A Systematic Computational Analysis of Biosynthetic Gene Cluster Evolution: Lessons for Engineering Biosynthesis

Bacterial secondary metabolites are widely used as antibiotics, anticancer drugs, insecticides and food additives. Attempts to engineer their biosynthetic gene clusters (BGCs) to produce unnatural metabolites with improved properties are often frustrated by the unpredictability and complexity of the enzymes that synthesize these molecules, suggesting that genetic changes within BGCs are limited by specific constraints. Here, by performing a systematic computational analysis of BGC evolution, we derive evidence for three findings that shed light on the ways in which, despite these constraints, nature successfully invents new molecules: 1) BGCs for complex molecules often evolve through the successive merger of smaller sub-clusters, which function as independent evolutionary entities. 2) An important subset of polyketide synthases and nonribosomal peptide synthetases evolve by concerted evolution, which generates sets of sequence-homogenized domains that may hold promise for engineering efforts since they exhibit a high degree of functional interoperability, 3) Individual BGC families evolve in distinct ways, suggesting that design strategies should take into account family-specific functional constraints. These findings suggest novel strategies for using synthetic biology to rationally engineer biosynthetic pathways.     

后基因组时代微生物天然产物高效发掘体系设计与展望

微生物天然产物具有丰富的化学结构多样性和诱人的生物活性,持续启迪着创新医药和农药的发现。近年来,随着高通量测序技术的快速发展,巨大的微生物基因组数据揭示了多样生物合成和新颖天然产物的潜能远高于以前的认知。然而,如何高效地激活隐性的生物合成基因簇 (BGCs) 并识别相应的化合物,以及避免已知代谢产物的重复发现等挑战依然严峻。本文描述了面对这些问题时基因组学、生物信息学、机器学习、代谢组学、基因编辑和合成生物学等新技术在发现药用先导化合物过程中提供的机遇;总结并论述了在潜力菌株优选、BGCs的生物信息学预测、沉默 BGCs的高效激活以及目标产物的识别和跟踪方面的新见解;提出了基于潜力菌株选择和多组学挖掘技术从微生物天然产物中高效发现先导结构的系统线路 (SPLSD),并讨论了未来天然产物药用先导发现的机遇和挑战。     

Use of the Local False Discovery Rate for Identification of Metabolic Biomarkers in Rat Urine Following Genkwa Flos-Induced Hepatotoxicity

Metabolomics is concerned with characterizing the large number of metabolites present in a biological system using nuclear magnetic resonance (NMR) and HPLC/MS (high-performance liquid chromatography with mass spectrometry). Multivariate analysis is one of the most important tools for metabolic biomarker identification in metabolomic studies. However, analyzing the large-scale data sets acquired during metabolic fingerprinting is a major challenge. As a posterior probability that the features of interest are not affected, the local false discovery rate (LFDR) is a good interpretable measure. However, it is rarely used to when interrogating metabolic data to identify biomarkers. In this study, we employed the LFDR method to analyze HPLC/MS data acquired from a metabolomic study of metabolic changes in rat urine during hepatotoxicity induced by Genkwa flos (GF) treatment. The LFDR approach was successfully used to identify important rat urine metabolites altered by GF-stimulated hepatotoxicity. Compared with principle component analysis (PCA), LFDR is an interpretable measure and discovers more important metabolites in an HPLC/MS-based metabolomic study.     

Associative clustering for exploring dependencies between functional genomics data sets

High-throughput genomic measurements, interpreted as cooccurring data samples from multiple sources, open up a fresh problem for machine learning: What is in common in the different data sets, that is, what kind of statistical dependencies are there between the paired samples from the different sets? We introduce a clustering algorithm for exploring the dependencies. Samples within each data set are grouped such that the dependencies between groups of different sets capture as much of pairwise dependencies between the samples as possible. We formalize this problem in a novel probabilistic way, as optimization of a Bayes factor. The method is applied to reveal commonalities and exceptions in gene expression between organisms and to suggest regulatory interactions in the form of dependencies between gene expression profiles and regulator binding patterns.     

Phytoplankton trigger the production of cryptic metabolites in the marine actinobacterium Salinispora tropica

Filamentous members of the phylum Actinobacteria are a remarkable source of natural products with pharmaceutical potential. The discovery of novel molecules from these organisms is, however, hindered because most of the biosynthetic gene clusters (BGCs) encoding these secondary metabolites are cryptic or silent and are referred to as orphan BGCs. While co-culture has proven to be a promising approach to unlock the biosynthetic potential of many microorganisms by activating the expression of these orphan BGCs, it still remains an underexplored technique. The marine actinobacterium Salinispora tropica, for instance, produces valuable compounds such as the anti-cancer molecule salinosporamide but half of its putative BGCs are still orphan. Although previous studies have used marine heterotrophs to induce orphan BGCs in Salinispora, its co-culture with marine phototrophs has yet to be investigated. Following the observation of an antimicrobial activity against a range of phytoplankton by S. tropica, we here report that the photosynthate released by photosynthetic primary producers influences its biosynthetic capacities with production of cryptic molecules and the activation of orphan BGCs. Our work, using an approach combining metabolomics and proteomics, pioneers the use of phototrophs as a promising strategy to accelerate the discovery of novel natural products from marine actinobacteria.     

Output ordering and prioritisation system (OOPS): ranking biosynthetic gene clusters to enhance bioactive metabolite discovery

The rapid increase of publicly available microbial genome sequences has highlighted the presence of hundreds of thousands of biosynthetic gene clusters (BGCs) encoding valuable secondary metabolites. The experimental characterization of new BGCs is extremely laborious and struggles to keep pace with the in silico identification of potential BGCs. Therefore, the prioritisation of promising candidates among computationally predicted BGCs represents a pressing need. Here, we propose an output ordering and prioritisation system (OOPS) which helps sorting identified BGCs by a wide variety of custom-weighted biological and biochemical criteria in a flexible and user-friendly interface. OOPS facilitates a judicious prioritisation of BGCs using G+C content, coding sequence length, gene number, cluster self-similarity and codon bias parameters, as well as enabling the user to rank BGCs based upon BGC type, novelty, and taxonomic distribution. Effective prioritisation of BGCs will help to reduce experimental attrition rates and improve the breadth of bioactive metabolites characterized.     

Strategy for efficient cloning of biosynthetic gene clusters from fungi

Filamentous fungi are excellent sources for the production of a group of bioactive small molecules which are often called secondary metabolites(SMs). The advanced genome sequencing technology combined with bioinformatics analysis reveals a large number of unexplored biosynthetic gene clusters(BGCs) in the fungal genomes. To unlock this fungal SM treasure, many approaches including heterologous expression are being developed and efficient cloning of the BGCs is a crucial step to do this.Here, we present an efficient strategy for the direct cloning of fungal BGCs. This strategy consisted of Splicing by Overlapping Extension(SOE)-PCR and yeast assembly in vivo. By testing 14 BGCs DNA fragments ranging from 7 kb to 52 kb, the average positive rate was over 80%. The maximal insertion size for fungal BGC assembly was 52 kb. Those constructs could be used conveniently for the heterologous expression leading to the discovery of novel natural products. Thus, our results provide an efficient and quick method for the low cost direct cloning of fungal BGCs.     

Metabolic Profiles are Principally Different between Cancers of the Liver,Pancreas and Breast

Molecular profiling of primary tumors may facilitate the classification of patients with cancer into more homogenous biological groups to aid clinical management. Metabolomic profiling has been shown to be a powerful tool in characterizing the biological mechanisms underlying a disease but has not been evaluated for its ability to classify cancers by their tissue of origin. Thus, we assessed metabolomic profiling as a novel tool for multiclass cancer characterization. Global metabolic profiling was employed to identify metabolites in paired tumor and non-tumor liver (n=60), breast (n=130) and pancreatic (n=76) tissue specimens. Unsupervised principal component analysis showed that metabolites are principally unique to each tissue and cancer type. Such a difference can also be observed even among early stage cancers, suggesting a significant and unique alteration of global metabolic pathways associated with each cancer type. Our global high-throughput metabolomic profiling study shows that specific biochemical alterations distinguish liver, pancreatic and breast cancer and could be applied as cancer classification tools to differentiate tumors based on tissue of origin.     

A genomics and multi-platform metabolomics approach to identify new traits of rice quality in traditional and improved varieties

Using a novel approach combining four complementary metabolomic and mineral platforms with genome-wide genotyping at 1536 single nucleotide polymorphism (SNP) loci, we have investigated the extent of biochemical and genetic diversity in three commercially-relevant waxy rice cultivars important to food production in the Lao People??s Democratic Republic (PDR). Following cultivation with different nitrogen fertiliser regimes, multiple metabolomic data sets, including minerals, were produced and analysed using multivariate statistical methods to reveal the degree of similarity between the genotypes and to identify discriminatory compounds supported by multiple technology platforms. Results revealed little effect of nitrogen supply on metabolites related to quality, despite known yield differences. All platforms revealed unique metabolic signatures for each variety and many discriminatory compounds could be identified as being relevant to consumers in terms of nutritional value and taste or flavour. For each platform, metabolomic diversity was highly associated with genetic distance between the varieties. This study demonstrates that multiple metabolomic platforms have potential as phenotyping tools to assist breeders in their quest to combine key yield and quality characteristics. This better enables rice improvement programs to meet different consumer and farmer needs, and to address food security in rice-consuming countries.     

Activating natural product synthesis using CRISPR interference and activation systems in Streptomyces

The rise of antibiotic-resistant bacteria represents a major threat to global health, creating an urgent need to discover new antibiotics. Natural products derived from the genus Streptomyces represent a rich and diverse repertoire of chemical molecules from which new antibiotics are likely to be found. However, a major challenge is that the biosynthetic gene clusters (BGCs) responsible for natural product synthesis are often poorly expressed under laboratory culturing conditions, thus preventing the isolation and screening of novel chemicals. To address this, we describe a novel approach to activate silent BGCs through rewiring endogenous regulation using synthetic gene regulators based upon CRISPR-Cas. First, we refine CRISPR interference (CRISPRi) and create CRISPR activation (CRISPRa) systems that allow for highly programmable and effective gene repression and activation in Streptomyces. We then harness these tools to activate a silent BGC by perturbing its endogenous regulatory network. Together, this work advances the synthetic regulatory toolbox for Streptomyces and facilitates the programmable activation of silent BGCs for novel chemical discovery.     

Metabolomic profiling from formalin-fixed, paraffin-embedded tumor tissue using targeted LC/MS/MS: application in sarcoma

The relatively new field of onco-metabolomics attempts to identify relationships between various cancer phenotypes and global metabolite content. Previous metabolomics studies utilized either nuclear magnetic resonance spectroscopy or gas chromatography/mass spectrometry, and analyzed metabolites present in urine and serum. However, direct metabolomic assessment of tumor tissues is important for determining altered metabolism in cancers. In this respect, the ability to obtain reliable data from archival specimens is desirable and has not been reported to date. In this feasibility study, we demonstrate the analysis of polar metabolites extracted directly from ten formalin-fixed, paraffin-embedded (FFPE) specimens, including five soft tissue sarcomas and five paired normal samples. Using targeted liquid chromatography-tandem mass spectrometry (LC/MS/MS) via selected reaction monitoring (SRM), we detect an average of 106 metabolites across the samples with excellent reproducibility and correlation between different sections of the same specimen. Unsupervised hierarchical clustering and principal components analysis reliably recovers a priori known tumor and normal tissue phenotypes, and supervised analysis identifies candidate metabolic markers supported by the literature. In addition, we find that diverse biochemical processes are well-represented in the list of detected metabolites. Our study supports the notion that reliable and broadly informative metabolomic data may be acquired from FFPE soft tissue sarcoma specimens, a finding that is likely to be extended to other malignancies.     

Machine learning for epigenetics and future medical applications

Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems with ML to develop a more efficient feature selection process and address the imbalance problem in all genomic data sets. The power of this novel ML approach and our ability to predict epigenetic phenomena and associated disease is suggested. The current approach requires extensive computation of features over the genome. A promising new approach is to introduce Deep Learning (DL) for the generation and simultaneous computation of novel genomic features tuned to the classification task. This approach can be used with any genomic or biological data set applied to medicine. The application of molecular epigenetic data in advanced machine learning analysis to medicine is the focus of this review.     

Establishment of recombineering genome editing system in Paraburkholderia megapolitana empowers activation of silent biosynthetic gene clusters

The Burkholderiales are an emerging source of bioactive natural products. Their genomes contain a large number of cryptic biosynthetic gene clusters (BGCs), indicating great potential for novel structures. However, the lack of genetic tools for the most of Burkholderiales strains restricts the mining of these cryptic BGCs. We previously discovered novel phage recombinases Redαβ7029 from Burkholderiales strain DSM 7029 that could help in efficiently editing several Burkholderiales genomes and established the recombineering genome editing system in Burkholderialse species. Herein, we report the application of this phage recombinase system in another species Paraburkholderia megapolitana DSM 23488, resulting in activation of two silent non-ribosomal peptide synthetase/polyketide synthase BGCs. A novel class of lipopeptide, haereomegapolitanin, was identified through spectroscopic characterization. Haereomegapolitanin A represents an unusual threonine-tagged lipopeptide which is longer than the predicted NRPS assembly line. This recombineering-mediated genome editing system shows great potential for genetic manipulation of more Burkholderiales species to activate silent BGCs for bioactive metabolites discovery.