Elsevier

Journal of Invertebrate Pathology

Volume 131, October 2015, Pages 137-154
Journal of Invertebrate Pathology

The use of -omic tools in the study of disease processes in marine bivalve mollusks

https://doi.org/10.1016/j.jip.2015.05.007Get rights and content

Abstract

Our understanding of disease processes and host–pathogen interactions in model species has benefited greatly from the application of medium and high-throughput genomic, metagenomic, epigenomic, transcriptomic, and proteomic analyses. The rate at which new, low-cost, high-throughput -omic technologies are being developed has also led to an expansion in the number of studies aimed at gaining a better understanding of disease processes in bivalves. This review provides a catalogue of the genetic and -omic tools available for bivalve species and examples of how -omics has contributed to the advancement of marine bivalve disease research, with a special focus in the areas of immunity, bivalve–pathogen interactions, mechanisms of disease resistance and pathogen virulence, and disease diagnosis. The analysis of bivalve genomes and transcriptomes has revealed that many immune and stress-related gene families are expanded in the bivalve taxa examined thus far. In addition, the analysis of proteomes confirms that responses to infection are influenced by epigenetic, post-transcriptional, and post-translational modifications. The few studies performed in bivalves show that epigenetic modifications are non-random, suggesting a role for epigenetics in regulating the interactions between bivalves and their environments. Despite the progress -omic tools have enabled in the field of marine bivalve disease processes, there is much more work to be done. To date, only three bivalve genomes have been sequenced completely, with assembly status at different levels of completion. Transcriptome datasets are relatively easy and inexpensive to generate, but their interpretation will benefit greatly from high quality genome assemblies and improved data analysis pipelines. Finally, metagenomic, epigenomic, proteomic, and metabolomic studies focused on bivalve disease processes are currently limited but their expansion should be facilitated as more transcriptome datasets and complete genome sequences become available for marine bivalve species.

Introduction

The -omic-based disciplines focus on the analysis of structure and function of the genetic material (genomics and epigenomics), expressed genes (transcriptomics), proteins (proteomics), and low molecular weight metabolites (metabolomics) in an organism using an array of recently developed analytical high-throughput technologies. Additionally, these techniques allow for the study of species composition or expressed gene pathways in a complex mix of microorganisms (metagenomics/metatranscriptomics). These technologies have allowed researchers to better determine the complex relationships between genotypes, phenotypes, and the environment by simultaneously analyzing large numbers of individuals, genes, proteins, or metabolites in samples directly collected from the environment (reviewed in Carvalho and Creer, 2010). Therefore, these tools are particularly suited to study the complex interactions between hosts, pathogens, and the environment that lead to disease outbreaks (Fig. 1). In recent decades, there has been an explosion of the application of -omic technologies to address fundamental and applied research questions in human and veterinary medicine, from elucidation of novel mechanisms of immunity in model and non-model host species (Dheilly et al., 2014) to applications of whole-genome sequencing of pathogens to the development of diagnostic, prevention, and treatment tools (Firth and Lipkin, 2013). This explosion has been facilitated by the development of tools for the analysis of genomes of non-model species, as well as the decrease in the cost of high-throughput analysis technologies.

The field of aquatic pathology has benefited from these major investments in studying the genomes, transcriptomes, and, to a lesser extent, the proteomes, metabolomes, and epigenomes of species of commercial and ecological interest. These tools have been also applied to the study of key pathogens causing major mortalities in these species. This review builds upon and expands several excellent reviews on the -omics of bivalve species (Cancela et al., 2010, Gestal et al., 2008, Guo et al., 2008, Peng, 2013, Rodrigues et al., 2012, Romero et al., 2012, Saavedra and Bachère, 2006, Schmitt et al., 2012, Suárez-Ulloa et al., 2013, Wang et al., 2013, Yue, 2014) to provide some examples on how -omic tools have been used in the study of immunity and host–pathogen interactions in molluscan bivalves, and how this knowledge has been applied to two key areas in the management of the impact of infectious diseases on wild and cultured populations of these species: (a) the development of disease resistant strains (Section 5); and (b) the development of diagnostic tools (Section 8).

Section snippets

Genetic and genomic resources available for disease research and management in bivalve species

Most of the health management issues affecting marine bivalve molluscs can be addressed through improved knowledge of (1) the genetic mechanisms underlying relevant traits such as fast growth (which reduces risks to some disease-related losses by decreasing harvest time) and resistance/tolerance to diseases, (2) existing environmental stressors and anticipated stressors resulting from environmental change, and (3) the ability of bivalves to limit the uptake and accumulation of human pathogens

The application of -omic tools to the study of bivalve immunity

Studies on the patterns of gene expression in bivalves in response to disease challenge have used medium-throughput approaches to sequencing and analysis of gene expression including EST analysis, suppression subtractive hybridization, microarrays and, more recently, high-throughput RNAseq (Table 1). This recent burst in bivalve transcriptome studies focused on responses to pathogens have led to the extensive characterization of immune-related genes in several bivalve species (Fig. 2),

The application of -omic tools to the study of host–pathogen interactions in bivalves

Transcriptomic and proteomic studies have also significantly increased our understanding of host–pathogen interactions in marine bivalves. In this section, we provide some examples on how these technologies have been applied to the study of bivalve responses to viral, bacterial, and parasitic challenge and attempt to summarize the most general findings from these studies.

Application of -omic tools to the study of mechanisms of disease resistance in bivalves

Functional genomics and proteomics methods can take advantage of the availability of disease-resistant families or lines to evaluate potential genes and processes involved in disease resistance. For example, candidate genes potentially associated with disease resistance to OsHV-1 identified through these studies include a superoxide dismutase [Cu–Zn] and inhibitor of NF-kappaB 2 (Green and Montagnani, 2013, Normand et al., 2014, Sauvage et al., 2010, Segarra et al., 2014c). Two studies, one

Functional genomics and proteomics of bivalve pathogens: mechanisms of immune avoidance

As expected, pathogens have co-evolved mechanisms to avoid and exploit immune defenses in bivalves (see for example review by Schmitt et al., 2012), and -omic tools can help provide insights into some of these mechanisms. The availability of cultures for bivalve pathogens allows for the study of in vitro responses of the pathogens to environmental stress or host extracts. Characterization of ESTs in P. marinus (Joseph et al., 2010) identified many genes potentially involved in the uptake,

Epigenomics, immunity, and disease processes in bivalves

Not all the heritable variation present in natural populations can be detected by differences in DNA sequences. Epigenetics is the science that studies those DNA sequence (genome)-independent changes that can result in phenotypic changes. Examples of epigenetic mechanisms include DNA methylation, non-coding RNAs, histone modifications and chromatin remodeling. These mechanisms alter gene expression without changing the underlying DNA sequence (Bogdanović, 2014, Cao, 2014).

Eukaryotic DNA

The use of -omic tools in pathogen identification and disease diagnosis

Another important area in bivalve health management potentially benefiting from -omic technologies is the area of diagnosis. Accurate and rapid diagnosis of pathogens is critical in the management of diseases and in studying infections and immune responses in host organisms. Diagnosis of diseases has been greatly improved by the development of molecular or genetic diagnostic tools. Robust end-point and quantitative PCR-based diagnostic protocols have been developed for all major bivalve

Metagenomics and population genomics

High-throughput sequencing technologies provide the ability to fully characterize the diversity of complex microbial populations in marine environments (the microbiomes), both at the taxonomic (e.g. DNA barcoding, sequencing of 16S or 18S rDNA libraries) and functional (e.g. metagenomics, metatranscriptomics, metabolite production, gene pathway analysis) levels without relying on culture techniques (Kuczynski et al., 2012, Sharpton, 2014). These powerful methods require relatively small amounts

Conclusions and future perspectives

Although the -omic resources available for marine bivalve molluscs pale in comparison to those for many model and commercially important vertebrate species, the rate at which new, low-cost, high-throughput genomic technologies are being developed has led to an expansion in the number of studies aimed at gaining a better understanding of disease processes in bivalves. The analysis of genomes and transcriptomes has revealed that many immune-related gene families are expanded in most bivalve taxa

Conflict of interest

The authors wish to disclose that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome.

Acknowledgements

The authors thank 2 anonymous reviewers for very useful editorial suggestions and the editors of this special issue for their invitation to write this review. We thank Springer Science and Business Media for their kind permission to reprint Fig. 2, published originally in Zhang et al. (2014a).

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