HEx Technology Cracks Cryptic Codes Identifying Potential Therapeutic Compounds
News Apr 11, 2018 | by Laura Elizabeth Mason, Science Writer, Technology Networks
Researchers have developed a technology, known as ‘HEx’, that can detect natural products from fungal species – products which are usually extremely difficult to identify. This approach could be used to find natural compounds with therapeutic potential, paving the way to novel antimicrobial drugs.1 The study was published in Science Advances.
Penicillin is just one well known example of a fungi derived drug (specifically from the genus Penicillium). Since its discovery in 1928 by Scottish researcher Alexander Fleming, several other drugs originating from fungal species have been approved for medicinal use, including cyclosporin and lovastatin. A huge percentage of drugs approved, between 1980 and 2012, by the US Food and Drug Administration (FDA) are either natural fungal products or fungal derivatives – >70% of antibiotics, ~49% of all anti-cancer drugs and >30% of all novel drugs.2
Thanks to improvements in genome sequencing, we now know that there a vast number of fungal species in existence – more than 5 million! Although each of these species can encode as many as 80 natural product biosynthetic pathways, it is difficult to culture many of these fungi under current laboratory conditions.
“The rapid growth in the number of available fungal genomes over the past decade has made it apparent that most fungal species, when grown under standard laboratory conditions, are realizing less than 20% of their biosynthetic potential.” Explained Colin J. B. Harvey of Stanford University School of Medicine and first author of the study.
Even in the minority that can be cultured, further challenges are often faced as most of the biosynthetic gene clusters (BGCs), which encode the natural products, are transcriptionally silent or expressed at extremely low levels.
Scientists have now developed the HEx (Heterologous EXpression) synthetic biology platform, designed to enhance natural product discovery. HEx is comprised of three key components; bioinformatics tools, genetics tools and synthetic biology tools, together these features allow it to identify and prioritize BGCs from within genomic data. Once detected, these ‘cryptic’ BGCs are genetically manipulated and can be expressed in a heterologous host – Saccharomyces cerevisiae.
“…it has become abundantly clear that we are able to obtain the DNA sequence of genetic machinery capable of producing far more natural products than we are able to isolate and characterize by traditional means. By using this DNA sequence as a starting point, stripping away all native regulation and moving this machinery in model hosts that we can grow quickly and easily, heterologous expression platforms allow access to this trove of potentially novel and otherwise inaccessible natural products.” Said Harvey.
Untargeted metabolomics can be used to detect strains expressing BGCs and full compound structures can then be solved using liquid chromatography-mass spectrometry (LM-MS) and nuclear magnetic resonance (NMR) for compounds identified as being ‘novel’.
The team applied HEx to 41 fungal BGCs across a wide array of species – 54% of which resulted in natural products not natively present in yeast. The authors emphasize that HEx could aid the production of natural therapeutic compounds, through its ability to identify unstudied BGCs, previously hidden within the fungal genome.
Harvey commented on ongoing and future studies: “Work is currently ongoing at the Stanford Genome Technology Center to look at several clusters, including several that worked and several that didn't, in much greater detail. While the scope of the current study was to verify our systems on the genetic level and then look for their ability to produce a natural product, our colleagues are currently looking at the abundance and identity of each mRNA transcribed from these genes and the proteins translated from each mRNA.”
“This 'multi-omics' approach to examining yeast strains expressing clusters, both those that were productive and those that weren't, will provide a great deal of insight into how to both rescue function from non-functional systems and improve function in those that are only modestly productive.”
Colin J. B. Harvey was speaking to Laura Elizabeth Mason, Science Writer for Technology Networks
1. C. J. B. Harvey et al., HEx: A heterologous expression platform for the discovery of fungal natural products. Sci Adv. 2018;4:eaar545. (11 April 2018)
2. D. J. Newman, G. M. Cragg, Natural products as sources of new drugs from 1981 to 2014. J. Nat. Prod. 79, 629–661 (2016).
MIT researchers have developed a cryptographic system that could help neural networks identify promising drug candidates in massive pharmacological datasets, while keeping the data private. Secure computation done at such a massive scale could enable broad pooling of sensitive pharmacological data for predictive drug discovery.
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