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Article

Assessing the Effectiveness of Functional Genetic Screens for the Identification of Bioactive Metabolites

by
Anahit Penesyan
1,2,†,
Francesco Ballestriero
1,†,
Malak Daim
1,
Staffan Kjelleberg
1,3,
Torsten Thomas
1 and
Suhelen Egan
1,*
1
School of Biotechnology and Biomolecular Sciences and Centre for Marine Bio-Innovation, University of New South Wales, Sydney 2052, New South Wales, Australia
2
Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney 2109, New South Wales, Australia
3
The Singapore Center on Life Sciences Engineering, Nanyang Technological University, Singapore, Singapore
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Mar. Drugs 2013, 11(1), 40-49; https://doi.org/10.3390/md11010040
Submission received: 18 October 2012 / Revised: 13 November 2012 / Accepted: 12 December 2012 / Published: 27 December 2012
(This article belongs to the Special Issue Marine Antibiotics)
Browse Figure
Versions Notes

Abstract

:
A common limitation for the identification of novel activities from functional (meta) genomic screens is the low number of active clones detected relative to the number of clones screened. Here we demonstrate that constructing libraries with strains known to produce bioactives can greatly enhance the screening efficiency, by increasing the “hit-rate” and unmasking multiple activities from the same bacterial source.

1. Introduction

Functional metagenomics, which includes the cloning of total DNA obtained from an environment into the host bacterium and screening the recombinant clones for a desired activity, is currently a widely used tool for the discovery of novel enzyme and bioactive metabolites [1]. Some of the successes of these functional screens are illustrated by the discovery of antibiotics, such as terragine A [2], bioactive N-acyl-tyrosine derivatives [3] as well as indirubin [4].
Functional screens have also assisted in the understanding of the genomic bases of biosynthetic pathways underlying the production of bioactive compounds in single organisms. As an example, Burke et al. (2007) [5] screened an Escherichia coli fosmid library constructed with genomic DNA from the marine bacterium P. tunicata, which is known for its ability to produce various bioactive compounds [6]. Clones producing the antifungal compound tambjamine were identified and a biosynthetic pathway was proposed based on the expressed genes required for tambjamine production [5].
Success of such screens is obviously dependent on the ability of the host organisms to express and produce the desired activity. This will be limited by such factors as transcription initiation, codon usage and protein folding, which are well-studied issues for heterologous protein expression in E. coli and other hosts [7,8,9]. In addition, heterologous expression of certain genes can be toxic to the host [10] and this is particularly relevant for screens that search for antibiotic activities. Finally, for metagenomic screens, one also has to consider that the desired activities (such as the production of antibiotics) are not evenly distributed among all members of the community sampled, but might reside in rare organisms [8]. Discovery of such “rare” activities would thus require the screening of a large number of clones. All these factors are likely to conspire to cause the low discovery rates (“hit rates”) typically observed for metagenomic screens [11,12], which is rarely exceeding one positive in 10,000 screened clones (0.01%) [3,13] and often being lower (e.g., 0.00013%) [14]. However, it is not clear which of these aspects mentioned above are the major limiting factors.
In this study, we addressed this issue by investigating the screening efficiency for libraries that are enriched for bioactive-producing genomes. By comparing the hit rate to other metagenomic screens we aim to identify if the expression of bioactives per se or the abundance of genes encoding for such activities is the major limiting factor for the success of functional genetic screens.

2. Results and Discussion

To assess the efficiency of our functional screens we constructed a fosmid library from the DNA of six marine bacterial isolates known to have antibacterial properties [15], expressed the library in E. coli and screened for activity against both bacteria and the nematode Caenorhabditis elegans. Our screens identified both antibacterial and antinematode clones (Table 1). Antibacterial activity was observed in eight clones. The selective grazing assay with C. elegans also resulted in eight positive clones, with five of them also possessing antibacterial activity. Clones with antinematode activity were further characterized in the nematode killing assay [16] which revealed a severe killing phenotype (LT50 < 5 days) for all the eight positives, with clone 20G8 being the most active in shortening the worms’ life span from 19 (non-toxic E. coli clone) to 6 days (Figure 1). The detection of both antibacterial and antinematode activities in five clones suggests that the compounds or enzymes encoded by the fosmid clones could possess a broad range of activity against bacteria and nematodes. Alternatively, two separate compounds or enzymes could be encoded on the same 35 kb fosmid.
Table
Table 1. Bioactive fosmid clones and their original producer strain.
Table 1. Bioactive fosmid clones and their original producer strain.
Library clone numberGenBank no.Fosmid size (bp)Antibacterial activityAntinematode activityParental strain *
3G11JX52394924 296++D250
7F7KC21177034 039−+U95
10D3JX52395136 314++D323
12A1JX52395232 547++D250
14D9JX52395319 858++D323
24H6KC21176915 952−+D323
27G10KC21176833 970−+U95
20G8 JX52395725 000++D250
15E10JX52395430 000++D250
23H6JX52395823 000++D250
19F10JX52395629 696+−U95
16B12JX52395539 325+−D323
9E12JX52395037 944+−D323
* see Table 2 for strain details.
Marinedrugs 11 00040 g001 1024
Figure 1. Killing kinetics of the eight antinematode clones. A randomly chosen clone from the library with no activity was used as a negative control. Bars represent standard deviation of three replicates.
Figure 1. Killing kinetics of the eight antinematode clones. A randomly chosen clone from the library with no activity was used as a negative control. Bars represent standard deviation of three replicates.
Marinedrugs 11 00040 g001
Of the 13 active (i.e., antibacterial and/or antinematode) clones identified, three (15E10, 20G8, 23H6) shared overlapping nucleotide sequences, while the remaining 10 clones were unique in terms of their sequences (see Table 1 for GenBank accession numbers). Thus for each screen (i.e., antibacterial and antinematode) this equates to 11 active clones with unique genomic regions for the 2880 clones screened (~0.4%), which is considerably higher than previous metagenomic screens (see above). For example, a hit-rate of 0.001% was recently achieved for a screen of the metagenome created from the microbial community of U. australis [17], from which some of the isolates used in this study were derived. Thus our results would suggest that a pre-selection of bioactive-producing genomes helps with improving hit-rates and that the low abundance of organisms that encode such activities could be a limitation to the success of metagenomic screens.
In addition, our data suggests that this method is able to detect genes and gene clusters for both known bioactive compounds, as well as detect genes encoding for the production of potentially novel bioactivities. For example, overlapping fosmids 15E10, 20G8, 23H6 in addition to encoding for both antibacterial and antinematode activities resulted in the production of a purple pigment when expressed in E. coli. Genetic analysis of each of these fosmids identified a cluster of five genes (vioA–vioE) previously characterized as the biosynthetic pathway for the purple pigment and known antibiotic violacein produced by other bacteria such as Chromobacterium violaceum and P. tunicata [18,19]. As another example, sequencing of fosmid 19F10, originating from the bacterial isolate U95—the type strain for the newly described genus and species Epibacterium ulvae [20], identified a gene with sequence similarity to a non-ribosomal peptide synthetase (NRPS) gene with homology to the NRPS gene bpsA from Streptomyces lavendulae [21] and indC from Erwinia chrysanthemi [22]. Both BpsA and IndC are annotated as indigoidine synthase, which is responsible in part for the production of the blue pigment indigoidine. Notably, the other genes required for the biosynthesis of indigoidine were absent from the 19F10 fosmid and expression of this fosmid did not result in the production of a blue pigment indicating that the NRPS of 19F10 may be responsible for expression of something other than indigoidine. There is strong evidence in the literature highlighting the role of NRPS in the production of various secondary metabolites with biological activities ranging from antibiotics and toxins to iron scavenging siderophores (as reviewed in [23]). Therefore, this gene is a primary candidate responsible for the production of a potentially novel antibacterial compound produced by a newly characterized bacterium. In addition to a NRPS, the 19F10 fosmid also encodes the genes for various transporters, such as the ATP-binding cassette (ABC) transporters, a major facilitator superfamily (MFS) permease, as well as genes encoding proteins for the type VI secretion system; these may potentially be involved in the secretion of bioactive compound. Moreover many of the genes detected on the active fosmids encoded for hypothetical proteins with little homology to previously characterized sequences, once again highlighting the opportunity to uncover new biologically active metabolites. Future studies will aim to elucidate further details of the chemical or biological nature for the activities found, however the unique gene sequences for the majority of clones identified in this study supports the hypothesis that screening efficiency can be greatly improved by the use of expression libraries that are enriched for bioactive-producing genomes.
For both the antibacterial and antinematode activity the clones were traced back to only three (50%) of the bacterial strains (D250, D323 and U95) used to construct the library. As mentioned above, this might be due to difficulties with the expression of foreign genes, particularly from distantly related organisms, in E. coli. Indeed a recent assessment of functional gene expression from soil metagenomes discovered several bioactive clones, which were only expressed in Streptomyces lividans (phylum Actinobacteria), but not in E. coli (phylum Proteobacteria) [24]. The limited expression of genes from strains distantly related to E. coli is further supported by our data as for the three phyla represented in our library (Actinobacteria, Bacteriodetes and Proteobacteria) (see Table 2), active E. coli clones were only detected for source strains belonging to the Proteobacteria.
Table
Table 2. Bacterial strains used in the construction of the fosmid library in this study.
Table 2. Bacterial strains used in the construction of the fosmid library in this study.
Strain IDGenBank no.Isolation sourceClosest relative Phylum% Identity
U95FJ440958Ulva australisUncultured alpha-proteobacterium, JN874385Proteobacteria98
U140FJ440963Ulva australisMicrococcus luteus, JQ795852Actinobacteria99
U156FJ440965Ulva australisGamma-proteobacterium D261, FJ440978Proteobacteria99
D250FJ440973Delisea pulchraGamma-proteobacterium D259, FJ440977Proteobacteria99
D295FJ440982Delisea pulchraFlavobacteriaceae bacterium SW058, AF493683Bacteroidetes98
D323FJ440988Delisea pulchraPseudovibrio sp. Pv348, 1413, HE818384Proteobacteria100
Our screens also detected multiple antibacterial and/or antinematode activities from the same source organism. For example, the genetic screened revealed five genetically distinct antibacterial fosmids for strain D323, which would suggest that five different antibacterial activities are encoded in the genome of isolate D323. Thus a functional genetic screen could help to “tease apart” multiple activities within a source organism and reveal previously unknown activities, something that is difficult to do with classical approaches, such as knock-out genetics. A functional screening approach is thus useful for the exploration of “metabolically talented” strains [25,26] able to produce a wide range of secondary metabolites and may further assist in the separation and identification of compounds by using the host strain without the expressed fosmid as a reference during chemical analysis.

3. Experimental Section

Six marine bacterial isolates known to have antibacterial activity were used to construct a combined fosmid library and screened for antibacterial and antinematode activities. Specifically, genomic DNA was extracted according to the XS DNA extraction protocol [27] from bacterial strains, which were previously isolated from the surface of the marine algae Ulva australis and Delisea pulchra and which comprised of both phenotypically and phylogenetically distinct groups [15] (Table 2). DNA was pooled in equimolar amounts, randomly sheared, size selected by gel purification (~35 kb) and cloned into the fosmid pCCFOS1 (Epicentre Biotechnologies) according to the manufacturers’ instructions. Fosmid clones were stored and maintained at a single copy number, but induced to high copy number (10–50 per cell) through the addition of L-arabinose (0.02%) to the growth medium during screens.
Clones were screened in an overlay assay using Staphylococcus aureus and Neisseria canis as target strains [15] as well as in a selective grazing assay and subsequent toxicity assay using the nematode C. elegans [16]. The 2880 clones (average insert size ~35 kb) were screened, which covered approximately 100 Mb of genomic DNA. In line with previous studies [5,17,18,24] and assuming an average genome size of 3.5 Mbp [28,29], this number of clones would cover all six genomes on average 4.5 fold. Screens were repeated three times after which thirteen clones were selected which consistently had high levels of either antibacterial or antinematode activities. Fosmids were extracted from these clones, shotgun sequenced (Craig Venter Institute, Rockville, MD, USA) and then annotated (supplementary material). Fosmids were linked back to the original bacterial strain via PCR (supplementary material).

4. Conclusions

Heterologous expression and possible toxic effects on the host remain clear limitations for the identification of bioactivities in genetic screens [9]. However the relatively high hit rate observed in our study indicates that the scarcity of DNA encoding for bioactivities might be a significant limitation for metagenomic screens. Whilst the higher hit rate using a pre-selection of active strains is not necessarily surprising, to our knowledge, this is the first study to experimentally address the abundance of bioactive genes as a limitation to functional metagenomic screens. Studies have shown that metagenomic libraries constructed of DNA pooled from cultured isolates is effective in detecting antibiotic resistance phenotypes [30] and more recently pigment production and hemolytic activity [24]. However, neither of these studies used cultured isolates known to have these respective activities. Our results further highlight the need for a targeted application of functional metagenomics to environments in which, for example, ecological factors select for high abundance of bioactive-producing organisms.

Acknowledgments

The authors would like to thank the Australian Research Council and the Centre for Marine Bio-Innovation for research support. We thank the Caenorhabditis Genetics Center at the University of Minnesota for provision of C. elegans samples and the University International Postgraduate Award for supporting F.B.

Supplementary Material

Analysis of Fosmids

Sequencing reads obtained from ABI3730XL and the Roche Titanium FLX DNA sequencer, were trimmed for vector contamination (i.e., pCC1FOS) and low quality, using the Phred/Phrap/Consed software pipeline [31]. Reads from the shotgun library were assembled with Phrap and the assembly manually checked in Consed. Gaps and low quality regions were closed by targeted PCRs and sequencing. Overlapping regions between the fosmids were identified from the final curated assemblies and from pairwise BLAST searches. Open reading frames (ORFs) were identified with the program MetaGene [32,33]. All predicted ORFs were searched (using an in-house pipeline) [34] against the Swiss-Prot database [35], the Institute of Genome Research Family (TIGRFAM) database [36], the Kyoto Encyclopaedia of Genes and Genomes (KEGG) database [37], and the Cluster of Orthologous Group of proteins (COG) database [38] to obtain a functional annotation.

Identification of Fosmid Parental Strains

PCR amplification was used to identify which parent genome the selected fosmids belonged to. Briefly, specific primer pairs were designed based on each fosmid sequence (Table S1). Genomic DNA of each of the six isolates used in the fosmid library construction (U95, U140, U156, D250, D295, D323) was used as template for amplification using the following conditions. Amplification was performed in 20 μL reaction mixes each containing 50 ng of genomic DNA; 2 μL REDtaq buffer (Sigma-Aldrich, St. Louis, MO, USA), 2.5 mM each dNTP (Roche, Penzberg, Germany), 12.5 pmol of each of the forward and reverse primers (Table S1) (Sigma-Aldrich, St. Louis, MO, USA), 0.5 μL of 10% BSA (w/v, New England Biolabs, Ipswich, MA, USA) and molecular grade water (Eppendorf, Hamburg, Germany). One unit of REDtaq polymerase (Sigma-Aldrich, St. Louis, MO, USA) was added at the Hot Start, after the initial thermal ramp. The PCR conditions were 94 °C for 3 min, then 25 cycles each of 1 min at 94 °C, 1 min at 55 °C, and 2 min at 72 °C. A final extension step of 72 °C for 6 min was performed. Amplified DNA fragments were subjected to agarose gel electrophoresis to check for the presence of amplification products (data not shown).
Table
Table S1. Primer pairs used for fosmid parent strain identification and their expected product lengths.
Table S1. Primer pairs used for fosmid parent strain identification and their expected product lengths.
FosmidPrimer pairsSequence (5′ to 3′)Product length
3G113G11 forwardGGC TAG AGG CGT TGC GTA TTG TGC679 bp
3G11 reverseCTT TAA AGG CGC CGG GCT CCA TCT
7F77F7 forwardAAC CTG CCA GAT ACC AAA CG1728 bp
7F7 reverseGGT CAA CCG GAA CAC AGA GT
9E129E12 forwardTGC TGA AGC GGA AGT GGA GTA TGA388 bp
9E12 reverseCGG CAC GTT GAA GTC GAA GTA GTC
10D310D3 forwardsCTA TGA TCA CGA CCA GCA CAC GAG571 bp
10D3 reverseACC AGG TCC GAG CCA TCT ACA CAA
12A112A1 forwardACA GCG GTG GTC ATT ATT GGA ACG432 bp
12A1 reverseGGC GGT GTG AAA GCG GTG ATA GTC
14D914D9 forwardGGC ACA CGG CTC TTC ATC TTC ACA532 bp
14D9 reverseGCC GCG TTC GTT CCC GTC AC
24H624H6 forwardCGT GAA TGT GGA AGG TGT TG2228 bp
24H6 reverseAAA GAA AGC TTG GCG TTG AA
15E1015E10 forwardGCT AAA CTG CCT GAC TTC TAC ACG509 bp
20G815E10 reverseCTG GAT ACT GCT GGT TTG ACT ACG
23H6
16B1216B12 forwardCTC TTT ACG CCC AGT GAT TCC613 bp
16B12 reverseTTA TTT GCG TGT TCC TCG TCT ATT
19F1019F10 forwardACA TCA TCG CCG CTA AGG TA772 bp
19F10 reverseTAT GGG ATT CTG TTG TTT CGT AA
27G1027G10 forwardAGC GGC TTA CCT CAA GAA CA1803 bp
27G10 reverseGCT GAG AAC CCA GAA AGT CG

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MDPI and ACS Style

Penesyan, A.; Ballestriero, F.; Daim, M.; Kjelleberg, S.; Thomas, T.; Egan, S. Assessing the Effectiveness of Functional Genetic Screens for the Identification of Bioactive Metabolites. Mar. Drugs 2013, 11, 40-49. https://doi.org/10.3390/md11010040

AMA Style

Penesyan A, Ballestriero F, Daim M, Kjelleberg S, Thomas T, Egan S. Assessing the Effectiveness of Functional Genetic Screens for the Identification of Bioactive Metabolites. Marine Drugs. 2013; 11(1):40-49. https://doi.org/10.3390/md11010040

Chicago/Turabian Style

Penesyan, Anahit, Francesco Ballestriero, Malak Daim, Staffan Kjelleberg, Torsten Thomas, and Suhelen Egan. 2013. "Assessing the Effectiveness of Functional Genetic Screens for the Identification of Bioactive Metabolites" Marine Drugs 11, no. 1: 40-49. https://doi.org/10.3390/md11010040

APA Style

Penesyan, A., Ballestriero, F., Daim, M., Kjelleberg, S., Thomas, T., & Egan, S. (2013). Assessing the Effectiveness of Functional Genetic Screens for the Identification of Bioactive Metabolites. Marine Drugs, 11(1), 40-49. https://doi.org/10.3390/md11010040

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