MAAs, temperature, and light-stable compounds demonstrate powerful photoprotective capacities and the ability to capture light in the UV-A and UV-B ranges without the production of damaging free radicals. The biotechnological uses of these secondary metabolites have been often limited by the small quantities restored from natural resources, variation in MAA expression profiles, and limited success in heterologous expression systems. Overcoming these obstacles requires a better understanding of MAA biosynthesis and its regulatory processes. MAAs are produced to a certain extent via a four-enzyme pathway, including genes encoding enzymes dehydroquinate synthase, enzyme O-methyltransferase, adenosine triphosphate grasp, and a nonribosomal peptide synthetase. However, there are substantial genetic discrepancies in the MAA genetic pathway in different species, suggesting further complexity of this pathway that is yet to be fully explored. In recent years, the application of genome-mining approaches allowed the identification of biosynthetic gene clusters (BGCs) that resulted in the discovery of many new compounds from unconventional sources. This review explores the use of novel genomics tools for linking BGCs and secondary metabolites based on the available omics data, including MAAs, and evaluates the potential of using novel genome-mining tools to reveal a cryptic potential for new bioproduct screening approaches and unrevealing new MAA producers.