Microbial-ecology guided discovery of antibiofilm

compounds from marine sponges

Jason C. KwanDivision of Pharmaceutical Sciences,

University of Wisconsin–Madison

2nd International Symposium on Alternatives to Antibiotics, Paris, France

13th December 2016

1

The fruits of evolution• Natural product structures, optimized by millions of years of

evolution

• Most antibiotics in use today

• Many anticancer treatments

• Other indications - related to ecological role?

• Antibiotic resistance

• Use of antibiotics by humans in medicine and agriculture selects for resistant strains

2

Interspecies interactions

3Nature 2015, 521, 516–519

Simulations suggest that three-way interactions where antibiotic-degrading species attenuate the inhibitory interactions between two other species are stable and well-mixed.

Studying interspecies interactionsCan we observe the behavior of environmental bacteria to determine how and why they use natural products?

4

Problem

Uncultured environmental microbes

Solution

Shotgun metagenomics

Complex communities, complex metagenomes

Better bioinformatics methods to assemble and

bin genomes

How to observe individual species behavior?

Integrated metagenomics and metatranscriptomics

Assembling and binningMixed microbial

community

e.g. marine sponge

Total DNA

~200,000,000 paired short reads

(~100 bp in length)40 Gbp total

Raw sequence data

541,656 contigsN50 6,211 bp

Largest 1.5 Mbp734.2 Mbp total

Slow, computationally expensive (time,

memory)

Metagenomic assembly

Assembly

Binning

Time consuming,Labor intensive

Individual microbial genomes (draft quality)

5

Repetitive biosynthetic pathways

6

Bryostatin pathway assembly from Illumina paired-end data

AEM 2016, 82, 6573–6583

Paired-end Illumina sequencing

~300 bp fragments

~100 bp sequenced at each end

“Candidatus Endobugula sertula” genome

7AEM 2016, 82, 6573–6583

Additional bryostatin biosynthetic genes were found in the “Ca. E. sertula” genome. Often pathways in symbionts are not clustered.

Marine sponges

• Sponges are sessile filter feeders

• Up to 40% of sponge tissue volume is occupied by microbial cells

• Sponge microbiomes are complex

• Symbionts are known to make bioactive natural products

Microbiol. Mol. Biol. Rev. 2007, 71, 295–347

Calyculin ApM cytotoxin

Nat. Chem. Biol. 2014, 10, 648–6558

Hypothesis

• Sponges actively pump water through internal channels with a large surface area

• Internal surfaces are vulnerable to biofouling/biofilm formation by non-symbiotic microbes

• Hypothesis: the symbiotic community produces compounds that inhibit biofouling/biofilm formation to protect the sponge and maintain its microbiota

9

Metagenomics binning aids de novo metatranscriptomics

1. Obtain microbial genomes using shotgun metagenomics

2. RNAseq from same sample, align to assembled genomes

Allows expression to be normalized for species abundance• In a complex community, who is doing what?

10

Routes to compound supply1. Production through heterologous expression.

• Synthesis of codon optimized pathway, expression in E. coli.

• Proof of concept - synthesis and expression of 83 kbp mandelalides pathway (in progress).

2. Targeted cultivation of the producing microbe.

• Use genome of producing bacterium to design selectivemedium that supports the growth of the target bacterium.

• Design feedback through dPCR/qPCR and FISH.

11

Evaluating resistance potential

12

Rose et al. Pharm. Res. 2015, 32, 61–73.

• Mechanism of action will be investigated using chemical genetics in E. coli, particularly which mutants are able to form biofilms despite presence of hit compounds.

• Bacteria will be exposed to hit compounds long-term in a drip-flow bioreactor, to determine the dynamics of resistance development.

Animal biofilm models

13

Andes et al. Infect. Immun. 2014, 82, 4931–4940.

Promising compounds will be tested in animal infection models to assess potential clinical efficacy against biofilms.

• Rat urinary catheter model.

• Rabbit intravenous catheter model.

Acknowledgments

Lab members: Skylar Carlson, Juan Lopera, Ian Miller, Christine Mlot, Kelly Montgomery, Ted Weyna

Collaborators: Kerry McPhail (Oregon State University), Grace Lim-Fong (Randolph-Macon College), Steve Fong (Virginia Commonwealth University), Melany Puglisi (Chicago State University), Warren Rose (UW-Madison)

Funding: UW-Madison School of Pharmacy, UW-Madison Graduate School, UW-Madison Institute for Clinical and Translational Research, NIH NIAID R21AI121704, Jeffress Trust, American Society of Pharmacognosy

Computation: UW-Madison Center for High-Throughput Computing (CHTC)

14

Questions?

15