Recent development of computational resources for new antibiotics discovery

Challenges in natural product-based drug discovery assisted with in silico-based methods

The application of traditional medicine by humans for the treatment of ailments as well as improving the quality of life far outdates recorded history. To date, a significant percentage of humans, especially those living in developing/underprivileged communities still rely on traditional medicine for primary healthcare needs. In silico-based methods have been shown to play a pivotal role in modern pharmaceutical drug discovery processes. The application of these methods in identifying natural product (NP)-based hits has been successful. This is very much observed in many research set-ups that use rationally in silico-based methods in combination with experimental validation techniques. The combination has rendered the use of in silico-based approaches even more popular and successful in the investigation of NPs. However, identifying and proposing novel NP-based hits for experimental validation comes with several challenges such as the availability of compounds by suppliers, the huge task of separating pure compounds from complex mixtures, the quantity of samples available from the natural source to be tested, not to mention the potential ecological impact if the natural source is exhausted. Because most peer-reviewed publications are biased towards "positive results", these challenges are generally not discussed in publications. In this review, we highlight and discuss these challenges. The idea is to give interested scientists in this field of research an idea of what they can come across or should be expecting as well as prompting them on how to avoid or fix these issues.

Biosynthetic gene cluster signature profiles of pathogenic Gram-negative bacteria isolated from Egyptian clinical settings

Biosynthetic gene clusters (BGCs) are a subset of consecutive genes present within a variety of organisms to produce specialized metabolites (SMs). These SMs are becoming a cornerstone to produce multiple medications including antibacterial and anticancer agents. Natural products (NPs) also play a pivotal role in enhancing the virulence of ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterobacter spp.), which represent a global health threat. We aimed to sequence and computationally analyze the BGCs present in 66 strains pertaining to three different ESKAPE pathogenic species: 21 A. baumannii, 28 K. pneumoniae, and 17 P. aeruginosa strains recovered from clinical settings in Egypt. DNA was extracted using QIAamp DNA Mini kit and Illumina NextSeq 550 was used for whole-genome sequencing. The sequences were quality-filtered by fastp and assembled by Unicycler. BGCs were detected by antiSMASH, BAGEL, GECCO, and PRISM, and aligned using Clinker. The highest abundance of BGCs was detected in P. aeruginosa (590), then K. pneumoniae (146) and the least in A. baumannii strains (133). P. aeruginosa isolates shared mostly the non-ribosomal peptide synthase (NRPS) type, K. pneumoniae isolates shared the ribosomally synthesized and post-translationally modified peptide-like (RiPP-like) type, while A. baumannii isolates shared the siderophore type. Most of the isolates harbored non-ribosomal peptide (NRP) BGCs with few K. pneumoniae isolates encoding polyketide BGCs. Sactipeptides and bottromycin BGCs were the most frequently detected RiPP clusters. We hypothesize that each species' BGC signature confers its virulence. Future experiments will link the detected clusters with their species and determine whether the encoded SMs are produced and cause their virulence. IMPORTANCE Our study analyzes the biosynthetic gene clusters (BGCs) present in 66 assemblies from clinical ESKAPE pathogen isolates pertaining to Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa strains. We report their sequencing and assembly followed by the analysis of their BGCs using several bioinformatics tools. We then focused on the most abundant BGC type in each species and we discussed their potential roles in the virulence of each species. This study is pivotal to further build on its experimental work that deciphers the role in virulence, possible antibacterial effects, and characterization of the encoded specialized metabolites (SMs). The study highlights the importance of studying the "harmful" BGCs and understanding the pathogenicity and virulence of those species, as well as possible benefits if the SMs were used as antibacterial agents. This could be the first study of its kind from Egypt and would shed light on BGCs from ESKAPE pathogens from Egypt.

Long-Read Metagenome-Assembled Genomes Improve Identification of Novel Complete Biosynthetic Gene Clusters in a Complex Microbial Activated Sludge Ecosystem

Microorganisms produce a wide variety of secondary/specialized metabolites (SMs), the majority of which are yet to be discovered. These natural products play multiple roles in microbiomes and are important for microbial competition, communication, and success in the environment. SMs have been our major source of antibiotics and are used in a range of biotechnological applications. In silico mining for biosynthetic gene clusters (BGCs) encoding the production of SMs is commonly used to assess the genetic potential of organisms. However, as BGCs span tens to over 200 kb, identifying complete BGCs requires genome data that has minimal assembly gaps within the BGCs, a prerequisite that was previously only met by individually sequenced genomes. Here, we assess the performance of the currently available genome mining platform antiSMASH on 1,080 high-quality metagenome-assembled bacterial genomes (HQ MAGs) previously produced from wastewater treatment plants (WWTPs) using a combination of long-read (Oxford Nanopore) and short-read (Illumina) sequencing technologies. More than 4,200 different BGCs were identified, with 88% of these being complete. Sequence similarity clustering of the BGCs implies that the majority of this biosynthetic potential likely encodes novel compounds, and few BGCs are shared between genera. We identify BGCs in abundant and functionally relevant genera in WWTPs, suggesting a role of secondary metabolism in this ecosystem. We find that the assembly of HQ MAGs using long-read sequencing is vital to explore the genetic potential for SM production among the uncultured members of microbial communities. IMPORTANCE Cataloguing secondary metabolite (SM) potential using genome mining of metagenomic data has become the method of choice in bioprospecting for novel compounds. However, accurate biosynthetic gene cluster (BGC) detection requires unfragmented genomic assemblies, which have been technically difficult to obtain from metagenomes until very recently with new long-read technologies. Here, we determined the biosynthetic potential of activated sludge (AS), the microbial community used in resource recovery and wastewater treatment, by mining high-quality metagenome-assembled genomes generated from long-read data. We found over 4,000 BGCs, including BGCs in abundant process-critical bacteria, with no similarity to the BGCs of characterized products. We show how long-read MAGs are required to confidently assemble complete BGCs, and we determined that the AS BGCs from different studies have very little overlap, suggesting that AS is a rich source of biosynthetic potential and new bioactive compounds.

Enterotoxin tilimycin from gut-resident Klebsiella promotes mutational evolution and antibiotic resistance in mice

Klebsiella spp. that secrete the DNA-alkylating enterotoxin tilimycin colonize the human intestinal tract. Numbers of toxigenic bacteria increase during antibiotic use, and the resulting accumulation of tilimycin in the intestinal lumen damages the epithelium via genetic instability and apoptosis. Here we examine the impact of this genotoxin on the gut ecosystem. 16S rRNA sequencing of faecal samples from mice colonized with Klebsiella oxytoca strains and mechanistic analyses show that tilimycin is a pro-mutagenic antibiotic affecting multiple phyla. Transient synthesis of tilimycin in the murine gut antagonized niche competitors, reduced microbial richness and altered taxonomic composition of the microbiota both during and following exposure. Moreover, tilimycin secretion increased rates of mutagenesis in co-resident opportunistic pathogens such as Klebsiella pneumoniae and Escherichia coli, as shown by de novo acquisition of antibiotic resistance. We conclude that tilimycin is a bacterial mutagen, and flares of genotoxic Klebsiella have the potential to drive the emergence of resistance, destabilize the gut microbiota and shape its evolutionary trajectory.

Production of the enterotoxin tilimycin by gut-resident Klebsiella species can alter gut microbiota composition, induce mutational evolution and drive the emergence of antibiotic resistance in mice.

Characterization of siderophores from Escherichia coli strains through genome mining tools: an antiSMASH study

Although urinary tract infections (UTIs) affect many people, they are usually a disease observed in women. UTIs happen when exogenous and endogenous bacteria enter the urinary tract and colonize there. Cystitis and pyelonephritis occur when bacteria infect the bladder and the kidneys, respectively. UTIs become much serious if the bacteria causing the infection are antibiotic resistant. Since the pathogenic microorganisms have been adopted to current antibiotics via genetic variations, UTIs have become an even more severe health problem. Therefore, there is a great need for the discovery of novel antibiotics. Genome mining of nonpathogenic and pathogenic Escherichia coli strains for investigating secondary metabolites were conducted by the antiSMASH analysis. When the resulting secondary metabolites were examined, it was found that some of the siderophores are effective in UTIs. In conclusion, since the siderophore production in E. coli is directly related to UTIs, these molecules can be a good target for development of future pharmaceutical approaches and compounds. Siderophores can also be used in industrial studies due to their higher chelating affinity for iron.

Genome mining on nonpathogenic and pathogenic E. coli was studied.

Comprehensive and comparative analysis of siderophores were investigated.

The results may open a new gate on the development of new drugs on pathogenic E. coli-based diseases.

Green Synthesis of Silver-Decorated Magnetic Particles for Efficient and Reusable Antimicrobial Activity

Metal and metal hybrid nanostructures have shown tremendous application in the biomedical and catalytic fields because of their plasmonic and catalytic properties. Here, a green and clean method was employed for the synthesis of silver nanoparticle (Ag NP)-SiO₂-Fe₂O₃ hybrid microstructures, and biomolecules from green tea extracts were used for constructing the hybrid structures. The SiO₂-Fe₂O₃ structures were synthesized using an ethanolic green tea leaf extract to form Bio-SiO₂-Fe₂O₃ (BSiO₂-Fe₂O₃) structures. Biochemical studies demonstrated the presence of green tea biomolecules in the BSiO₂ layer. Reduction of the silver ions was performed by a BSiO₂ layer to form Ag NPs of 5–10 nm in diameter in and on the BSiO₂-Fe₂O₃ microstructure. The reduction process was observed within 600 s, which is faster than that reported elsewhere. The antimicrobial activity of the Ag-BSiO₂-Fe₂O₃ hybrid structure was demonstrated against Staphylococcus aureus and Escherichia coli, and the nanostructures were further visualized using confocal laser scanning microscopy (CLSM). The magnetic properties of the Ag-BSiO₂-Fe₂O₃ hybrid structure were used for studying reusable antimicrobial activity. Thus, in this study, we provide a novel green route for the construction of a biomolecule-entrapped SiO₂-Fe₂O₃ structure and their use for the ultra-fast formation of Ag NPs to form antimicrobial active multifunctional hybrid structures.

Mining and unearthing hidden biosynthetic potential

Genetically encoded small molecules (secondary metabolites) play eminent roles in ecological interactions, as pathogenicity factors and as drug leads. Yet, these chemical mediators often evade detection, and the discovery of novel entities is hampered by low production and high rediscovery rates. These limitations may be addressed by genome mining for biosynthetic gene clusters, thereby unveiling cryptic metabolic potential. The development of sophisticated data mining methods and genetic and analytical tools has enabled the discovery of an impressive array of previously overlooked natural products. This review shows the newest developments in the field, highlighting compound discovery from unconventional sources and microbiomes.

Natural products are an important source of bioactive compounds and have versatile applications in different fields, but their discovery is challenging. Here, the authors review the recent developments in genome mining for discovery of natural products, focusing on compounds from unconventional microorganisms and microbiomes.

Predicting biochemical and physiological effects of natural products from molecular structures using machine learning

Covering: 2016 to 2021Discovery of novel natural products has been greatly facilitated by advances in genome sequencing, genome mining and analytical techniques. As a result, the volume of data for natural products has increased over the years, which started to serve as ingredients for developing machine learning models. In the past few years, a number of machine learning models have been developed to examine various aspects of a molecule by effectively processing its molecular structure. Understanding of the biological effects of natural products can benefit from such machine learning approaches. In this context, this Highlight reviews recent studies on machine learning models developed to infer various biological effects of molecules. A particular attention is paid to molecular featurization, or computational representation of a molecular structure, which is an essential process during the development of a machine learning model. Technical challenges associated with the use of machine learning for natural products are further discussed.

Recent Advances in Discovery of Lead Structures from Microbial Natural Products: Genomics- and Metabolomics-Guided Acceleration

Natural products (NPs) are evolutionarily optimized as drug-like molecules and remain the most consistently successful source of drugs and drug leads. They offer major opportunities for finding novel lead structures that are active against a broad spectrum of assay targets, particularly those from secondary metabolites of microbial origin. Due to traditional discovery approaches’ limitations relying on untargeted screening methods, there is a growing trend to employ unconventional secondary metabolomics techniques. Aided by the more in-depth understanding of different biosynthetic pathways and the technological advancement in analytical instrumentation, the development of new methodologies provides an alternative that can accelerate discoveries of new lead-structures of natural origin. This present mini-review briefly discusses selected examples regarding advancements in bioinformatics and genomics (focusing on genome mining and metagenomics approaches), as well as bioanalytics (mass-spectrometry) towards the microbial NPs-based drug discovery and development. The selected recent discoveries from 2015 to 2020 are featured herein.

Automatic reconstruction of metabolic pathways from identified biosynthetic gene clusters

Background

A wide range of bioactive compounds is produced by enzymes and enzymatic complexes encoded in biosynthetic gene clusters (BGCs). These BGCs can be identified and functionally annotated based on their DNA sequence. Candidates for further research and development may be prioritized based on properties such as their functional annotation, (dis)similarity to known BGCs, and bioactivity assays. Production of the target compound in the native strain is often not achievable, rendering heterologous expression in an optimized host strain as a promising alternative. Genome-scale metabolic models are frequently used to guide strain development, but large-scale incorporation and testing of heterologous production of complex natural products in this framework is hampered by the amount of manual work required to translate annotated BGCs to metabolic pathways. To this end, we have developed a pipeline for an automated reconstruction of BGC associated metabolic pathways responsible for the synthesis of non-ribosomal peptides and polyketides, two of the dominant classes of bioactive compounds.

Results

The developed pipeline correctly predicts 72.8% of the metabolic reactions in a detailed evaluation of 8 different BGCs comprising 228 functional domains. By introducing the reconstructed pathways into a genome-scale metabolic model we demonstrate that this level of accuracy is sufficient to make reliable in silico predictions with respect to production rate and gene knockout targets. Furthermore, we apply the pipeline to a large BGC database and reconstruct 943 metabolic pathways. We identify 17 enzymatic reactions using high-throughput assessment of potential knockout targets for increasing the production of any of the associated compounds. However, the targets only provide a relative increase of up to 6% compared to wild-type production rates.

Conclusion

With this pipeline we pave the way for an extended use of genome-scale metabolic models in strain design of heterologous expression hosts. In this context, we identified generic knockout targets for the increased production of heterologous compounds. However, as the predicted increase is minor for any of the single-reaction knockout targets, these results indicate that more sophisticated strain-engineering strategies are necessary for the development of efficient BGC expression hosts.

Transporter genes in biosynthetic gene clusters predict metabolite characteristics and siderophore activity

Biosynthetic gene clusters (BGCs) are operonic sets of microbial genes that synthesize specialized metabolites with diverse functions, including siderophores and antibiotics, which often require export to the extracellular environment. For this reason, genes for transport across cellular membranes are essential for the production of specialized metabolites and are often genomically colocalized with BGCs. Here, we conducted a comprehensive computational analysis of transporters associated with characterized BGCs. In addition to known exporters, in BGCs we found many importer-specific transmembrane domains that co-occur with substrate binding proteins possibly for uptake of siderophores or metabolic precursors. Machine learning models using transporter gene frequencies were predictive of known siderophore activity, molecular weights, and a measure of lipophilicity (log P) for corresponding BGC-synthesized metabolites. Transporter genes associated with BGCs were often equally or more predictive of metabolite features than biosynthetic genes. Given the importance of siderophores as pathogenicity factors, we used transporters specific for siderophore BGCs to identify both known and uncharacterized siderophore-like BGCs in genomes from metagenomes from the infant and adult gut microbiome. We find that 23% of microbial genomes from premature infant guts have siderophore-like BGCs, but only 3% of those assembled from adult gut microbiomes do. Although siderophore-like BGCs from the infant gut are predominantly associated with Enterobacteriaceae and Staphylococcus, siderophore-like BGCs can be identified from taxa in the adult gut microbiome that have rarely been recognized for siderophore production. Taken together, these results show that consideration of BGC-associated transporter genes can inform predictions of specialized metabolite structure and function.

The Integration of Genome Mining, Comparative Genomics, and Functional Genetics for Biosynthetic Gene Cluster Identification

Antimicrobial resistance is a worldwide health crisis for which new antibiotics are needed. One strategy for antibiotic discovery is identifying unique antibiotic biosynthetic gene clusters that may produce novel compounds. The aim of this study was to demonstrate how an integrated approach that combines genome mining, comparative genomics, and functional genetics can be used to successfully identify novel biosynthetic gene clusters that produce antimicrobial natural products. Secondary metabolite clusters of an antibiotic producer are first predicted using genome mining tools, generating a list of candidates. Comparative genomic approaches are then used to identify gene suites present in the antibiotic producer that are absent in closely related non-producers. Gene sets that are common to the two lists represent leading candidates, which can then be confirmed using functional genetics approaches. To validate this strategy, we identified the genes responsible for antibiotic production in Pantoea agglomerans B025670, a strain identified in a large-scale bioactivity survey. The genome of B025670 was first mined with antiSMASH, which identified 24 candidate regions. We then used the comparative genomics platform, EDGAR, to identify genes unique to B025670 that were not present in closely related strains with contrasting antibiotic production profiles. The candidate lists generated by antiSMASH and EDGAR were compared with standalone BLAST. Among the common regions was a 14 kb cluster consisting of 14 genes with predicted enzymatic, transport, and unknown functions. Site-directed mutagenesis of the gene cluster resulted in a reduction in antimicrobial activity, suggesting involvement in antibiotic production. An integrated approach that combines genome mining, comparative genomics, and functional genetics yields a powerful, yet simple strategy for identifying potentially novel antibiotics.

Pinacho D. Personalized Medicine for Antibiotics: The Role of Nanobiosensors in Therapeutic Drug Monitoring

Due to the high bacterial resistance to antibiotics (AB), it has become necessary to adjust the dose aimed at personalized medicine by means of therapeutic drug monitoring (TDM). TDM is a fundamental tool for measuring the concentration of drugs that have a limited or highly toxic dose in different body fluids, such as blood, plasma, serum, and urine, among others. Using different techniques that allow for the pharmacokinetic (PK) and pharmacodynamic (PD) analysis of the drug, TDM can reduce the risks inherent in treatment. Among these techniques, nanotechnology focused on biosensors, which are relevant due to their versatility, sensitivity, specificity, and low cost. They provide results in real time, using an element for biological recognition coupled to a signal transducer. This review describes recent advances in the quantification of AB using biosensors with a focus on TDM as a fundamental aspect of personalized medicine.

Recent developments in self-resistance gene directed natural product discovery

Covering: 2000 to 2019Natural products (NPs) are important sources of human therapeutic agents and pesticides. To prevent self-harm from bioactive NPs, some microbial producers employ self-resistance genes to protect themselves. One effective strategy is to employ a self-resistance enzyme (SRE), which is a slightly mutated version of the original metabolic enzyme, and is resistant to the toxic NP but is still functional. The presence of a SRE in a gene cluster can serve as a predictive window to the biological activity of the NPs synthesized by the pathway. In this highlight, we summarize representative examples of NP biosynthetic pathways that utilize self-resistance genes for protection. Recent discoveries based on self-resistance gene identification have helped in bridging the gap between activity-guided and genome-driven approaches for NP discovery and functional assignment.

Recent advances in inducing endophytic fungal specialized metabolites using small molecule elicitors including epigenetic modifiers

Today when the quest of new lead molecules to supply the development pipeline is driving the course of drug discovery, endophytic fungi with their outstanding biosynthetic potential seem to be highly promising avenues for natural product scientists. However, challenges such as the production of inadequate quantities of compounds, the attenuation or loss of ability of endophytes to produce the compound of interest when grown in culture and the inability of fungal endophytes to express their full biosynthetic potential in laboratory conditions have been the major constraints. These have led to the application of small chemical elicitors that induce epigenetic changes in fungi to activate their silent gene clusters optimizing the amount of metabolites of interest or inducing the synthesis of hitherto undescribed compounds. In this respect small molecular weight compounds which are known to function as inhibitors of histone deacetylase (HDAC), DNA methyltransferase (DNMT) and proteasome have proven their efficacy in enhancing or inducing the production of specialized metabolites by fungi. Moreover, organic solvents, metals and plants extracts are also acknowledged for their ability to cause shifts in fungal metabolism. We highlight the successful studies from the past two decades reporting the ability of structurally diverse small molecular weight compounds to elicit the production of previously undescribed metabolites from endophytic fungi grown in culture. This mini review argues in favor of chemical elicitation as an effective strategy to optimize the production of fungal metabolites and invigorate the pipeline of drug discovery with new chemical entities.

Novel sequencing technologies to support industrial biotechnology

Industrial biotechnology develops and applies microorganisms for the production of bioproducts and enzymes with applications ranging from food and feed ingredients and processing to bio-based chemicals, biofuels and pharmaceutical products. Next generation DNA sequencing technologies play an increasingly important role in improving and accelerating microbial strain development for existing and novel bio-products via screening, gene and pathway discovery, metabolic engineering and additional optimization and understanding of large-scale manufacturing. In this mini-review, we describe novel DNA sequencing and analysis technologies with a focus on applications to industrial strain development, enzyme discovery and microbial community analysis.

Antarctic Streptomyces fildesensis So13.3 strain as a promising source for antimicrobials discovery

Antarctic have been suggested as an attractive source for antibiotics discovery and members of Streptomyces genus have historically been studied as natural producers of antimicrobial metabolites. Nonetheless, our knowledge on antibiotic-producing Streptomyces from Antarctic is very limited. In this study, the antimicrobial activity of organic extracts from Antarctic Streptomyces strains was evaluated by disk diffusion assays and minimum inhibitory concentration. The strain Streptomyces sp. So13.3 showed the greatest antibiotic activity (MIC = 15.6 μg/mL) against Gram-positive bacteria and growth reduction of Gram‒negative pathogens. The bioactive fraction in the crude extract was revealed by TLC‒bioautography at R_f = 0.78 with molecular weight between 148 and 624 m/z detected by LC-ESI-MS/MS. The strain So13.3 was taxonomically affiliated as Streptomyces fildesensis. Whole genome sequencing and analysis suggested a 9.47 Mb genome size with 42 predicted biosynthetic gene clusters (BGCs) and 56 putative clusters representing a 22% of total genome content. Interestingly, a large number of them (11 of 42 BGCs and 40 of 56 putative BGCs), did not show similarities with other known BGCs. Our results highlight the potential of the Antarctic Streptomyces strains as a promising source of novel antimicrobials, particularly the strain Streptomyces fildesensis So13.3, which first draft genome is reported in this work.

Impact of ∼omics in the detection and validation of potential anti-infective drugs

New anti-infective drugs are an unmet necessity of modern medicine. The use of ∼omics technologies has exponentially increased the knowledge on active anti-infective structures, where to search for them and their mechanisms of action. Research involving extreme and unique environments (such as endophytes) revealed their potential for many yet unknown active molecules. This work intends to review a recent research involving discovery of secondary metabolites with an established anti-infective action which was mediated by one of the ∼omics sciences: genomics, proteomics, transcriptomics, metabolomics, glycomics or their combinations, as well as the software at the base of these discoveries.

CRISPR/Cas9-Based Editing of Streptomyces for Discovery, Characterization, and Production of Natural Products

Microbial natural products (NPs) especially of the Streptomyces genus have been regarded as an unparalleled resource for pharmaceutical drugs discovery. Moreover, recent progress in sequencing technologies and computational resources further reinforces to identify numerous NP biosynthetic gene clusters (BGCs) from the genomes of Streptomyces. However, the majority of these BGCs are silent or poorly expressed in native strains and remain to be activated and investigated, which relies heavily on efficient genome editing approaches. Accordingly, numerous strategies are developed, especially, the most recently developed, namely, clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated (Cas) system reveals remarkable higher accuracy and efficiency for genome editing in various model organisms including the Streptomyces. In this mini review, we highlight the application of CRISPR/Cas9-based approaches in Streptomyces, focus on the editing of BGCs either in vivo or in vitro, as well as target cloning of large-sized BGCs and heterologous expression in a genetically manipulatable host, for discovery, characterization, reengineering, and production of potential pharmaceutical drugs.

Extending the "One Strain Many Compounds" (OSMAC) Principle to Marine Microorganisms

Genomic data often highlights an inconsistency between the number of gene clusters identified using bioinformatic approaches as potentially producing secondary metabolites and the actual number of chemically characterized secondary metabolites produced by any given microorganism. Such gene clusters are generally considered as “silent”, meaning that they are not expressed under laboratory conditions. Triggering expression of these “silent” clusters could result in unlocking the chemical diversity they control, allowing the discovery of novel molecules of both medical and biotechnological interest. Therefore, both genetic and cultivation-based techniques have been developed aimed at stimulating expression of these “silent” genes. The principles behind the cultivation based approaches have been conceptualized in the “one strain many compounds” (OSMAC) framework, which underlines how a single strain can produce different molecules when grown under different environmental conditions. Parameters such as, nutrient content, temperature, and rate of aeration can be easily changed, altering the global physiology of a microbial strain and in turn significantly affecting its secondary metabolism. As a direct extension of such approaches, co-cultivation strategies and the addition of chemical elicitors have also been used as cues to activate “silent” clusters. In this review, we aim to provide a focused and comprehensive overview of these strategies as they pertain to marine microbes. Moreover, we underline how changes in some parameters which have provided important results in terrestrial microbes, but which have rarely been considered in marine microorganisms, may represent additional strategies to awaken “silent” gene clusters in marine microbes. Unfortunately, the empirical nature of the OSMAC approach forces scientists to perform extensive laboratory experiments. Nevertheless, we believe that some computation and experimental based techniques which are used in other disciplines, and which we discuss; could be effectively employed to help streamline the OSMAC based approaches. We believe that natural products discovery in marine microorganisms would be greatly aided through the integration of basic microbiological approaches, computational methods, and technological innovations, thereby helping unearth much of the as yet untapped potential of these microorganisms.

Omics and multi-omics approaches to study the biosynthesis of secondary metabolites in microorganisms

Natural products produced by microorganisms represent the main source of bioactive molecules. The development of high-throughput (omics) techniques have importantly contributed to the renaissance of new antibiotic discovery increasing our understanding of complex mechanisms controlling the expression of biosynthetic gene clusters (BGCs) encoding secondary metabolites. In this context this review highlights recent progress in the use and integration of 'omics' approaches with focuses on genomics, transcriptomics, proteomics metabolomics meta-omics and combined omics as powerful strategy to discover new antibiotics.