| Literature DB >> 30147432 |
John A Darling1, Raymond M Frederick2.
Abstract
Understanding the risks of biological invasion posed by ballast water-whether in the context of compliance testing, routine monitoring, or basic research-is fundamentally an exercise in biodiversity assessment, and as such should take advantage of the best tools available for tackling that problem. The past several decades have seen growing application of genetic methods for the study of biodiversity, driven in large part by dramatic technological advances in nucleic acids analysis. Monitoring approaches based on such methods have the potential to increase dramatically sampling throughput for biodiversity assessments, and to improve on the sensitivity, specificity, and taxonomic accuracy of traditional approaches. The application of targeted detection tools (largely focused on PCR but increasingly incorporating novel probe-based methodologies) has led to a paradigm shift in rare species monitoring, and such tools have already been applied for early detection in the context of ballast water surveillance. Rapid improvements in community profiling approaches based on high throughput sequencing (HTS) could similarly impact broader efforts to catalogue biodiversity present in ballast tanks, and could provide novel opportunities to better understand the risks of biotic exchange posed by ballast water transport-and the effectiveness of attempts to mitigate those risks. These various approaches still face considerable challenges to effective implementation, depending on particular management or research needs. Compliance testing, for instance, remains dependent on accurate quantification of viable target organisms; while tools based on RNA detection show promise in this context, the demands of such testing require considerable additional investment in methods development. In general surveillance and research contexts, both targeted and community-based approaches are still limited by various factors: quantification remains a challenge (especially for taxa in larger size classes), gaps in nucleic acids reference databases are still considerable, uncertainties in taxonomic assignment methods persist, and many applications have not yet matured sufficiently to offer standardized methods capable of meeting rigorous quality assurance standards. Nevertheless, the potential value of these tools, their growing utilization in biodiversity monitoring, and the rapid methodological advances over the past decade all suggest that they should be seriously considered for inclusion in the ballast water surveillance toolkit.Entities:
Keywords: Ballast water; Compliance; High throughput sequencing; Monitoring; Nucleic acids; PCR
Year: 2018 PMID: 30147432 PMCID: PMC6104837 DOI: 10.1016/j.seares.2017.02.005
Source DB: PubMed Journal: J Sea Res ISSN: 1385-1101 Impact factor: 2.108
Relative merits of genetic and traditional methods. Criteria are worded so that desirable outcomes are always reflected in a “high” rating. Advantages of genetic methods should be taken with some caution, as many nucleic acids tools are still in development phases and have not yet fully achieved their expected potential. For instance, the affordability of genetic tools currently depends on availability of appropriate in-house or extramural molecular support, which varies widely across users, and also on sample throughput.
| Criterion | Genetic methods | Traditional morphological methods |
|---|---|---|
| Sensitivity | HIGH | low |
| Specificity | HIGH | low |
| Ability to identify sub-adult or partial specimens | HIGH | low |
| Ability to identify cryptic taxa | HIGH | low |
| Quantification | low | HIGH |
| Opportunity for passive surveillance | HIGH | low |
| Affordability of up-front costs | low | HIGH |
| Affordability per sample | HIGH | low |
| Speed of analytical turnaround | HIGH | low |
| False negative avoidance | HIGH | low |
| False positive avoidance | low | HIGH |
Affordability of genetic methods is highly scale-dependent, and per sample costs are strongly negatively correlated with sample throughput.
Questions associated with ballast water monitoring, and applicable genetic tools. Different nucleic acids-based detection methods satisfy different criteria and are associated with different challenges to technology development and deployment.
| Management/science question | Criteria that must be satisfied to answer question | Possible genetic tools | Most significant challenges |
|---|---|---|---|
| Single species approaches | |||
| Does the sample contain target species X? | Target specificity, sensitivity | PCR/qPCR or other probe-based detection methods | Managing false positive and negative errors |
| What is the abundance of target species X in the sample? | Target specificity, sensitivity, quantification | qPCR or other probe-based detection method calibrated for quantification of target | Managing false positive and negative errors, plus calibration for robust quantification of target |
| Does the sample comply with a standard? | Target specificity, sensitivity, quantification, viability | qPCR targeting transcripts associated with viability | Managing false positive and negative errors, calibration for robust quantification of target, plus identification of targets tightly associated with viability and possibly additional costs associated with handling RNA targets |
| Community approaches | |||
| What species are in the sample? | Broad community profiling, sensitivity | HTS metabarcoding | Gaps in reference databases, difficulty interpreting data from rare species, not amenable to fast turnaround |
| What is the overall biodiversity (species richness and abundance) in the sample? | Broad community profiling, sensitivity, quantification | HTS metabarcoding, calibration of sequence frequency data to relative abundance | Gaps in reference databases, difficulty interpreting data from rare species, not amenable to fast turnaround, plus calibration for robust quantification |
Fig. 1.Workflow for targeted probe-based detection (left) and HTS-based community profiling (right), including potential sources of error introduced at each process step. False positive and false negative errors can derive from multiple steps in each process. *Encompasses a broad range of error sources including, but not limited to, poor filtering of reads, failure to remove chimeras, and inaccuracies in taxonomic assignment.
Published direct applications of molecular genetic tools for ballast water monitoring and/or research. Note that in a number of these publications nucleic acids-based methods are not adopted as the primary detection technology. Abbreviations for methodologies are defined in the text.
| Authors | Journal | Target organisms | Target loci | Methodology | |
|---|---|---|---|---|---|
| 1. | 18S,ORF1, tcpA | PCR, qPCR | |||
| 2. | 18S | qPCR | |||
| 3. | Total bacteria, enterobacteria, | Fluorescence In situ Hybridization (FISH) | |||
| 4. | 18S | qPCR | |||
| 5. | 16S | Sanger sequencing, barcoding | |||
| 6. | 18S | PCR | |||
| 7. | Harmful algae | 18S, 16S | PCR | ||
| 8. | mcyE | PCR and qPCR | |||
| 9. | multiple | ITS1, ITS2, 5.8S | PCR, Sanger sequencing | ||
| 10. | Planktonic bacterial community | 16S | Restriction fragment length polymorphisms (RFLP) | ||
| 11. | microbial community | 16S | Denaturing gradient gel electrophoresis (DGGE) | ||
| 12. | Diapausing eggs | 16S | Sanger sequencing, barcoding | ||
| 13. | 16S | Sanger sequencing, barcoding | |||
| 14. | 16S | PCR | |||
| 15. | groEL, tcpA | qPCR, NASBA | |||
| 16. | Phytoplankton | 16S | DGGE | ||
| 17. | Microbial community | 16S | HTS with propidium mono-azide (PMA) cross-linking | ||
| 18. | Eukaryotic community | 18S | DGGE | ||
| 19. | COI | HTS | |||
| 20. | CO1, species Specific | PCR and LTS | |||
| 21. | Viral community | 16S | HTS | ||
| 22. | Microbial community | 16S | HTS and qPCR | ||
| 23. | Phytoplankton | 18S | Sanger sequencing, barcoding | ||
| 24. | Multiple | COI, RBC | HTS | ||
| 25. | Multiple | COI | HTS | ||
| 26. | Microbial eukaryotes | SSU RNA | HTS |