Drug-resistant infections kill more than 65,000 people in the United States per year and the identification of new antibiotics capable of combating antibiotic resistant bacterial pathogens is desperately needed. Bacterial natural products have a long proven history of being good lead structures for the development of clinically useful antibiotics but, unfortunately, the discovery of novel natural products from cultured bacteria has dramatically slowed in recent years. During the same time, the advent of cost effective high-throughput sequencing and an increasingly sophisticated understanding of bacterial secondary metabolite biosynthesis have led to two important revelations with respect to the search for new natural products: first, that the biosynthetic potential of most cultured bacteria, as judged by the number of biosynthetic gene clusters observed in sequenced genomes, is far greater than previously estimated; second, that the number of bacterial species in most environments is at least one hundred times greater than the number of species that is readily cultured. These observations indicate that conventional natural product screening approaches, which rely on laboratory culturing of random bacterial strains from the environment, have not come close to realizing the full biosynthetic potential of the earth's microbiome and that a fundamental change in the approach to bacterial natural products discovery is therefore needed. Consequently, my laboratory has sought to develop methods to allow natural product discovery from environmental samples, without the need for strain isolation and cultivation. The approaches I propose to use to harvest small molecules from metagenomic libraries are divided into two general categories: 1) Sequence-based metagenomics, which relies on DNA sequence similarity to identify clones containing a specific gene of interest and 2) Function-based metagenomics, which relies on the random, unbiased screening of individual eDNA clones in phenotypic assays to identify clones producing bioactive metabolites. Using sequence-based approaches we can interrogate metagenomes for novel gene clusters (i.e., new antibiotics), as well as clusters that encode congeners of clinically relevant natural product (i.e., improve the utility of clinically important classes of antibiotics). Our functional screening studies will focus on developing methods for creating metagenomic libraries that are enriched in natural product gene clusters to facilitate more efficient screening for novel antibiotics. For this proposal, we will use our existing pipelines and improved protocols, as they come on-line, to identify novel natural products with improved activities against antibiotic resistant bacterial pathogens. We have undoubtedly just begun to scratch the surface of what lies hidden in the genomes of environmental bacteria. The work here will greatly increase access to the natural products that remain hidden in the environment and ultimately yield a series of novel natural antibiotics with the potential to better treat antibiotic resistant bacterial pathogens. !

Public Health Relevance

Bacteria that have not yet been cultured outnumber their cultured counterparts by at least two to three orders of magnitude and represent a rewarding source of as yet unstudied, biologically active small molecules. The work described in this proposal is designed to develop and apply culture-independent natural product discovery methods to the discovery of antibiotics that are better able to treat antibiotic resistant bacterial pathogens.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Unknown (R35)
Project #
1R35GM122559-01
Application #
9276995
Study Section
Special Emphasis Panel (ZGM1)
Program Officer
Fabian, Miles
Project Start
2017-05-01
Project End
2022-04-30
Budget Start
2017-05-01
Budget End
2018-04-30
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Rockefeller University
Department
Genetics
Type
Graduate Schools
DUNS #
071037113
City
New York
State
NY
Country
United States
Zip Code
10065