'Identification and Characterization of Candidate Genes Influencing Salinity

Home , Osmoconformer

‘Identification and characterization of candidate genes influencing salinity tolerance in Macrobrachium australiense: a model for the molecular basis of colonization of low ionic environments’

Azam Moshtaghi

BSc (General Biology), MSc (Cellular & Developmental Biology)

Principal supervisor: Dr. David A Hurwood (EEBS, QUT)

Associate Supervisor: Professor Peter B Mather (EEBS, QUT)

School of Earth, Environmental and Biological Science

Science and Engineering Faculty

Queensland University of Technology

Brisbane, Australia

A thesis submitted in fulfilment of requirements for the degree of Doctor of Philosophy

2017

Key Words

Macrobrachium australiense, Osmoregulation, Na+/K+ -ATPase (NKA), V-type ATPase (VTA),

Carbonic Anhydrase (CA), Aquaporin (AQP), Na+/K+/2Cl- cotransporter (NKCC), Calreticulin

(CRT), Na+/H+ exchanger, osmoregulatory genes, gill, hepatopancreas, antennal glands, quantitative real-time polymerase chain reaction (qRT-PCR), transcriptome, next generation sequencing (NGS), single nucleotide polymorphism (SNP), salinity tolerance, osmotic pressure, haemolymph osmolarity, crustacean taxa, decapods, osmoregulator, osmoconformer, hyper- osmoregulation, hypo-osmoregulation, particle per thousand (ppt,‰).

Abstract

Freshwater prawns in the genus Macrobrachium display a wide variety of life history strategies and occur in a diverse array of aquatic environments and naturally occupy a wide range of habitats

(including salinities) while presenting a variety of behaviors to deal with changing environments

(Piao, et al. 2010). Functional genomic resources associated with effective osmoregulatory capacity in crustaceans play a fundamental role in survival when individuals are exposed to environmental stressors. Identifying candidate gene (CG) loci and functional mutations within these genes that affect salinity tolerance as well as understanding gene expression patterns can assist predictive power for likely outcomes of the effects of climate change on wild populations and how impacts can be mitigated in both wild and farmed stocks.

Here I used Macrobrachium australiense (Palaemonidae) as a model for freshwater (FW) prawns in the genus Macrobrachium more widely because it is the most widespread FW prawn species across mainland Australia. Moreover, this species is successfully adapted to a complete freshwater life cycle, yet under controlled conditions in the laboratory can tolerate up to 17 ‰ salinity. To date based on published literature there has been no comprehensive data published about the role of all categories of potential candidate genes that are directly involved in osmoregulation in FW prawns. New genomic and molecular technologies provide powerful methods that can assist understanding of adaptation to FW conditions at the molecular and cellular levels under environmental stress. Environmental salinity is probably the most important abiotic factors that can affect survival and natural distributions of a wide range of crustacean species. So, identification and characterization of the major candidate genes influencing salinity tolerance and the mutations in these genes that encode novel phenotypes can help to unravel the molecular basis of osmoregulation in this important group.

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In chapter 2 (study 1), a comprehensive genomic resource was generated using NGST (next generation sequencing technology) to identify the key candidate genes involved in osmoregulation in M. australiense. The transcriptome data set was generated from four cDNA libraries (gill, antennal gland, hepatopancreas and whole post-larvae) and resulted in 32 different candidate genes families with potential major roles in osmoregulation and salinity tolerance processes. Overall

25.43% of the assembled contigs showed significant blast hits and around 17% contigs produced

GO term annotations. Of the osmoregulatory gene families identified here, 17 genes were

+ + + + + - identified to be involved with transport of the most important ions (Na , K , H , Ca2 , Mg2 , HCO3 and Cl-). Differential gene expression patterns (based on transcript abundance estimates) of candidate genes involved with osmoregulation, clearly indicate major roles for gill and antennal gland tissues in adult individuals as CG expression patterns in these tissues were high compared with that seen in the hepatopancreas. Of interest however, was that post-larvae showed only moderate expression levels of the same candidate genes. Differential gene expression analysis suggested that the higher number of genes expressed differentially in post-larvae could be related to rapid developmental changes that are occurring during early life history stages that require raised levels of expression of genes involved with growth, tissue development and other developmental processes.

In chapter 3 (study 2), potential outlier SNPs were identified in 3 populations of M. australiense exposed to different natural osmotic niches using a comparative transcriptomics approach to understand the molecular basis of osmoregulation. A hypothesis was tested that outlier SNPs identified in M. australiense populations exposed naturally to different osmotic conditions influence specific gene expression patterns that allow individuals to respond to variation in local salinity conditions. Here, I found most mutations (outliers) in M. australiense were located outside

IV of coding regions (i.e. in UTRs), suggesting that osmoregulatory control under different environmental conditions is not controlled by changes to amino acid sequences in M. australiense, rather it is controlled by regulatory mutations that alter expression patterns of essentially identical functional genes. This implies that adaptation by M. australiense to various osmotic niches is likely to be an ongoing process and is currently largely controlled by changes in gene expression under different environmental conditions. Four outlier loci were identified in target candidate

(osmoregulatory) genes: Na+/H+ exchanger, Na+/K+-ATPase, V-type-(H+)-ATPase and

Calreticulin. These four genes are considered to be the major genes controlling osmoregulation in a wide variety of aquatic crustacean species. Following this, sequences of outlier contigs between different sampled populations were compared to determine the physical locations of identified outlier SNPs. All outlier SNPs in the 4 important osmoregulatory genes in M. australiense were present in UTR regions and were located prior to the start codon region. This suggests that they are likely to be regulatory mutations that control gene expression patterns of the critical functional genes.

Chapter 4 (study 3) screened gene expression patterns under sub-lethal stress conditions to resolve the molecular bases of gene expression regulation in response to stressors. The objectives of the study were to measure relative gene expression levels of the five major candidate genes in M. australiense controlling osmotic adaptation in experimental animals exposed to 3 experimental sub-lethal salinity levels (0, 5 and 10‰) over a period of 5 days at different time intervals and to understand the potential roles that they play in the adaptive response. Higher expression level of

NKA (1.4 to 3 fold higher) was observed at 5‰ and 10‰ compared with freshwater (0‰). For

NKCC, significant differences were observed for 0‰ vs 5‰, 0 ‰ vs 10‰ and 5‰ vs 10‰ treatments with 3-6 fold differences in expression level. Raised salinity (5‰ and 10‰) induced

V higher NKCC expression apparently to facilitate greater ion transport and ion exchange under changed osmotic conditions.

NHE is known to play an important functional role in maintaining osmotic balance in low ionic environments (freshwater habitats) compared with saltwater conditions (5‰ and 10‰). NHE expression levels declined gradually over time in the 5‰ and 10‰ treatments. Expression levels of the AQP gene were high initially after which expression of this gene also declined sharply.

Towards the end of the experiment, expression levels in all treatments reached a fairly constant and relatively low level in contrast to initial levels. Significant differences in expression patterns were noticed between the 0‰ and 10‰ treatments by the second day while, significant differences were observed across the whole experimental period between the 5‰ and 10‰ treatments. Of interest, higher AQP expression at 0‰ than in 5‰ at T4 to the end of the experiment was seen.

Also comparatively high relative VTA expression levels was observed in the 5‰ and 10‰ treatments over the first 24h; levels then declined between 24 and 48 hours and then reached a relatively constant expression pattern in all treatments from T2 to the end of the experiment.

Significant differences among treatments were evident for first 24 hours, after which no significant differences were evident between any treatments.

Overall, the three studies produced full-length gene sequences of the most important genes involved in osmoregulation from M. australiense. In addition, deeper analyses were performed to uncover the molecular/cellular bases of adaptation to FW conditions in this prawn as a model for osmoregulation in other Macrobrachium family members. The study can provide a foundation for future genomic investigations of FW prawns and can allow a comprehensive assessment of how evolution of freshwater by species in the genus Macrobrachium is achieved at the molecular level.

Table of Content

Key Words ______II

Abstract ______III

List of Figures ______XI

List of Tables ______XIII

List of Abbreviations ______XIV

General note on thesis preparation ______XVI

Statement of Original Authorship ______XVII Works Published or Submitted for Publication by the Author incorporated into the Thesis ______XVIII

Acknowledgements ______XIX

1 Chapter 1. General Introduction ______1 1.1 Impacts of climate change ______1 1.2 An overview of osmoregulation in crustaceans ______6 1.2.1 Osmoconformers: ______8 1.2.2 Osmoregulators: ______10 1.2.3 Compensatory process ______14 1.2.4 Limiting process ______14 1.3 Osmoregulation capacity (OC) during development (Ontogeny): ______15 1.4 The role of pivotal organs and identified genes in osmoregulation______19 1.5 Candidate gene approach (Transcriptomics) to identify key genes involved in osmoregulation 22 1.6 Macrobrachium australiense as a model for salinity tolerance in FW prawns. ______24 1.7 Next Generation Sequencing (NGS) ______26 1.8 Single Nucleotide Polymorphism (SNP) identification ______27 1.9 Differential gene expression (DGE) patterns and morphological changes under osmotic challenge ______29 1.10 Knowledge gaps and research questions ______31 1.11 Objectives and aims ______32

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2 Chapter 2: A transcriptomic scan for potential candidate genes involved in osmoregulation in an obligate freshwater palaemonid prawn (Macrobrachium australiense) ______34 2.1 Abstract______36 2.1.1 Background ______36 2.2 Introduction ______37 2.3 Materials and Methods ______41 2.3.1 Sample collection and maintenance ______41 2.3.2 RNA extraction, cDNA library preparation and Illumina sequencing ______42 2.3.3 Quality control of Illumina sequence data ______43 2.3.4 Bioinformatics analyses ______43 2.3.5 Identification of potential genes ______44 2.4 Results ______46 2.4.1 Illumina sequencing, de novo assembly and annotation ______46 2.4.2 Identification of putative osmoregulatory gene families (contigs) ______48 2.4.3 Differential gene expression (DGE) patterns ______51 2.5 Discussion ______52 2.6 Supplementary files ______59

3 Chapter 3. Understanding the genomic basis of adaptive response to variable osmotic niches in freshwater prawns: a comparative intraspecific analysis of Macrobrachium australiense ______60 3.1 Abstract______62 3.2 Introduction ______63 3.3 Materials and methods ______66 3.3.1 Sample collection and maintenance ______66 3.3.2 RNA extraction, cDNA library preparation and Illumina sequencing ______67 3.3.3 Quality control of Illumina sequence data ______68 3.3.4 Bioinformatics analysis ______68 3.3.5 SNP detection ______69 3.3.6 Outlier detection ______70 3.3.7 Differential gene expression (DGE) and functional enrichment analysis ______70 3.4 Results ______70 3.4.1 Illumina sequencing, de novo assembly and annotation ______70 3.4.2 Candidate gene identification and differential gene expression (DGE) analysis ______71 3.4.3 SNP and outlier detection ______73 3.4.4 Functional enrichment analysis ______74 3.5 Discussion ______75

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3.6 Supplementary files ______81

4 Chapter 4. An investigation of gene expression patterns that contribute to effective osmoregulation in Macrobrachium australiense: assessment of adaptive responses to variable sub-lethal osmotic conditions ______86 4.1 Abstract______88 4.2 Introduction ______89 4.3 Methodology ______92 4.3.1 Sample collection and maintenance ______92 4.3.2 Salinity stress experiment and tissue collection ______92 4.3.3 RNA extraction and cDNA synthesis ______93 4.3.4 Primer design and quantification of gene expression via real time quantitative PCR (RT-qPCR) ___ 94 4.4 Results ______95 4.4.1 Na+/K+-ATPase (NKA) expression ______95 4.4.1.1 Within sampling times ______96 4.4.1.2 Between sampling times ______96 4.4.2 Na+/K+/2Cl-Co-transporter (NKCC)______97 4.4.2.1 Within sampling times ______97 4.4.2.2 Between sampling times ______98 4.4.3 Na+/H+ exchanger (NHE) ______98 4.4.3.1 Within sampling times ______98 4.4.4 Between sampling times ______99 4.4.5 Aquaporin (AQP) ______99 4.4.5.1 Within sampling times ______99 4.4.5.2 Between sampling times ______100 4.4.6 Vacuolar-type H+-ATPase (VTA) ______100 4.4.6.1 Within sampling times ______100 4.4.6.2 Between sampling times ______101 4.5 Discussion ______101 4.6 Conclusions ______108

5 Chapter 5: General Discussion ______110 5.1 Group 1 Genes (Stress sensing and signalling) ______124 5.2 Group 2 Genes (Ion regulation) ______125 5.3 Group 3 Genes (Cell volume control) ______126 5.4 Group 4 Genes (Water channel regulation) ______127 5.5 Group 5 Genes (Body fluid maintenance) ______128 5.6 Interaction of the five most important osmoregulatory genes in M. australiense under salinity stress and their role with respect to FW adaptation ______129

5.7 Conclusions ______132

6 References: ______135

List of Figures

Fig 1-1: Transcriptome assembly and analysis workflow. The first step is to collect target tissue from target species to obtain good quality RNA which is then converted to cDNA library to be sequenced in NGS platforms. Sequence data is then processed by bioinformatics pipelines for gene identification or discovery based on blast similarity matches with other species. Moreover, DGE can be performed to compare between different biological or experimental conditions or even between tissue types. ______27 Fig 2-1: represents the top hit species distribution chart from blast results. Daphnia pulex was the top hit species. While D. pulex is distantly related to M. australiense, it is to date, one of the crustacean species for which a complete sequenced and well annotated genome is available. The ‘Others’ category contained many crustacean taxa including commercially important Penaeids, in particular, P. monodon and L. vannamei. ______47 Fig 2-2: Top most represented GO terms per biological categories: a) molecular function, b) cellular component and c) biological process. ______50 Fig 2-3: Venn diagram showing the number of expressed transcripts shared among different samples based on abundance of transcripts per million (TPM). ______52 Fig 2-4: Heatmap showing differential gene expression pattern (based on read counts) of different tissues of adult and post-larvae. AEB= Antennal Gland, GB= Gill, HIB= Hepatopancreas, PL= Post-Larvae. Y-axis represents the hierarchical clustering of differentially expressed transcripts, while red colored lines indicate highly expressed transcripts and green colored lines indicate lowly expressed transcripts. ______59 Fig 3-1: Venn diagram of outlier contigs for different population comparisons (Blunder/Bulimba comparison in blue, Blunder/Stony comparison in yellow and Bulimba/Stony comparison in green). ______74 Fig 3-2: Top hit species distribution chart (absolute values of blast match hits with different species). ______81 Fig 3-3: Top most abundant GO term categories for different mechanisms: a) biological process, b) cellular component and c) molecular function (absolute number of expressed transcripts assigned to each GO categories). ______83 Fig 3-4: Expression pattern of the differentially expressed transcripts for individuals collected from different populations (the red coloured transcripts are highly expressed or upregulated and the green coloured transcripts are downregulated). BuC = Bulimba Creek, StC = Stony Creek, BlC = Blunder Creek ______83 Fig 4-1: NKA (Na+/K+-ATPase) mRNA expression patterns (relative to 18S expression) in the gill of M. australiense for three salinity treatments over a five day period. Error bars represent

+/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times). ______97 Fig 4-2: Relative NKCC (Na+/K+/2Cl- Co-transporter) mRNA expression levels in M. australiense in 3 salinity treatments. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times). ______98 Fig 4-3: Relative NHE (Na+/H+ exchanger) mRNA expression patterns in the gill tissues of M. australiense in 3 salinity treatments. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times). ______99 Fig 4-4: Relative AQP (aquaporin) mRNA expression levels in M. australiense at 3 salinity treatments. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times). ______100 Fig 4-5: Relative VTA (V-type H+-ATPase) mRNA expression levels in 3 salinity treatments. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at the α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times).101 Fig 5-1: A model for integration of potential candidate genes involved in osmoregulation in M. australiense. ______123 Fig 5-2: Expression levels of the five major osmoregulatory genes in M. australiense over a five day period (a – 0ppt, b – 5ppt and c – 10ppt). ______130

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List of Tables

Table 1-1: Specific genes previously identified as having a functional role in salinity tolerance. ______21 Table 2-1: Specific genes previously identified as having a functional role in salinity tolerance in crustaceans. ______45 Table 2-2: Summary of raw reads and quality control of Illumina results (Q ≥ 30, 75 bp paired- end reads). ______46 Table 2-3: Summary of de novo assembly and functional annotation results for M. australiense. ______47 Table 2-4: Summary of identified candidate genes potentially associated with osmoregulation in M. australiense from this study. ______48 Table 2-5: Top 10 osmoregulatory genes expressed in different tissues in adult and PL individuals. ______51 Table 3-1: Assembly, annotation and transcriptome completeness features of M. australiense. 71 Table 3-2: Abundance estimation (DGE) of the most important 15 osmoregulatory genes between 3 different populations of Macrobrachium australiense. ______72 Table 3-3: Top 20 genes/transcripts expressed in different populations of M. australiense. __ 72 Table 3-4: SNP table for three Macrobrachium australiense populations. ______73 Table 3-5: Enriched GO terms for the Blunder/Bulimba comparison and the Blunder/Stony comparison. ______74 Table 3-6: Summary of the identified candidate genes potentially associated with osmoregulation in M. australiense from this study. (GenBank Accession Numbers will be available upon acceptance of the paper). ______84 Table 4-1: Specific primers used in the current study (after Moshtaghi et al., 2016).______94

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List of Abbreviations

ABCC= ATP Binding Cassette C12 ACBP=Acyl-CoA binding protein

AK= Arginine Kinase ALD= Abbreviated larvae Development AP= Alkaline Phosphatase AQP= Aquaporin BEST= Bestrophin bp= base pair CA= Carbonic Anhydrase CCP= Crustacean cardiovascular peptide CEGMA= Core Eukaryotic Genes Mapping Approach CFTR= Cystic Fibrosis Transmembrane Regulator CG= Candidate Gene COMT= Catechol-o-methyltransferase CRT= Calreticulin DGE= Differential Gene Expression GO= Gene Ontology HAT= V-type-(H+) ATPase HOG= Hedgehog signaling pathway ILF= Interlukin enhancer binding factor ITG= Integrin MAPK= Mitogen Activated Protein Kinase MTCP= Mitochondrial Carrier Protein NBC= Na+/HCO3- cotransporter NCX= Na+/Ca+2 exchanger

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NGS= Next Generation Sequencing NHE= Na+/H+ exchanger NKA= Na+/K+-ATPase NKCC= Na+/K+/2Cl- cotransporter OC= Osmoregulatory Capability OST= Oligosaccharyltransferase Complex qRT-PCR= quantitative Real Time - Polymerase Chain Reaction SNP= Single Nucleotide Polymorphism SP= Selenoprotein SPS1= Selenophosphate1 TPM= transcripts per million UTR= Untranslated Region

General note on thesis preparation

In this study, each chapter contains an individual introduction, methodology, results, discussion and conclusion section. Chapters 2, 3 and 4 of this thesis represent independent publishable studies (see below) that address specific objectives that consequentially require individual introduction and discussion sections distinctive to each study. In parts, repetition of information presented in the General Introduction (Chapter

1), General Discussion and Conclusions (Chapter 5) sections was unavoidable.

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Statement of Original Authorship

This thesis is composed of my original work and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

Signature: Azam Moshtaghi

QUT Verified Signature

Date: May, 2017

XVII Works Published or Submitted for Publication by the Author incorporated into the Thesis

Statement of contribution to jointly authored works in the thesis

1. Moshtaghi et al. (2016), A transcriptomic scan for potential candidate genes involved in osmoregulation in an obligate freshwater palaemonid prawn (Macrobrachium australiense). PeerJ 4:e2520; DOI 10.7717/peerj.2520 This manuscript is incorporated as Chapter 2 of this thesis. The first author was responsible for conducting the research, analysis of data and writing this manuscript. The co-authors were involved in refining the experimental design and provided conceptual and editorial support.

2. Moshtaghi et al. (submitted). Understanding the genomic basis of adaptive response to variable osmotic niches in freshwater prawns: a comparative intraspecific analysis of Macrobrachium australiense. Manuscript submitted in the Journal of Heredity.

This manuscript is incorporated as Chapter 3 of this thesis. The first author was responsible for conducting the research, analysis of data and writing this manuscript. The co-authors were involved in refining the experimental design and provided conceptual and editorial support.

3. Moshtaghi et al. (submitted). An investigation of gene expression patterns that contribute to effective osmoregulation in Macrobrachium australiense: assessment of adaptive responses to variable osmotic gradients. Manuscript submitted in the Hydrobiologia.

This manuscript is incorporated as Chapter 4 of this thesis. The first author was responsible for conducting the research, analysis of data and writing this manuscript. The co-authors were involved in refining the experimental design and provided conceptual and editorial support.

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Acknowledgements

I wish to extend my sincere thanks to my excellent supervisors- principal supervisor, Dr. David Hurwood

(School of Earth, Environmental and Biological Sciences, Science and Engineering Faculty, Queensland

University of Technology) and my associate supervisor, Professor Peter B Mather (School of Earth,

Environmental and Biological Sciences, Science and Engineering Faculty, Queensland University of

Technology), for their steady support, guidance, and patience throughout the long years of my candidature.

In particular, I would like to acknowledge for introducing me to new ideas, and bringing out the research insight. Also special thanks for providing me with the unique opportunity of working with the Molecular

Genetic Group at the Queensland University of Technology

I would like to acknowledge the funds provided by QUT (Queensland University of Technology) Higher

Degree Research Support for tuition fee waiver and a 6 months scholarship provided by EEBS, Head of

School, Professor Stuart Parsons that facilitated this research.

Thanks to all in the QUT Molecular Genetic Resource Facility (MGRF) for help during my project period especially, Dr. Kevin Dudley and Vincent Chand and Sahana Maloni who supported me and arranged facilities at QUT Molecular Genetics Lab. Also I would like to thank technical staff at Banyo Pilot Plant

Precinct who arranged and organised everything nicely for rearing live Macrobrachiums at the Banyo Pilot

Plant Precinct and I would also thank to Hamish Kelly for sequencing my samples and to Frank De Bruyne and his team for providing equipment at Banyo Aqualab. I also would like to thank administrative, technical, academic and research staff within the School of Earth, Environmental and Biological Sciences for making a friendly environment with a feeling of belonging to a community of people.

I would like to express my gratitude for support and cooperation of my best friend, Md. Lifat Rahi (PhD student, EEBS, QUT) for his knowledge and experience and assisting me with sample collection and appropriate laboratory work.

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Finally, I sincerely appreciate that all members of our group met regularly and discussed issues related to our project work, shared our experiences and learnt new knowledge.

Special thanks to my beloved and supportive husband and dearest daughter for enduring the sacrifice and difficulties during my study. No words are sufficient to acknowledge and thank for supporting and encouraging me.

I dedicate the thesis to my father and mother. I know they will be happy more than anyone else to see its completion.

1 Chapter 1. General Introduction

1.1 Impacts of climate change

Impacts of climatic change on aquatic systems that result from human activities are increasing and responses to this change can now be observed at most levels of life (Allison et al., 2005). Direct and indirect impacts of climate change should be considered across all levels of organization from gene expression and cellular/organism physiology to whole ecosystem structure. Taken together, they can lead to changes in vital factors including growth, mortality, fecundity and distributions via altering physiological and behavioral factors and can affect the structure of both freshwater and marine ecosystems and their species compositions (Brander, 2010). Rate of vulnerability to climate change depends on: 1) Exposure (E) which relates to the condition and degree of climatic changes that can alter system productivity; 2) Sensitivity (S) which means rate of vulnerability of climatic conditions under change; 3) Potential Impact (PI) that implies all impacts of climatic risks on organisms including trends, shock and seasonality; and 4) Adaptive Capacity (AC) that is the capacity of living systems to change or modify to deal with altered normal conditions (Allison et al., 2005).

Constant change in the surrounding environment can place organisms at risk, leading to increases in the rate of oxidative stress, changes to nutrient sources, changes in pH and temperature and unbalanced osmotic gradients that demand adaptive responses to maximize survival at the cellular and organism levels (Argüelles et al., 2013). These changes can adversely affect global aquaculture production because this sector is highly susceptible to any sort of environmental change.

Significance of fisheries to national economies in many developing countries and lack of social policies to address climate change impacts mean that resources for aquaculture may be significantly negatively impacted if this problem is not addressed (Tyler et al., 2009). Rapid impacts of climate change over the short term in both marine and freshwater ecosystems can explain the physiological changes that lead to shifts in natural distributions and species abundance as well as more explicit effects on farming of aquatic species (Pörtner and Knust 2007; Cochrane et al., 2010).

Due to the direct effects of sea level rise, we have already seen a decline in natural breeding of marine organisms in coastal regions that impact wild and farmed aquatic species. Results of this damage can reduce profitability and cause loss of coastal ecosystems. In addition, indirect influences of rapid temperature change in ocean currents increase the rate of drought on land and reduce water flows, leading to shifts in river and lake water flows. All direct/indirect changes described above are factors that can reduce productivity and yields from natural aquatic ecosystems (de Nadal et al., 2011).

According to U.S. Global Change Research in 1990 (GCRA), predicted climate change may potentially impact global productivity, the oceans and other natural water resources and ecological systems that together could lead to a shift in the earth’s ability to sustain life as we currently know it. Global warming already has resulted in melting of some glaciers, sea level rise, coastal ablation and increased evaporation in some freshwater ecosystems. Currently, only 0.01% of all the world’s water is fresh water that covers only 0.8 % of the planet surface area, yet this relatively small quantity supports at least 100,000 freshwater taxa out of an estimated 1.8 million extant species

(almost 6% of all described species) (Dudgeon et al., 2006).

Associated impacts of climate change include negative consequences for food production and reduced fresh water availability in many regions around the world (Eissa and Zaki, 2011). While predictions regarding specific outcomes of climate change are still debated (Rodó and Comin,

2003), it is generally agreed that some of the most significant effects will be seen on aquatic organisms and in particular on FW ecosystems, affecting all biological levels that include gene expression, cellular and organismal physiology, behaviour and the dynamic structure of populations and even whole ecosystems (Brierley and Kingsford, 2009; Harrison, 1979). One of the key drivers of climatic change in freshwater is a shift in natural salinity levels that directly or indirectly can influence farming of aquatic species (Perry et al., 2005; Adger et al., 2005; Haines et al., 2006). Direct effects are coupled with changes to the frequency of hazards including hurricanes and penetration of saline water into freshwater habitats and coastal areas (Mackenzie et al., 2007). Indirect impacts of salinity change can lead to changes in ecosystem productivity by changing quality and quantity of aquatic habitats (Edwards and Richardson, 2004). As a result of coastal habitat loss, food security and food production at global and regional scales have been impacted (Rosegrant and Cline, 2003).

The relative vulnerability of marine organisms remains unclear and physical changes to many species are not predictable and depend on the change duration and each organism’s tolerance range

(Brierley and Kingsford, 2009). Salinity can act as an indirect but sensitive indicator and increases in surface water temperatures in many evaporative areas, can lead to significant reductions in surface ocean density, precipitation rates, more evaporation from surface waters, loss of breeding regions and changes to circulation models of marine waters (Cochrane et al., 2009). Taken together, the changes can cause declines in many important native species and give more opportunity for expansion of warm-water species that, in turn, causes more stress to cold-water

3 species (Eissa and Zaki, 2011). Shifts in the distributions of cold-water species replaced by warm- water species can lead to a reduction in species diversity (Allison et al., 2005; Cochrane et al.,

2009).

Dispersal to habitats with more favourable climatic conditions has already been documented in some marine species, for example in shallow-water crabs (e.g. Cancer pagurus) (Pallas et al.,

2006). For freshwater taxa however, latitudinal migration is often more difficult due to the physical constraints imposed by riverine or lacustrine environments (i.e. FW habitats are bounded by marine or terrestrial habitat that is unfavourable to FW species) (Handisyde and Britain, 2006;

Eissa and Zaki, 2011). Furthermore, predicted sea level rises will promote incursion of saline waters into many coastal FW ecosystems. All of these factors are likely to contribute to a significant reduction in suitable FW habitat coupled with an associated decline in biodiversity

(Revenga et al., 2000).

Aquaculture plays an increasingly important role in economic growth and food security in a large number of countries around the world. In fact, world fisheries supply more than 2.6 billion people with at least 20% of their average annual per capita protein intake (FAO, 2007). Currently, billions of humans rely on aquatic species for their primary source of protein and predicted changes to fishery production will therefore impact human populations and economies worldwide (Ficke et al., 2007). A number of researchers that have investigated the effects of climate change on aquatic food abundance have suggested that production is likely to show drastic declines in certain geographical regions because of changes to FW availability. As an example, climate warming will cause greater eutrophication of many FW lakes making them less productive (Eissa and Zaki,

2011). Predicted changes to fishery production due to climate change will impact human populations as well as regional economies worldwide (Ficke et al., 2007).

A number of studies have suggested that FW biodiversity is particularly vulnerable to rising salinity levels. Therefore, many studies have focused on the negative impacts of salinity stress on

FW ecosystems (Halse et al., 2003) because currently there is significant pressure on expanding production of farmed FW species. Unprecedented changes to salinity levels are one of the major abiotic stresses that can have significant negative impacts on FW crustacean physiology and survival (Ali et al., 2015).

Sub-lethal osmotic stress has been considered to be an acute stress and in multicellular organisms can be controlled by specific functional genes in specific target tissues to prepare individuals to cope with external change (Charmantier and Charmantier-Daures, 2001). Even allowing for adaptive responses, current predictions are that 20% of FW species are at risk or are likely to face extinction as a consequence of climate change effects (Revenga et al., 2000). Since approximately

65% of farmed aquatic species in inland waters are produced in tropical and sub-tropical areas, these regions are particularly vulnerable (Cochrane et al., 2009).

Even without considering climate change impacts in Australia, a large portion of available ground water is saline (Blinn et al., 2004) and this high level of inland salinity has resulted from three main processes: 1) removal of deep-rooted native plants from freshwater catchments that has led to saline water flow into rivers (Sliva and Williams, 2001); 2) active human controlled discharge of extra salinity from water into rivers (Kefford, 1998); and 3) mining activity causing saline incursion into rivers. Together this has reduced the growth rate of some FW species including clam

Carbicula fluminea and reduced fecundity levels in Ceriodaphnia (Kennedy et al., 2003). This is because the majority of FW species have only limited ability to control their internal body fluid

(blood or haemolymph) osmolality compared with species that live in brackish or marine waters.

For these organisms, maintaining their internal osmotic pressure is crucial to individual survival,

5 and when faced with increased salinity levels, they will be exposed to increases in body fluid

(haemolymph) osmotic concentration, and will need to deal with raised ionic levels. This requires increased levels of energy. In this situation viability or resilience can be affected over the long term (Hart et al., 1991) and impose strong selective pressures on affected species (Piscart et al.,

2006). As a result, environmental salinity is one of the most important abiotic factors that impacts adaptive osmotic concentrations in aquatic animals and this will affect not only natural distributions of organisms, but also their physiology and long term persistence (Charmantier and

Charmantier-Daures, 2001).

1.2 An overview of osmoregulation in crustaceans

Salinity tolerance/osmotic regulation in aquatic species relies on the ability of individuals to control their haemolymph osmolality (i.e. the total amount of osmolytes in their body fluids and cells including Na+ and Cl- ions, expressed in milliosmoles (mOsm) kg-1) (Lignot et al., 2000).

The osmoregulatory system plays a pivotal role in dealing with changes to ionic concentrations in an organism’s body compared with the aquatic medium they occupy. This complex interaction impacts regulation of total osmotic concentration, regulation of intracellular vs. extracellular ion levels, acid-base balance and levels of several other organic ions (Lignot et al., 2000). Thus a specific reaction to osmostress is to supply intracellular osmolytes to maintain the internal/external osmolarity balance. Nevertheless, reactions of FW organisms to salinity stress vary from remaining in diapause (cysts or eggs), moving locally to shallower areas or even migration to new areas with lower conductivity (James et al., 2003; Karraker, 2007; Argüelles et al., 2013).

Osmostress can produce significant impacts on cell wall stability, modulation of gene transcription, cell physiology, cytoskeleton structure and even cell the cycle. Thus one specific

6 reaction to osmostress is to supply intracellular osmolytes to maintain the balance between internal/external osmolarity (Lignot et al., 2000).

Crustaceans as a group are well-known for their successful colonisation of freshwater from the marine environment and this evolutionary trend is associated with a wide diversity in osmoregulatory capacity across this group (Freire et al., 2008). In terms of farmed aquatic species, around the world, approximately 9.6 % are crustacean species and FW crustaceans contribute 29.4

% of all farmed crustacean taxa, so, they contribute an important aquatic resource (FAO, 2012).

Many decapods can utilise a wide range of salinity levels (freshwater, brackish and marine conditions), with some species able to move among aquatic salinity gradients within their own life times (Gillanders et al., 2003). Anatomical changes in crustaceans (development of osmoregulatory epithelial tissue) lead to physiological changes (via increases in Na+/K+-ATPase activity, the major enzymes involved in ion transportation in crustaceans) resulting in improved osmoregulatory capacity with age that produces salinity tolerance and hence adaptation to new osmotic environments that vary in mean salinity level (Williams and Sherwood, 1994). At least in teleosts, osmoregulatory performance and growth rate are closely linked to each other as both demand high levels of energy that are related to associations between genetic, physiological and environmental factors. The osmoregulation system commonly accounts for around 25-50% of metabolic production (Streelman and Kocher, 2002). Low rates of growth in Mytilus edulis in the

Baltic Sea has been shown to result from costly osmoregulation when salinity levels vary (Höher et al., 2013).

Determining osmoregulatory capacity (OC) has been proposed as an appropriate and reliable way for investigating physiological conditions and impacts of stressors on crustacean taxa (Lignot et al. 2000; Romano and Zeng 2012). Each crustacean species’ relative ability to regulate hypo- or

7 hyperosmotic haemolymph is linked to variation they experience in their external environmental salinity and behavioural responses that expose them to fluctuating salinities. With respect to ion balance in their haemolymph therefore, crustaceans act either as osmoconformers or osmoregulators (Rainbow and Black, 2001).

1.2.1 Osmoconformers:

Certain taxa acclimate to, and can tolerate, a wide range of salinities with their haemolymph remaining nearly isosmotic with the external media. In fact they do not normally invest energy in ionic transport mechanisms and they are unable to maintain significant osmotic gradients between their internal body fluid and the external media (surrounding water) (Freire et al., 2008).

Isosmocity between internal and external media with tolerance to a wide range of salinity levels is found widely in many invertebrate marine species in general, and many crustacean taxa, in particular. Species in this group are referred to as osmoconformers (Lockwood, 1962). For isosmotic crustaceans, ion regulation and OC are not very important since OC is close to zero and they will die if they are exposed to lower salinity levels than that to which they are acclimated.

Because of the similarity in intracellular osmotic pressure in osmoconformers individuals with their blood, osmoregulation is a low cost energy process. During salinity reduction, key ions are excreted (mainly Na+, K+, Cl-) to maintain osmolarity regulation that requires elevation of ATP demand (Somero, 2002). Under salinity stress, levels of allocated energy for other processes such as growth and reproductive behavior will be reduced (Sokolova, 2013).

Osmoregulatory changes occur only marginally during their developmental stages, which mean that no significant change in osmoregulation is required for metamorphosis. Adults are usually either poor osmoregulators or are also osmoconformers. True osmoconforming organisms cannot

8 tolerate full fresh water because tissues are too thin to allow normal metabolic processes (their body fluid ionic balance changes in parallel with external ion concentration changes) (Dall, 1967).

For freshwater osmoconformer species, most of the time there is a gradient between their internal osmotic concentration and that of the external environment; consequently, osmoconformers will vary in their cellular osmotic volume contents and as a result, some FW species (e.g. Daphnia), osmoregulate at low salinity levels but are osmoconformers at raised salinities. Some physiological processes, including reproduction and metabolic rate however, can be affected negatively by these changes (this group regulate their osmotic pressure actively) (Chen and Stillman, 2012).

Results of several studies show that, there is a rapid, two way flux of Na+ across the body surface in marine crustaceans (Dall, 1967). Additionally, when they are exposed to low salinity conditions, production of Na+/K+-ATPase can be a costly strategy to achieve osmotic acclimation, and as a result this response can have negative impacts on growth, oxygen consumption and/or immune and excretory functions (Joseph and Philip, 2007). Among marine animals, teleosts (bony fishes) have the lowest osmotic concentration in their bodies (they excrete excess salt via their gills). In some marine crab species, stenohaline individuals cannot tolerate a substantial reduction in environmental salinity, while other marine crabs are euryhaline osmoconformers and can tolerate significant changes to external salinity levels (Bradley, 2009). Nevertheless, salinity acts as an abiotic factor and can modify ion toxicity by increasing protection against ions and change physiology (Fiol et al., 2006). For example, in some estuarine teleosts such as killifish, salinity is a protective factor against tissue injuring oxidative stress by zinc (Loro et al., 2012).

Even in the euryhaline teleost Fundulus heteroditus, salinity is not a challenging factor as this species can acclimate to very high salinity levels (Lushchak, 2011). In these individuals, high salinity levels lead to an increase in the concentrations of significant competitive ions (Na+, K+,

Ca2+, Mg2+). Among these, Ca directly plays a role in zinc uptake in particular, in gill tissue. So salinity as a significant abiotic factor acts against metal toxicity in estuarine conditions (Grosell et al., 2007). Salinity can also protect against copper (Cu) accumulation in gill and liver tissues (Cu accumulation is highest under FW conditions in this species) (Klos et al., 2015). Sub-lethal concentrations of some metal ions like Cu can lead to a change in Na+ up by osmoregulatory organs

(gills) and alter their permeability under oxidative stress. These impacts have been reported in investigations of zebrafish (Danio rerio), killifish (Fundulus heteroclitus) and marine gulf toadfish

(Opsanus beta) (Craig et al., 2007, Grosell et al., 2004). High levels of Cu in marine species as a result of changing water chemistry can cause damage to DNA structure (Costa et al., 2002) or alter biomarker functions such as increasing lipid peroxidation and amount of protein carbonyls in gill tissues, the major site for osmoregulation. These results have been reported in a large number of fish species, and in Indian flying crab (Esomus danricus) (Craig et al., 2007; Eyckmans et al.,

2011; Vutukuru et al., 2006). A positive protective role for salinity stress against oxidative stress caused by Ni has been documented in the euryhaline fish- inanga (Galaxias maculatus) that produced less oxidative stress in individuals acclimated to high salinity levels (Blewett et al.,

2016).

1.2.2 Osmoregulators:

Among osmoregulating aquatic taxa, depending on the ion concentration of the surrounding media, two classes of response have been identified; hypo-osmoregulation and hyper-osmoregulation

(Lockwood, 1962). In hyper-osmotic environments (where external osmolality is higher than the haemolymph), individuals must compensate for ion influx via hypo-osmoregulation. When faced with hypo-osmotic environments, they must compensate for ion loss from their haemolymph via hyper-osmoregulation.

All FW species are osmoregulators and this trait affects their long term survival or resilience and also affects fitness parameters (Piscart et al., 2006). Certain species show different osmoregulatory capacity associated with their life cycle stage. For example, the freshwater prawn (Macrobrachium rosenbergii) is found in brackish water as a larvae and then osmoregulates in freshwater environments as an adult (Funge-Smith et al., 1995).

For hypo-osmoregulators, internal osmotic concentration is lower than the external medium because cells contain low levels of ions so individuals have a tendency to lose water to their environment. Greater plasticity of response to salinity in hyposmotic environments has been documented compared with hyperosmotic conditions in salt water vs FW species.

The most well-known hypo-osmoregulators are certain marine shrimp taxa that survive in the face of extreme osmotic gradients. In marine shrimps, the internal osmotic pressure of haemolymph can vary from 200 to 400 mOsm (the average osmotic pressure in FW organisms and terrestrial forms is 300 mOsm) and to compensate for loss of body volume, they drink water from the external environment. Osmoregulatory mechanisms present in some shrimp taxa allow them to regulate their internal osmotic content by modifying their body surface permeability and also by altering urine production, hence affecting ion transport. Hypo-osmoregulating crustacean species compensate for passive NaCl loss to dilute, external media by active NaCl absorption across their gill filaments (Kaeodee et al., 2011). As occurs in the nauplii of Artemia salina, hyper- osmoregulation of Na+ in the haemolymph appears to be related to active Na+ secretion associated with increasing Na+/K+- ATPase activity (Charmantier, 1998).

Investigations of osmoregulatory capacity in five different species of FW prawn (Family

Palaemonidae) have determined that taxa that can tolerate marine and brackish water are well-

11 adapted to both hyper- and hypo-osmotic environments (Freire et al., 2003). Palaemonid shrimps in saline habitats in general, show the highest OC, thus they have been very successful at invading freshwater while this capacity has been lost in FW species faced with variation in environmental salinities. Two species in this family including Palaemonetes varians (live in very dilute brackish water) and Palaemon longirostris (from fresh water) have been studied in detail for their osmoregulatory capacity. P. longirostris returns to salt water to breed but its osmotic concentration remains in the same range as it was in fresh water and this ability is associated with levels of

Na+/K+- ATPase activity and haemolymph concentrations of Na+ and Cl- as the main haemolymph osmolytes (Charmantier 1998).

Hyper-osmoregulators maintain their blood hyperosmotic in dilute media, while they are isosmotic in concentrated media (Rainbow and Black, 2001). Regardless of variation in FW habitat for size, mean temperature or speed of water flow, their osmotic concentration is similar. Many estuarine and coastal crustacean species tend to belong to this group. When they are exposed to dilute sea water (SW), their responses can vary, but they have only limited capacity for maintaining their haemolymph hyper-osmotic in low salinity concentration (dilute) environments. In these organisms, no significant change occurs in internal body fluid’s ion concentration regardless of variation in external salinity. At very low salinity levels however, they show poorer survival than do isosmotic forms, notably patterns of osmoregulation in adults are the same as for first post embryonic stages (Lockwood, 1962).

FW organisms with hyper-osmoregulatory capacity are faced with two physiological challenges.

The first challenge is dealing with water influx via the body surfaces (i.e. gill, skin, body wall).

The second challenge is coping with low ion concentration in FW; (i.e. a poor external source of

Na+, K+, Ca2+ and Cl- ions that are required by all organisms). In FW conditions, physiological

12 mechanisms including hyper-osmoregulation are vital to meet their needs (Bradley, 2009).

Because of lower osmotic concentration in FW organisms, they are often poorly adapted to exposure to high levels of salinity (higher than 25‰). At salinity levels above 25 ‰, their osmoregulatory mechanisms tend to collapse as a result of cellular damage. As a consequence, optimum performance of FW organisms will be at intermediate salinity levels where their energy consumption for osmoregulation will be at the lowest level (Dall, 1967).

The endemic Australian freshwater prawn, Macrobrachium australiense is a good example of a hyper-osmoregulator and when exposed to environments with increased osmotic stress, this species has a mechanism to reduce Na+ loss from the body by producing hypo-osmotic urine

(Denne, 1968). Furthermore, when individuals experience changes in environmental ionic concentration, specific histological changes occur, including to the gill epithelia that contribute to maintenance of their osmotic pressure balance (Bradley, 2009). Weak hyper-regulators are those that can survive in dilute SW but cannot cope with FW for any significant period of time. This includes most crab and shrimp species (Kaeodee et al., 2011). For instance, the gut in some crabs can condense water when the organism is hyper-osmotic (Bradley, 2009).

In euryhaline aquatic species such as the tiger shrimp (Penaeus monodon) and the European green crab (Carcinus maenas), both are strong osmoregulators and haemolymph osmolality is higher than the surrounding environment (Wilder et al., 2009). In C. maenas, some changes occur at the cellular level in muscle fibres (increasing water content) in response to salinity stress, at the same time they lose ions like Cl-, during acclimation to different salinity levels (Shaw, 1958). In this organism, K+ concentration changes as does Cl-, with both ions believed to be in electrochemical equilibrium but permeability of the muscle fibre membrane is considerable for Na+. High divergence of Na+ concentration from expected equilibrium levels against the electrochemical 13 gradient shows that Na+ must be involved in an active process of osmoregulation. The role of K+ appears to be to maintain the potential of the cell membrane against variation between the haemolymph and the environment. This will cause changes to the metabolism of Na+ in muscle cells, while the influx rate of Na+ into muscle fibres is associated with the gradient between the internal body fluid and cells (Lockwood, 1962).

In crustacean taxa, two processes are responsible for controlling these ion concentrations, the compensatory process and the limiting process:

1.2.3 Compensatory process

To maintain their osmotic balance, ions need to be moved actively into or out of the haemolymph.

The main enzyme responsible for ion transportation in this process is Na+/K+-ATPase, an enzyme that is hyper-expressed in the branchial chambers (gill). This is the primary organ used for osmotic and ionic regulation in all crustaceans (Lignot et al., 2000).

1.2.4 Limiting process

Reducing penetrance of the gill membrane is the best way to lower ion movement and water flow without relying on active transport that requires energy. In fact, gill permeability in crustaceans provides the ability to tolerate a wide range of ionic concentrations in aquatic environments

(Rainbow and Black, 2001). For effective osmoregulation two factors are important; the concentration gradient between the haemolymph and the external environment and the relative penetrance of the body surface, although FW, estuarine and semi-terrestrial species tend to limit their body permeability (Romano and Zeng, 2012). Body surface penetrance in FW organisms is in general very limited while it is at the highest levels in SW adapted forms (Lockwood, 1962).

Diet can also act as a factor that can contribute to OC under stress from changed osmotic conditions and this has been recognised in farmed crustaceans where diet can be manipulated to improve individual productivity. Osmoregulatory compensation using a supply of energy provided from protein, lipids and phospholipids in a diet can reduce gill penetrance by increasing membrane solidity. Additionally, Vitamin E can improve lipid stability in the gill structure by increasing

Na+/K+-ATPase activity. Some recent studies have also shown that a high concentration of Na+/K+ and Mg2+/Ca2+ ions can cause a decrease in the performance of gill Na+/K+-ATPase and as a result, individuals may be exposed to low K+ concentration that can lead to mortality (Romano and Zeng,

2012).

1.3 Osmoregulation capacity (OC) during development (Ontogeny): The requirement for regular moulting to allow normal growth is a key characteristic of all crustacean taxa. In decapod crustaceans, the moulting cycle is divided into four major stages (some with sub-stages) that include:

1. Metecdysis (post-moult) (stage A, B): the period immediately following ecdysis (where rapid calcification of the exoskeleton occurs).

2. Anecdysis (inter-moult) (stage C): the period of tissue growth and accumulation of food reserves.

3. Proecdysis (pre-moult) (stage D): the period of active morphological and physiological changes in preparation for the next moult.

4. Ecdysis (stage E): the shedding of the old cuticle (Chan et al., 1988).

Changes in osmoregulatory capacity during development occur via anatomical changes

(development of osmoregulatory epithelia) leading to physiological modification (increased

Na+/K+-ATPase activity, associated with an increase in OC) that improve salinity tolerance, allowing acclimation to a wide range of environmental salinities (Conte et al., 1972).

Developmental studies of crustaceans that have focused on osmoregulation suggest that larvae and post-larvae (PL) in general, show an increase in osmolality over time (Freire et al., 2003; Read,

1986).

During post-embryonic stages, ion-transporting epithelia are present and located in sites that vary based on both species and specific developmental stage. For example, osmoregulatory sites in

Artemia salina nauplii include the neck, dorsal organ and salt gland, that contain numerous transport cells (Conte et al., 1972). It appears that the inner surfaces of the branchial chambers in larval decapods also play a role in osmoregulation before this function is later shifted to the functional gills and epipodites. The role of the gut epithelium and excretory organs in salt regulation are very limited in post-embryonic stages while the role of the antennal gland in ion regulation is significant in the control of urine composition, during early pre-embryonic stages.

(Charmantier, 1998).

In coastal and estuarine environments, crustacean larvae face spatial variability in salinity and can experience osmotic stress that may reduce growth and affect their survival (as discussed previously). This effect will vary however, depending on physiological adaptations including capacity for osmoregulation during early developmental life-history stages. In species where the larvae are osmoconformers until metamorphosis (e.g. Libinia marginata), only a limited range of salinity variation can be tolerated (Charmantier, 1998). Osmoregulating species, with larvae that show the adult type of osmoregulatory capacity from the beginning of their larval development

(e.g. Macrobrachium petersi), occur in freshwater habitats or in environments with variable or even extreme salinities; they possess a wide range of salinity tolerances (Charmantier, 1998). A different pattern is found in some other species including P. japonicus, and H. gammarus

(Charmantier et al., 2001), where the larvae are osmoconformers while the adults are osmoregulators.

Haemolymph osmolality in crustaceans in general, increases from larval through to the PL stages.

During the moult cycle, individuals tend to be hyper-osmoregulators in the hours or the day before ecdysis and are nearly isomotic at the post-moult stage. Such variation in osmotic concentration has been reported in a variety of species including: Rhithropanopeus harrisii, Cardisoma guanhumi, Homarus americanus and Cancer irroratus. As an example, during the post embryonic stage, in a large number of Cladoceran species, individuals are hyper-osmoregulators (freshwater animals) or hypo-osmoregulators (those living in sea water) or there is an ability to be both hyper- osmoregulators and hypo-hyper osmoregulators in nauplii of Artemia salina, when they mature to the adult stage. In a number of species, osmoregulatory structures are temporary during development, including dorsal or neck organs and will be replaced by contemporary organs

(usually gills) during later development. Significance of this temporary ability for hypo-hyper osmoregulation is unknown, although it may be related to the appearance of the gills in juveniles and adults and degeneration of the dorsal organ. This variation in osmoregulatory ability during developmental stages appears to be one of the key factors that permits successful adaptation of species to their habitats, in particular for post-embryonic development under salinity variation

(Charmantier, 1998). M. rosenbergii or (giant freshwater prawn (GFP)) is a widely cultured FW prawn species that has been studied for preferred salinity and temperature conditions to optimise growth rate (Agard, 1999). GFP have the ability to both hypo-hyperosmoregulate at the PL stage

17 of the life cycle but this ability declines as individuals pass through later developmental stages and adults are only capable of hyper-osmoregulating (Sandifer et al., 1975). At raised salinity levels

(above the isosmotic point), individuals are exposed to stress, and OC reduces significantly, resulting in an increase in haemolymph concentration. In fact in all osmoregulatory studies of GFP, osmolality and Ca+2 concentration were maintained even at the highest salinities (Wilder et al.,

1998). In GFP, maximum osmotic concentration is associated with stage D and minimum levels in stage E of moulting. Changing intracellular free amino acid concentration (FAA; involved in mediating response to salinity exposure in FW prawns) (Huong et al., 2001), provides primary facilitation for keeping osmotic equivalence between cells and the haemolymph in both early and post-embryonic stages and adults. As an example in M. rosenbergii, three FAAs (glycine, proline, alanine) levels in muscle tissues have been shown to be associated with salinity changes

(Charmantier and Charmantier-Daures, 2001). Fluctuations in Na+, Cl-, K+, Mg2+ and Ca2+ levels in this organism during the moulting cycle were also observed. Overall, Na+ and Cl- concentrations in M. rosenbergii increased with the progression of the moulting cycle (especially between stages

C and D) and this rising trend affects relative water absorption between moulting stages. Similar patterns have also been observed in some other crustacean taxa. For example, in C. maenas, Na+,

Cl- and K+ concentration and osmotic concentration rise significantly during the pre-moult stage and in L. stylirostris, osmotic concentration reached maximum levels during the inter-moult and then reduced during the pre-moult stage (Wilder et al., 2009).

Because of the need for raised salinity (~15‰) required for production of GFP larvae in hatcheries, this can be a significant obstacle to development and growth of local culture industries unless a source of sea water is available locally. Consequently, there is wide current interest in identifying physiologically important candidate genes that influence salinity tolerance in this farmed

18 freshwater prawn and applying these data to optimize hatchery practices to reduce reliance on sea water to complete the larval lifecycle in the hatchery effectively. Thus, a focus on understanding salinity tolerance and natural adaptation of wild FW prawns to different salinity conditions is linked to predictions of future climate change that reflect increasing problems with rising salinity levels in many major rivers and streams where GFP and other Macrobrachium spp. occur naturally.

1.4 The role of pivotal organs and identified genes in osmoregulation The most important organs for osmoregulation in crustaceans include:

Gills - specialised organs for osmoregulation, gas exchange, maintenance of acid-base balance and

N2 extraction (osmoregulation occurs in the posterior gill sections) (Cieluch et al., 2004). The most important ion transport functions occur in gill epithelial cells that possess a specific ultrastructure featuring apical microvilli and basolateral infolds that host large numbers of mitochondria. From a histological point of view, the gill thinness is similar to other external parts of the body but they possess a highly penetrable layer that plays a major absorptive, rather than excretory role (Dall,

1967).

Antennal gland - the main role of this organ in osmoregulation is to produce urine (considered as the main excretory organ). The antennal gland is also important for reabsorption of ions by active transport or passively into water (Dall, 1967).

Hepatopancreas - is the major digestive gland and is a sensitive indicator of metabolism, ecdysis

(moulting) and feeding status. It also plays a role in acclimation to environmental stress of both low and raised salinities (Li et al., 2008). In general, the hepatopancreas is considered the master organ for the majority of body functions. 19

All of these organs provide ideal targets for a comparative genomic analysis of the molecular basis of adaptive physiological response to variation in salinity. An analysis of variation in gene expression in the target tissues in a model FW prawn, M. australiense exposed to different salinity concentrations within the natural tolerance range of the species can be used to understand adaptive mechanisms for dealing with salinity stress. This can help to generate DNA sequence information on Expressed Sequence Tags (ESTs), in particular those that are likely play roles in regulatory pathways during salt water acclimation. EST analysis is now a routine genomic method for rapid identification of expressed genes and regulation of specific genes in different tissues, developmental stages and under different environmental conditions. A subset of forward subtracted transcripts can also be down/up-regulated and examined via quantitative real-time PCR

(RT-qPCR) to characterise differential expression patterns in selected tissues after experimental exposure to different salinity conditions.

Crustaceans in general, show higher levels of differential gene expression patterns in comparison with teleosts, because in general, they act as osmoconformers in high salinity environments and as osmoregulators in low salinities, while teleosts are strong osmoregulators across a very wide range of salinities. A major enzyme responsible for ion regulation in crustaceans is Na+/K+-ATPase that shows greatest activity in the posterior gills (this enzyme has different isoforms expressed in the anterior and posterior gills). For instance in Carcinus maenas, C. sapidus and Uca vocans, changes in Na+/K+-ATPase activity in the gills are higher than in the antennal gland or gut. In the crab

Scylla sp., Na+/K+-ATPase activity and rate of mRNA production after transfer from high (45‰) to low salinity levels (5‰), increased significantly. Rising mRNA concentration in the posterior gills in dilute media in turn leads to more Na+/K+-ATPase activity and this appears to result from increased gene transcription and/or mRNA translation, while mRNA expression in concentrated

20 media does not result in any change in Na+/K+-ATPase activity levels. Differences in gene expression patterns in the genus Macrobrachium therefore is directly associated with environmental salinity levels, tissue type and the period of exposure of the experimental prawns submitted to osmotic stress (Havird et al., 2013). Table 1-1 summarises this and other genes previously identified as contributing to osmoregulatory capacity in aquatic organisms, in particularly, in crustaceans.

Table 1-1: Specific genes previously identified as having a functional role in osmoregulation and salinity tolerance in a range of aquatic species.

GENE TISSUE FUNCTION ORGANISMS REFERENCE

USP gill Involved in salinity Macrobrachium (Barman et al., 2012) tolerance rosenbergii (Zolk et al., 2006) Antennal gland

Calreticulin gill Involved in signal M. rosenbergii (Barman et al., 2012) transduction in many biological processes of (Luana et al. ,2007) growth and moulting

Na/K- ATPase (NKA) Gill creating electrochemical Euryhaline animals (Havird et al., 2013) gradient across the cell membrane in the gills of crustaceans for active (Boudour-Boucheker et absorbtion or release ions al., 2013) M. amazonicum

Na+/K+/2Cl- cotransporter Gill Transport ions into gill Killifish (Scott et al., 2005) (NKCC) cells either from the blood or media based on salinity.

H+-ATPase (HAT) Gill Pumps protons to Euryhaline animals (Havird et al., 2013) influence HCO3- and Cl- exchange and Na uptake.

Carbonic anhydrase (CA) Gill Involved in in all main M. rosenbergii (Jasmani et al., 2008) gill functions such as ion regulation

V-H ATPase Gill, hepatopancreas Role in salinity M. amazonicum (Faleiros et al., 2010) acclimation

LvCHH-ITP Eye stalk Protection of ion loss to Litopenaeus vannamei (Tiu et al., 2007) the environment though acclimation to low salinity

Catechol-o-methyl gill, gut, muscle, high associated with low and Penaeus monodon (Rajesh et al., 2012) transferase (COMT) expression in high salinity and crucial hepatopancreas role in salinity stress Role in regulating ion ATP-binding cassette gill, hepatopancreas channels in the plasma M. rosenbergii (Barman et al., 2012) (ABC) membrane

HSP70 Gill Role in salt stress M. rosenbergii ,brown (Barman et al., 2012) trout ,rainbow trout

ABC protein C12 gill Functional role in M. rosenbergii (Barman et al., 2012) (ABCC 12) osmoregulation under osmotic stresses

Acyl-CoA binding gill, gut, muscle Role in salinity tolerance Penaeus monodon (Kiruthika et al., 2013) protein (ACBP) and adaptation at low and high salinity levels

ILF2 (interlukin enhancer hepatopancreas, gill a transcriptional M. rosenbergii (Barman et al., 2012) binding factor 2) regulator against biotic stresses

OST complex gill Maintaining homeostasis M. rosenbergii (Barman et al., 2012) during salinity/osmotic stress

Selenophosphate (SPS1) gill Involved in salinity M. rosenbergii (Barman et al., 2012) tolerance (Lobanov et al., 2008)

1.5 Candidate gene approach (Transcriptomics) to identify key genes involved in osmoregulation Crustacean taxa including Macrobrachium species contribute significantly to the global aquaculture industry and this has encouraged development of programs to improve stock and culture performance (Jung et al., 2013). A lack of comprehensive data on the genomes of many aquatic species and limited knowledge about molecular and biochemical factors that support

22 important quantitative traits like growth, survival and other economically significant traits however, has restricted endeavours to increase the output of crustacean species from culture.

Among crustaceans, certain species that possess specific quantitative traits including the ability to survive variable environmental conditions or more specifically, that possess some physiological adaptations that provide efficient osmoregulation can be used as models organisms for investigating the physiology and genetic control of salinity tolerance and osmoregulatory adaptation more widely in related species (Faria et al., 2011). Genomic approaches are increasingly being applied to develop a better understanding of genetic factors that affect variation in economically important traits in many cultured species (Jung et al., 2011) and provide efficient and relatively low cost methods for such studies. Increasingly, genomic techniques are being applied to a large number of model and non-model organisms to determine the role of functional genes and specific mutations that contribute to important production traits in both agriculture and aquaculture. As an example, transcriptomic studies are now beginning to shed light on cellular and molecular regulatory mechanisms of salinity tolerance (Morrell et al., 2012). There is a huge potential of the existing modern genomic tools to explore many different ecological and evolutionary mysteries. We can use them to investigate how changes in environmental salinity levels can impose serious stress on crustaceans that can ultimately hamper growth and negatively affect commercial production. It would therefore, be a great interest to identify and characterize genes that allow efficient osmoregulation for successful ionic balance due to salinity change.

1.6 Macrobrachium australiense as a model for salinity tolerance in FW prawns. The genus Macrobrachium is a member of the Palaemonidae family that has dispersed widely across tropical and sub-tropical zones around the world. This genus currently constitutes over 250 species globally with 43 species being commercially important (Jung et al., 2011). Macrobrachium species are found in all water types across the planet except for Antarctica (Ye et al., 2009). M. australiense is an endemic Australian species, widely distributed across the continent, with populations in both inland and coastal river systems (Short, 2004). Macrobrachium australiense

(Palaemonidae) was used here as a model for other freshwater prawns in the genus Macrobrachium because it is the most widespread FW prawns species across mainland Australia and is the most successfully adapted species to completing its entire life cycle in freshwater (Dimmock et al.,

2004), yet under controlled conditions in the laboratory can still tolerate up to 17 ‰ salinity

(Sharma and Hughes, 2009). The most important features of this model species include high tolerance of saline conditions at all life history stages, short generation time (life cycle is abbreviated into three zoeal stages, maturation normally takes 4-6 months after hatching), reduced larval stage, mature stage at hatching and relatively large egg size (Dimmock et al., 2004).

M. rosenbergii (GFP) currently is the most important FW prawn species used in aquaculture.

Production levels of this prawn have increased rapidly over recent decades and now many people depend on this culture industry for their livelihoods and food security. Female M. rosenbergii migrate naturally from FW to brackish water after spawning and larvae must be released into brackish water where they remain until the post-larval stage, then juveniles return to FW to complete their lifecycle. M. rosenbergii is an osmoregulator (at salinities between 0-15‰) but acts as an osmoconformer at higher salinities (> 15‰) (Huong et al., 2010). Studies have shown that

24 rates of protein synthesis and respiratory metabolism are affected significantly by changes in salinity in this organism. Individuals are very sensitive to changes in salinity, pH and temperature during the premoult stages of their life cycle and this can have significant effects on their immune responses (Cheng and Chen, 2000). At raised salinity levels, mortality rate is considerable and growth performance is compromised, while successful reproduction depends on availability of an appropriate low salinity environment. Only under very low salinity conditions will juveniles show optimum growth performance (Stephenson and Knight, 1982). M. nipponense is another FW prawn species that shows maximum growth performance at low salinities (Wang et al., 2004).

Efficient osmoregulatory capacity is a fundamental requirement for coping with environmental stressors and it will be important to identify physiologically significant candidate genes (CG) that contribute to effective osmoregulation in these organisms in the face of rapid environmental change. Understanding the genomic and/or functional genomic resources associated with effective salinity tolerance in crustaceans is a basic requirement for survival under different environmental conditions. Next generation sequencing technologies (NGST) in parallel with differential gene expression (DGE) profiling are cutting-edge methodologies that allow deep understanding of gene expression patterns and signaling pathway to be developed. Transcriptomic analysis has been used widely in biological studies and has provided practical insights into gene discovery, differential gene expression patterns under different environmental conditions and for molecular marker discovery (Jin et al., 2013; Li et al., 2015; Manfrin et al., 2015; Song et al., 2015; Thanh et al.,

2014; Zhao et al., 2015).

25 in particular Macrobrachium prawns, certain organs/tissues provide ideal targets for a comparative genomic analysis of the molecular basis of adaptive physiological response to saline environments.

Therefore, the primary aim of this study was to develop a more complete understanding of the molecular basis of osmoregulatory function by sequencing transcriptomes from the most important organs involved in ion regulation and salinity tolerance using an Illumina platform. Transcriptomes could then be used to identify the major candidate genes with osmoregulatory roles in osmoregulatory organs including the gill, antennal gland and hepatopancreas.

1.7 Next Generation Sequencing (NGS)

NGS is a new technology for identifying genes and functional mutations in both model and non- model species (Ekblom and Galindo, 2011) and can generate millions of sequence reads concurrently with standard dye-terminator methods. Availability of large genomic datasets generated with NGST can increase our understanding of factors that have influenced the evolution of specific taxonomic groups and has important applications for improving the quality of stock used in aquaculture (Morrell et al., 2012). NGS approaches provide massive amounts of genome wide sequence data that can be used for better understanding various biological processes. This approach can also provide a substantial opportunity to identify the molecular basis of phenotypic variation in living organisms (Jung et al., 2013). Already these technologies have made significant contributions to productivity in aquaculture species for example in catfish, Atlantic salmon and rainbow trout (Kocher et al., 1998; Kartavtsev et al., 2007; Davidson et al., 2010; Salem et al.,

2010). A main focus of this approach is to identify and characterise genes and molecular markers that contribute to variation in important Quantitative Trait Loci (QTLs). Such markers can potentially be applied in Marker Assisted Selection (MAS) programs to increase the rate of stock

26 improvement and to increase genetic gains in stock improvement programs for cultured species.

ESTs screened from the library encode functional proteins that are involved in cellular metabolic processes, signal transduction and biological regulation and others vital steps. Information generated from the current study is expected to provide new insights into salinity–mediated stress tolerance mechanisms in M. australiense and to functionally validate these ESTs in vivo and to determine their physiological significance in response to salinity tolerance.

1.8 Single Nucleotide Polymorphism (SNP) identification

Exposure to new osmotic conditions by migrants requires rapid adaptation to permit persistence in the new environment. A common strategy during adaptation is that genetic plasticity can lead to adaptation by selection (Lee et al., 2011). This phenomenon is considered by many to be an

27 interesting problem and one where evolutionary biologists seek to understand changes at the genomic level during local adaptation (Butlin, 2010). Of the diverse array of extant animal taxa, only a few phylogenetic groups have been able to adapt successfully to low osmotic conditions

(inland freshwaters) following a marine ancestry. There are many crustacean species that provide ideal candidate species for understanding the mechanisms underlying osmoregulatory capacity in variable osmotic gradients.

Population genomics approaches are now widely available for investigating the molecular basis of the adaptive process where populations are experiencing different natural environmental conditions that show a clear footprint of natural selection on genetic variation (Kohn et al., 2006).

Such approaches are useful for understanding the process of speciation where markers derived from transcriptomic data can identify candidate genes and functional mutations in key genes under selection (Berdan et al., 2015). Modern genomic approaches (particularly NGS methodologies) have made it relatively easy to either acquire a list of candidate polymorphic loci, or to search for

SNPs and/or outliers (loci under selection or adaptive loci) among a set of candidate genes in contrast to using a blind shotgun approach to identify > 1000ʹ s of SNPs from massive sequence dataset. SNP calling from transcriptomic data (using expressed sequences) provides a cost effective way for reducing genomic complexity while still retaining a significant fraction of the functionally relevant information (De Wit et al., 2015). An important additional outcome from this type of data is the potential to identify outlier loci from expressed sequences. Another powerful feature of a transcriptomic data set is the potential to examine changes in gene expression patterns among natural populations or different individuals in a controlled experimental set up.

Furthermore, transcriptomic approaches can focus on genetic variation in specific candidate genes

28 that potentially may be under selection and therefore can be useful for investigating the molecular basis of local adaptation in wild populations (Helyar et al., 2012).

A number of studies have confirmed that there is a genetic basis to differences in relative transcript abundance (Gracey et al., 2004), that can lead to adaptive divergence in wild populations

(Eguavoen et al., 2015; Leder et al., 2015). Thus, combining gene expression pattern data

(transcript abundance) and SNP detection from the same set of individuals from transcriptomic data set can provide a novel way of examining the functional roles of population specific SNPs in controlling gene expression patterns.

For instance, M. australiense wild populations are exposed naturally to different osmotic gradients that provide a genomic resource for identifying and characterising important osmoregulatory genes and functional mutations in these genes that may have diverged during adaptation to inland freshwaters. Such data can be used to investigate whether this process is controlled largely by mutations in regulatory sequences or mutations in functional protein coding regions.

1.9 Differential gene expression (DGE) patterns and morphological changes under osmotic challenge

Changes in gene expression levels provide an important mechanism for stress response in living organisms but currently very little information is available about differential gene expression related to salinity tolerance in freshwater prawns. Recognition of key genes and key mutations in one target species that are related to phenotypic regulation of important adaptive responses are likely to have similar functions in closely related taxa due to conservation over evolutionary time, thus providing a useful tool for identifying important candidate genes in a broad range of taxa. In

29 eukaryote species, genes possess both coding and non-coding areas. During acclimation to osmotic stress, significant changes may occur in the main factors associated with osmoregulation including, change to protein structure, change in permeability and function of gill epithelium cells, alteration of expression patterns of the most important ion transporter genes (Evans, 2002; Seeb et al., 2011).

Recently gene expression studies via new and developed technologies and platforms have uncovered many quantitative traits in many taxa.

Changes in gene expression patterns at the cellular levels via activation of intracellular signaling pathways have been considered to be a major component of stress responses that will be vital for long-term adaptation. Recently high quality results from transcriptomic scanning and mRNA stability studies have uncovered the molecular basis of gene expression regulation in response to stressors. They used genomic technologies to focus on genome content and searching in transcriptomes followed by sequencing studies that covered the influences of both non-stressed and other abiotic stressors on cell physiology (Cochrane et al., 2009). The primary ability of organisms to deal with changing environments is a complement of protein-coding regions across the genome with factors that regulate gene expression to repair damage and produce better functional phenotypes (Cochrane et al., 2009).

Comparative studies can help to identify the genes and mutations with common transcriptional responses during acclimation to salinity stress. In fact, genome-wide sequencing can be used to identify potential candidate genes involved in adaptation to divergent osmotic environments and can determine consistent genomic changes that occur in related species across micro/macro evolutionary time scales. Under different salinity conditions, mRNAs may produce different gene products, because of this, we can target just a single specific gene using RT-PCR to describe relative expression patterns of target genes under variable salinity conditions. During salinity

30 stress, high amounts of energy are required to increase metabolic rates that lead to more ATP production for active transport within epithelial cells. These findings provide a framework for using Macrobrachium australiense as a model to study gene expression changes with regards to salinity stress, the potential outcomes will be to understand this process in crustaceans more widely.

1.10 Knowledge gaps and research questions

The culture industry for FW prawns is now confronted with a serious challenge; saltwater penetration into many freshwater ecosystems as a result of impacts of climate change. To respond proactively to this problem, we need to develop culture stock with better tolerance of raised saline environments. Understanding the genomic basis of salinity tolerance and performance of the osmoregulatory system can help address this problem. Macrobrachium species naturally occupy a wide range of environments (e.g. salinity) and show a variety of behavioural strategies to deal with changing environments. The capacity to resist significant changes in environmental salinities is believed to depend on specific osmoregulatory mechanisms that are important in their natural life cycle and in farming. A number of studies have shown specific key genes and mutations can play pivotal roles in osmoregulation and sustainability in crustacean taxa (Liu and Cordes, 2004). In the current study, M. australiense was used as a genetic model to address the following research questions:

1. Which candidate genes are involved in osmoregulation in M. australiense?

2. Which key mutations (functional and/or regulatory) are associated with adaptation to variable osmotic niches in the wild populations?

3. How do changes in CG expression patterns influence salinity tolerance in the FW model, M. australiense?

Comparative analysis of salinity tolerance in the model species with different natural tolerance to salinity will be conducted to identify common candidate genes and mutations that affect osmoregulatory performance under salinity stress in different populations.

1.11 Objectives and aims

In this Project, transcriptomic analyses were used to identify the major candidate genes and functional mutations involved with osmoregulation in M. australiense. Three specific aims of this project include:

1. Transcriptomic scan of M. australiense using NGS approach for identifying key CG

involved with osmoregulation and ion regulation.

2. Identifying outlier loci (adaptive loci) of M. australiense collected from different wild

populations that are exposed to different osmotic niches, using a comparative population

transcriptomic approach.

3. Investigating changes in expression patterns of osmoregulatory genes exposed to different

experimental salinity levels.

Identifying CG loci and functional (and/or regulatory) mutations within genes that affect salinity tolerance as well as understanding how they are regulated (gene expression patterns) can assist our understanding of the effects of climate change on wild populations and how impacts can be mitigated in cultured stocks. The resulting data can provide the basis for understanding and addressing both future conservation of wild stocks under changing environmental conditions and

32 also improve practices used in hatcheries and grow out ponds to produce larvae and juveniles for the expanding culture industry. In the current study, a genomic approach was used to investigate underlying genomic control of osmoregulation in M. australiense. M. australiense was used as a model to identify key genetic loci that are responsible for osmoregulation more widely in other

Macrobrachium taxa. Data produced in this study can be applied to better understand salinity tolerance in aquatic species generally and to discover key genes/SNPs in other Macrobrachium species (e.g. M. rosenbergii) that play fundamental roles in this important trait. This information can assist development of more saline-resistant culture lines that can withstand, and acclimate to, increased salinity levels in farm ponds.

2 Chapter 2: A transcriptomic scan for potential candidate genes involved in osmoregulation in an obligate freshwater palaemonid prawn (Macrobrachium australiense)

Published as: Moshtaghi et al. (2016), A transcriptomic scan for potential candidate genes involved in osmoregulation in an obligate freshwater palaemonid prawn (Macrobrachium australiense). PeerJ 4:e2520; DOI 10.7717/peerj.2520

A transcriptomic scan for potential candidate genes involved in osmoregulation in an obligate freshwater palaemonid prawn (Macrobrachium australiense)

Azam Moshtaghi1, Md. Lifat Rahi1, Viet Tuan Nguyen2, Peter B. Mather1 and David A. Hurwood1

1Queensland University of Technology (QUT), Science and Engineering Faculty, School of Earth

Environmental and Biological Sciences, Brisbane, QLD 4001, Australia

2University of the Sunshine Coast, Science and Engineering Faculty

*Corresponding author: Azam Moshtaghi, [email protected]

Email addresses:

AM: [email protected]

MDR: [email protected]

TVN: [email protected]

PBM: [email protected]

DAH: [email protected]

2.1 2.1 Abstract

2.1.1 Background

Understanding the genomic basis of osmoregulation (candidate genes and/or molecular mechanisms controlling the phenotype) addresses one of the fundamental questions in evolutionary ecology. Species distributions and adaptive radiations are thought to be controlled by environmental salinity levels, and efficient osmoregulatory (ionic balance) ability is the main mechanism to overcome problems related to environmental salinity gradients.

Methods: To better understand how osmoregulatory performance in FW crustaceans allow individuals to acclimate and adapt to raised salinity conditions, here we; i), reviewed the literature on genes that have been identified to be associated with osmoregulation in FW crustaceans, and ii), performed a transcriptomic analysis using cDNA libraries developed from mRNA isolated from three important osmoregulatory tissues (gill, antennal gland, hepatopancreas) and total mRNA from post-larvae taken from the freshwater prawn, Macrobrachium australiense using

Illumina deep sequencing technology. This species was targeted because it can complete its life cycle totally in freshwater but, like many Macrobrachium sp., can also tolerate brackish water conditions and hence should have genes associated with tolerance of both FW and saline conditions.

Results: We obtained between 55.4 and 65.2 million Illumina read pairs from four cDNA libraries.

Overall, paired end sequences assembled into a total of 125,196 non-redundant contigs (≥200 bp) with an N50 length of 2282 bp and an average contig length of 968 bp. Transcriptomic analysis of

M. australiense identified 32 different gene families that were potentially involved with osmoregulatory capacity. A total of 32,597 transcripts were specified with gene ontology (GO) terms identified on the basis of GO categories. Abundance estimation of expressed genes based on

TPM (transcript per million) ≥ 20 showed 1625 transcripts commonly expressed in all four libraries. Among the top 10 genes expressed in four tissue libraries associated with osmoregulation, arginine kinase and Na+/K+- ATPase showed the highest transcript copy number with 7098 and 660, respectively in gill which is considered to be the most important organ involved in osmoregulation.

Discussion: The current study provides the first broad transcriptome from M. australiense using next generation sequencing and identifies potential candidate genes involved in salinity tolerance and osmoregulation that can provide a foundation for investigating osmoregulatory capacity in a wide variety of freshwater crustaceans.

Keywords: Macrobrachium australiense, transcriptome, osmoregulation, Illumina, gill, antennal gland, hepatopancreas

2.2 Introduction

Currently, billions of humans rely on aquatic species as their primary source of animal protein and so future predicted changes to fishery production are likely to have major impacts on human populations and economies worldwide (Ficke et al., 2007). Among farmed aquatic species, decapod crustacean taxa (Order: Decapoda) comprise more than 200 species (including prawns/shrimps, lobsters and crabs), many of which support significant commercial industries. Of these, prawns are particularly important to the FW aquaculture industry. The FW prawn aquaculture industry worldwide depends largely on unimproved, essentially wild stocks for farming, most of which are produced in basic hatcheries or that are harvested as juveniles from the wild (Jung et al., 2011). Currently the most important FW prawns used in aquaculture are all

37 members of the genus Macrobrachium (Family Palaemonidae). Macrobrachium spp. display a wide variety of life history strategies and occur in a diverse array of aquatic habitats and naturally occupy a wide range of environments (including salinities) and show a variety of behavioural patterns that allow them to deal with changing environments (Huong et al., 2010). Culture of FW prawns in some places (the Mekong River Basin (MRB) is a notable example), is confronted with a serious challenge; saltwater penetration of freshwater ecosystems as many species are unable to perform osmoregulation successfully in variable environmental conditions (Nguyen et al., 2014).

FW crustaceans need to control their body ion concentrations in the face of variation in environmental salinity levels and therefore require an ability to regulate their osmotic haemolymph pressure in order to survive. The osmoregulatory system plays a pivotal role in dealing with changes in ionic concentrations (regulation of Na+, K+, H+, Ca2+, Mg2+, HCO3- and Cl- ions) in an individual’s body compared with the aquatic medium they occupy. This complex interaction impacts regulation of total osmotic concentration, regulation of intracellular vs. extracellular ion levels, acid-base balance and levels of a number of other organic ions (Lignot et al., 2000). This is further complicated by the presence of an open blood system.

Crustaceans are a well-known group for their successful colonisation of freshwater from the marine environment and this evolutionary trend is generally associated with a wide diversity in osmoregulatory capacity (Freire et al., 2008). Many decapods can utilise a wide range of salinity levels (freshwater, brackish water and marine conditions), with some species able to move among ecotypes within their own life times (Gillanders et al., 2003). So, efficient osmoregulatory capacity is a fundamental requirement for coping with environmental stressors and it will be important to identify physiologically significant candidate genes (CG) that contribute to effective osmoregulation in these organisms in the face of rapid environmental change. Understanding the

38 genomic and/or functional genomic resources associated with effective salinity tolerance in crustaceans is a basic requirement to survive under different environmental conditions. Identifying

CG loci and functional mutations within these genes that affect salinity tolerance as well as understanding how they are regulated (gene expression patterns) can assist predictive power for likely outcomes of the effects of climate change on wild populations and how impacts can be mitigated in farmed stocks.

Modern genomic approaches, particularly, next generation sequencing technology (NGST) in addition to differential gene expression (DGE) profiling allow deep understanding of gene expression patterns and signaling pathways to be developed. Transcriptomic analysis has been used widely in biological studies and has provided practical insights into gene discovery, differential gene expression patterns under different environmental conditions and also efficient discovery of molecular markers (Jin et al., 2013; Li et al., 2015; Manfrin et al., 2015; Song et al.,

2015; Thanh et al., 2014; Zhao et al., 2015).

Understanding the genomic basis of salinity tolerance and relative efficiency of the osmoregulatory system under changing conditions can help us to understand the genomic basis of osmoregulation itself (Freire et al., 2008). Salinity (ionic content) is one of the most important abiotic factors that affect species distribution, population expansions and growth of local culture industries. For instance, rates of protein synthesis and respiratory metabolism in M. rosenbergii, the species supporting the largest FW crustacean culture industry worldwide, are affected significantly by changes in salinity. M. rosenbergii individuals are very sensitive to changes in salinity, pH and temperature during the pre-moult stages of their life cycle and this can have significant effects on their immune response later (Cheng and Chen, 2000). At raised salinity levels, mortality rate is considerable and growth performance is compromised. Alternatively, in

39 the marine shrimp Penaeus monodon, raised or lower levels of salinity (compared to the optimal level~25‰) is associated with increased energy expenditure for active ion transport and involves degradation of energy-rich compounds including lipids (Ye et al., 2009). This results in energy being diverted from growth to ion balance maintenance, an outcome that affects production cycle productivity.

Transcriptomic analysis can allow potential candidate genes involved in adaptation to divergent osmotic environments to be identified and can determine consistent genomic changes that occur in related species across micro/macro-evolutionary time scales (Jung et al., 2011). To this end, we used Macrobrachium australiense (Palaemonidae) as a model (to better understand potential genes involved in osmoregulation) for other freshwater prawns in the genus Macrobrachium because it is the most widespread FW prawns species across mainland Australia and is the most successfully adapted species that completes its life cycle in freshwater (Dimmock et al., 2004), yet under controlled conditions in the laboratory can tolerate up to 17 ‰ salinity (Sharma and Hughes, 2009).

The most important features of this model species include high tolerance of saline conditions at all life history stages, short generation time (life cycle is abbreviated into three zoeal stages, maturation normally takes 4-6 months after hatching), reduced larval stages, mature stage at hatching and relatively large egg size (Dimmock et al., 2004).

Adaptation to different salinity levels is a complicated process and is related to cell volume regulation, organic ion transport, changes to cellular carbohydrate levels, nitrogen metabolism and whole tissue remodelling (Henry et al., 2011). In crustaceans, in particular Macrobrachium prawns, certain organs/tissues provide ideal targets for a comparative genomic analysis of the molecular basis of adaptive physiological response to saline environments. Therefore, the primary aim of this study was to develop a more complete understanding of the molecular basis of

40 osmoregulatory functioning by sequencing transcriptomes from the most important organs involved in ion regulation and salinity tolerance using an Illumina platform. The transcriptomics approach was used to identify the major potential genes involved with osmoregulation in osmoregulatory organs including the gill, antennal gland and hepatopancreas of M. australiense.

2.3 Materials and Methods

2.3.1 Sample collection and maintenance

M. australiense specimens were collected from a site on Bulimba Creek (27°56 S, 153°09 E) (a tributary of the Brisbane River, SE Queensland, Australia) (Field permit for this study was issued by the Department of Agriculture, Fisheries and Forestry under Queensland Government, Permit

Number: 166312). This site had 0 ‰salinity but was only 22 km from the confluence with the main channel of the Brisbane River where water salinity is approximately 30‰. Furthermore, tidal influence occur upto only 5 km downstream from the sampling site with no barriers to dispersal between the site and brackish water. Population genetic data (Rogl, 2015) using a CO1 marker revealed no evidence for genetic differentiation between different M. australiense populations that are separated by brackish water across the entire lower reaches of the Brisbane River drainage. It was assumed therefore that M. australiense individuals collected in this study had ready access to brackish water. Total genomic DNA was extracted from pleopods taken from each prawn for taxonomic validation using a fragment of the mtDNA COI gene following methods outlined in a previous study on M. rosenbergii (Hurwood et al., 2014). Sanger sequencing results for mtDNA

COI confirmed that all individuals used to generate the transcriptomes in this study were M. australiense, with all individuals having haplotypes identical to, or minimally divergent from (one or two bp), those previously detected for this species in the Brisbane River (Sharma and Hughes,

2009).

2.3.2 RNA extraction, cDNA library preparation and Illumina sequencing

For transcriptomic analyses, collected prawns were transported live to the Molecular Genetics

Research Facility (MGRF) at Queensland University of Technology (QUT). Live individuals were then euthanized and dissected immediately to obtain fresh tissue for total RNA extraction. In total,

12 adults (body weight between 6-8 grams) prawn individuals were used for RNA extraction but due to the small amount of available tissue, we pooled tissues from 4 prawns each time. Then we used the best quality RNA samples for each tissue. In addition, we extracted total RNA from 2 post-larvae (body weight 0.5-0.6 gram) individuals. Dissected and pooled tissues including gill

(G), antennal gland (A), and hepatopancreas (H) from adults and whole individual of post-larvae

(PL) were transferred to a mortar containing liquid nitrogen, and tissues were crushed into a fine powder using pestles. Powdered tissues were then used for total RNA extraction using an RNeasy

Mini Kit (Qiagen, Germany) according to the manufacturer’s protocol. Genomic DNA was removed using a Turbo DNA-free kit (Ambion, Life Technologies) and the integrity and concentration of extracted total RNA was checked using a Bioanalyzer 2100 (Agilent

Technologies, version 6) and a Nano Drop 2000 spectrophotometer (Thermo Scientific), respectively. RNA samples were stored at -80 °C for later use. 4 µg of total RNA were used as starting material for mRNA purification, using a TrueSeqV1 Standard mRNA Sample Prep kit

(Illumina, USA). Purified mRNA was then fragmented into smaller fragments to initiate cDNA synthesis. After synthesizing the first and second strands of cDNA, an Illumina barcoding adapter

(individual index) ligation step was performed chemically according to the manufacturer’s protocol. Adapter ligated cDNA was purified using the AMPure XP magnetic bead (Beckman

Coulter, Beverly, USA) system combined with ten subsequent washing steps. Quality of constructed cDNA libraries was checked using a Bioanalyzer 2100 (Agilent), Qubit® 2.0

Fluorimeter (Invitrogen, Life Technologies) and RT-qPCR (BJS Biotechnologies, UK). All libraries were normalized via dilution before the final sequence run. The resulting four cDNA libraries (G, A, H and whole PL) were subjected to the Illumina NextSeqTM 500 Platform for 75 bp paired end sequencing. All activities were performed at the Molecular Genetics Research

Facility (MGRF) under the Central Analytical Research Facility (CARF) at Queensland University of Technology.

2.3.3 Quality control of Illumina sequence data

High throughput Illumina sequencing produced >257 million raw reads from the four cDNA libraries. Quality of the sequence data was checked first with FastQC (version 0.11.3) software

(Andrews, 2010). Trimmomatic software (version 0.36) was used for quality filtering of raw data

(Bolger et al., 2014), to trim noisy reads (low quality bases) at both ends, reads <36 bases were not considered. Only high quality and filtered reads (Phred Score, Q≥30) were used in the subsequent bioinformatics analyses.

2.3.4 Bioinformatics analyses

All of the quality controlled raw reads from the four cDNA libraries were pooled to make a single reference transcriptome from Bulimba Creek M. australiense. De novo assembly of high quality reads was performed using the Trinity software package (version 2014-04-13p1) applying perl script (Trinity --seqType fq --JM 200G --left Combined_1.fastq --right Combined_2.fastq -- trimmomatic --CPU 12) and default settings (Haas et al., 2013) to obtain contigs. We assessed transcriptome assembly completeness using the CEGMA software package (Parra et al., 2007).

De novo assembled contigs were then blasted (BLASTX searched) against the NCBI non- redundant database using BLAST+ (version 2.2.29) with an e value of 1e-5 for significant blast hits. The resulting blasted files were loaded into Blast2GO pro software (version 3.0) (Camacho

43 et al., 2009) to map and annotate sequences to determine potential functions of each transcript based on gene ontology (GO) analysis. Following this step, we exported the top hit species distribution chart using the same Blast2Go pro software to check blast hits matching with other species. The annotated contigs were further used in Blast2Go software for InterPro scanning in order to obtain InterProScan IDs. Annotated and InterPro scanned results were then exported and extracted as text files to use for preparing WEGO plots (Web Gene Ontology Annotation Plotting) to categorize gene functions into three different categories (cellular components, molecular functions and biological processes) (Ye et al., 2006). We also used the de novo assembled transcripts for differential gene expression analysis that included: mapping of raw reads against the reference transcriptome using Bowtie (version 1.1.1) (Song et al., 2014), abundance estimation using RSEM (version 1.2.7) (Li and Dewey, 2011) and differential gene expression analysis using edgeR Bioconductor (version 3.3) (Robinson et al., 2010). The RSEM approach generates expression value matrices by calculating maximum likelihood abundance estimation at 95% credibility intervals for genes. Expression value matrices were normalized via the Trimmed Mean of M-values (TMM) method in edgeR to adjust for library size and skewed expression of transcripts (Nakasugi et al., 2013).

2.3.5 Identification of potential genes

Prior to bioinformatics analysis, a list of candidate genes (Table 2-1) was prepared from a literature survey. The blasted and annotated transcriptome data sets were mined for candidate gene identification. Results of Blastx searches identified a large number of transcripts belonging to different gene families. The full length of each transcript was identified using open reading frames

(ORF) finder to identify a start and stop codon in both strands. (Liu et al., 2012).

Table 2-1: Specific genes previously identified as having a functional role in salinity tolerance in crustaceans (only the authors who worked with the specific genes have been cited) .

GENE FUNCTION REFERENCE Aquaporin 3 (AQP) cell volume regulation and water (Gao, 2009) permeability under stress Calreticulin (CRT) signal transduction in many biological (Luana et al., 2007) processes of growth, moulting and stress response Na+/K+- ATPase (NKA) creating an electrochemical gradient (Santos et al., 2007) across the cell membrane in the gills of (Boudour-Boucheker et al., 2014) crustaceans for actively absorb or release ions Na+/K+/2Cl- cotransporter (NKCC) transport ions into gill cells either from the blood or media based on salinity (Faleiros et al., 2010) Carbonic anhydrase (CA) involved in all main gill function such as regulation of Na+, Cl-, H+, HCO3- (Henry et al., 2003) (specifically in low salinities) V type- (H+) ATPase role in salinity acclimation and (Tsai and Lin, 2007) osmoregulation ILF2 (interlukin enhancer binding a transcriptional regulator against biotic (Barman et al., 2012) factor 2) stresses Alkaline phosphatase involved in salinity acclimation and (Lovett et al., 1994) osmoregulation response Selenophosphate (SPS1) involved in salinity tolerance, oxidative (Gillanders et al., 2003) role Integrin involved in salinity stress (Pongsomboon et al., 2009) ABC protein C12 (ABCC 12) functional role in osmoregulation and (Liu et al., 2012) salinity tolerance under osmotic stresses P38 MAP kinase regulation processes (mRNA stability, (He et al., 2013) protein degradation) under environmental stress USP involved in salinity tolerance (Kotlyar et al., 2000) Na+/H-exchanger regulation of Na+ and H- levels and (Freire et al., 2008) involved in regulation of extracellular acid-base Ca2+ -ATPase involved in Ca2+ regulation and (Freire et al., 2008) calcification Cystic Fibrosis Transmembrane regulation of Cl- channels and increases (Bodinier et al., 2009) Regulator (CFTR) the osmoregulatory capacity Arginine kinase involved in active ion transport across (Kotlyar et al., 2000) membrane OST complex maintaining homeostasis during (Pongsomboon et al., 2009) salinity/osmotic stress Acyl-CoA binding protein (ACBP) role in salinity tolerance and adaptation (Kiruthika et al., 2013) within low and high salinity levels Catechol-o-methyltransferase (COMT) association with low and high salinity and (Rajesh et al., 2012) crucial role within salinity stresses

Mitochondrial carrier protein involved in osmoregulation signal (Menze et al., 2005) transduction

2.4 Results

2.4.1 Illumina sequencing, de novo assembly and annotation

Illumina sequencing results from four different cDNA libraries based on important osmoregulatory tissues (gill, antennal gland, hepatopancreas) as well as whole individual postlarvae from M. australiense are presented in Table 2-2.

Table 2-2: Summary of raw reads and quality control of Illumina results (Q ≥ 30, 75 bp paired-end reads).

Sequencing Parameters Gill Antennal Gland Hepatopancreas PL Number of raw reads 65,713,435 61,417,916 59,472,637 70,538,605 Read length (bp) 26-70 35-76 26-70 26-70 % of GC content 42 42 43 42 Number of read after trimming 61,184,264 57,182,730 55,385,162 65,229,834 % of read after trimming 93.10 93.10 93.12 92.47

Data from the four cDNA libraries were pooled together to make a single reference transcriptome for M. australiense. Overall, Illumina paired end sequences were assembled into a total of 125,196 non-redundant contigs (≥200 bp) with an N50 length of 2282 bp (Table 2-3). Average and median contig lengths for the transcriptome were 968 bp and 389 bp respectively. 27,052 contigs were greater than 1000 bp in length. CEGMA (Core Eukaryotic Genes Mapping Approach) based transcriptome assembly completeness test revealed 97.98% completeness for full length genes and

98.6% completeness for partial length genes, indicating a high quality transcriptome assembly. An overview of assembly results are presented in Table 2-3.

Table 2-3: Summary of de novo assembly and functional annotation results for M. australiense.

Number of contigs 125,196 Total bases in contigs 124,731,462 Number of large contigs (≥ 1000bp) 27,052 N50 (bp) 2,282 Average contig length 968 Median contig length 389 Contigs blasted 32,597 Contigs mapped 29,160 Contigs annotated 20,722 Transcriptome assembly completeness 97.98%

Fig 2-1: represents the top hit species distribution chart from blast results. Daphnia pulex was the top hit species. While D. pulex is distantly related to M. australiense, it is to date, one of the crustacean species for which a complete sequenced and well annotated genome is available. The ‘Others’ category contained many crustacean taxa including commercially important Penaeids, in particular, P. monodon and L. vannamei.

2.4.2 Identification of putative osmoregulatory gene families (contigs)

32 different candidate gene families that are potentially involved with osmoregulation (Table 2-4) were identified based on blast hits and GO annotation. Most of the key candidate genes involved with osmoregulation in aquatic crustaceans were identified in the analysis of the M. australiense transcriptome.

Table 2-4: Summary of identified candidate genes potentially associated with osmoregulation in M. australiense from this study.

Contig ID Contig (Gene) Name Contig Amino Gene Ontology (GO) Length Acid (bp) Length c57301_g1_i3 Alkaline phosphatase 2671 548 phosphatase activity; metabolic process c54762_g4_i3 Arginine kinase 1843 363 ATP binding; kinase activity; phosphorylation c58410_g1_i4 Aquaporin (AQP) 5176 299 substrate-specific transmembrane transporter activity; transmembrane transport c58153_g1_i1 ATP-binding cassette 11145 3471 protein binding; cellular process; regulation of biological process; response to a stimulus c52504_g1_i1 Carbonic anhydrase (CA) 3027 269 carbonate dehydratase activity; zinc ion binding c46606_g1_i1 Calreticulin 1717 404 calcium ion binding c37700_g1_i1 H+ transporting ATP 944 233 proton-transporting ATP synthase activity, ATP synthase synthesis coupled proton transport c59162_g1_i2 Integrin 7468 1771 cell adhesion, stress response c45418_g1_i2 Interleukin 2113 538 cellular response to unfolded protein c48742_g1_i1 Mitochondrial carrier 2728 310 Ion transport c55567_g3_i2 Na+/K+-ATPase alpha 4263 1036 sodium, potassium-exchanging potassium ion subunit transport; sodium ion transport; cation transmembrane transport c55187_g2_i1 P38 map kinase 776 103 MAP kinase activity; ATP binding; protein phosphorylation c46739_g1_i1 V-type proton (H+)- 2931 426 proton-transporting V-type ATPase, proton- ATPase transporting ATP synthase activity, proton- transporting ATPase activity, ATP hydrolysis coupled proton transport c55321_g1_i1 USP6 n-terminal-like 2474 724 regulation of GTPase activity c57918_g1_i1 Sodium Hydrogen 6777 672 an integral component of membrane; sodium: proton Exchanger antiporter activity, sodium ion transport; regulation of pH; inorganic cation transmembrane transport c57172_g1_i5 Sodium bicarbonate 6754 1191 sodium, bicarbonate symporter activity; anion, anion cotransporter antiporter activity; sodium ion transport; chloride transport; bicarbonate transport

c54650_g1_i1 Sodium potassium chloride 4878 1051 sodium, potassium, chloride symporter activity; cotransporter sodium, chloride symporter activity; amino acid transmembrane transport; potassium ion transport; rubidium ion transport; chloride transmembrane transport c8687_g1_i1 Selenophosphate (SPS) 2204 326 Involved in stress tolerance c88643_g1_i1 Sodium myo-inositol 2453 669 Ion transporter, transmembrane transport cotransporter c47111_g1_i1 Sodium channel protein 1907 385 regulation of ion transmembrane transport; sodium ion 60e transmembrane transport regulation of ion transmembrane transport c57445_g1_i2 Sodium Calcium 8944 814 integral component of membrane; calcium, sodium Exchanger 1 antiporter activity; calcium ion transport; cell communication; transmembrane transport c52329_g1_i1 Selenoprotein s 2044 209 regulation of biological process; regulation of cellular process; response to stimulus c58145_g1_i4 Potassium voltage-gated 5784 618 potassium ion transport; transmembrane transport; channel protein shaker-like signal transduction by phosphorylation isoform 1 c44674_g1_i2 Potassium Sodium 1422 342 voltage-gated potassium channel activity; potassium hyperpolarization- channel ion transmembrane transport c58353_g6_i2 Potassium channel 2476 640 potassium channel activity; potassium ion subfamily K member 16- transmembrane transport like c53215_g1_i2 Mitochondrial 2244 252 response to oxidative stress and stress tolerance transcription factor isoform a c52854_g1_i1 Magnesium transporter 3159 326 magnesium ion transmembrane transporter activity protein 1-like c52684_g1_i1 Heat shock protein 90 3461 726 response to stress c58231_g3_i1 Chorion peroxidase 4172 922 response to oxidative stress; oxidation-reduction process c58101_g3_i4 Chloride channel protein 3 6326 826 chloride transport; ion transmembrane transport; regulation of anion transport c28616_g2_i1 Calcium-binding 293 697 transport; biological regulation, regulation of cellular mitochondrial carrier process protein 1 c56962_g1_i7 Bestrophin isoform a 2668 370 chloride transport; cellular water homeostasis

A total of 32,597 transcripts were specified with 145,870 GO terms and were assigned to GO categories, as either associated with molecular function (33551 GOs), biological process (73826

GOs) or cellular component (38493 GOs) (Figure 2-2). The highest numbers of GO term categories were found to be involved with biological process while molecular function showed the smallest number.

Fig 2-2: Top most represented GO terms per biological categories: a) molecular function, b) cellular component and c) biological process.

2.4.3 Differential gene expression (DGE) patterns

Abundance estimation of expressed genes based on transcript per million values (TPM≥ 20) showed that 3810, 2707, 3806 and 4011 transcripts were expressed in antennal gland, gill, hepatopancreas and post-larval tissues, respectively (Figure 2-3). 1625 transcripts were found to be in common and expressed in all libraries. Table 2-5 shows the expression patterns (TPM values) of the top most 10 important candidate osmoregulatory genes. The heatmap (based on quality filtered raw read counts for differentially expressed transcripts) in supplemental figure (Figure 2-

3) shows the qualitative pattern of differentially expressed transcripts in different M. australiense tissues.

Table 2-5: Top 10 osmoregulatory genes expressed in different tissues in adult and PL individuals.

Contig ID Contig Description Transcript Per Million (TPM) Gill Antennal Gland Hepatopancreas Post-Larvae c54762_g4_i3 Arginine kinase 7098 5525 2077 4153 c55567_g3_i2 Na+/K+-ATPase 660 407 90 266 c46606_g1_i1 Calreticulin 385 262 683 524 c57445_g1_i2 Na+/Ca+2 exchanger 71 74 28 80 c54650_g1_i1 Na+/K+/2Cl- cotransporter 115 73 50 78 + - c57172_g1_i5 Na /HCO3 cotransporter 61 54 4 25 c46739_g1_i1 V-type ATPase 70 33 70 85 c59162_g1_i2 Integrin 38 31 5 21 c52504_g1_i1 Carbonic anhydrase 211 27 20 41 c8687_g1_i1 Selanophosphate 12 22 7 19

Fig 2-3: Venn diagram showing the number of expressed transcripts shared among different samples based on abundance of transcripts per million (TPM).

2.5 Discussion

Palaemonid prawns contain the most impressive list of crustacean invaders of freshwater from an ancestral marine habitat and they manifest a diverse array of osmoregulatoy capacities (Towle et al., 2011). Various tissues including gill, antennal gland and hepatopancreas all contribute to ion balance and regulation that has allowed this family to colonise a wide variety of osmotic aquatic niches (Shekhar et al., 2013). New technologies linked to NGS developments have allowed mechanisms that permit adaptive colonisation of freshwater to be explored with high data accuracy, at relatively low cost and generation of very large amounts of data (Lv et al., 2013). The current study generated a comprehensive NGS database for a freshwater-adapted Palaemonid species as an initial step to better understand the role of various potential candidate genes with a

52 function in osmoregulation and to provide a model for more detailed investigations of other related and commercially important crustacean taxa. The transcriptome was generated as a starting point to investigate the molecular foundation for osmoregulation in FW crustaceans. M. australiense is a freshwater inhabitant, but this taxon’s ability to tolerate brackish water conditions is well documented (Sharma and Hughes, 2009). This species is widely distributes and abundant across

Australia and possesses a comparatively large body (provides sufficient tissue material) size, characteristics that make it an ideal candidate species for identification of the molecular basis of osmoregulation (and the associated candidate genes involved) under different salinity conditions.

The highest number of raw reads (over 70 million) was obtained for post-larvae (Table 2-2) because at this early stage of the life cycle, a higher number of genes are expressed. Different genes are expressed in different tissue types and more reads were obtained from tissues where more genes were actively expressed (Tian et al., 2011). Thus, availability of reads can vary according to the tissue sampled. The transcriptome data set generated here from four cDNA libraries resulted in over 250 million copies of 75 bp paired end raw reads that generated 125,196 longer contigs from de novo assembly.

In total, 32 important gene families were identified that have potential roles in osmoregulation and ion regulation that contribute to salinity tolerance in this species. Overall, 32,597 (25.43%) of the assembled contigs showed significant blast hits and 20,722 (16.55%) contigs produced GO term annotations (Table 2-3). In the current study, we identified more potential osmoregulatory genes

(32 genes, Table 2-4) compared with the total number of genes (21 genes, Table 2.1) identified in the literature survey. This is because we focused only on prawn species for the literature review but our GO annotation approach involved blast matching with all identified genes for many different species that were available in the public database. Thus, the M. australiense transcriptome

53 dataset provided us with a set of potential target genes available (not only for osmoregulation but for many other phenotypes) for further explanation in this and other related species with different osmotic capacities so that an investigation of the key genes and their functional mutations can be explored in greater details.

Abundance estimates of expressed genes based on TPM values showed that 1625 transcripts common to, and expressed in, all four libraries that represent different life history stages and tissue types. The 1625 common transcripts (Fig. 2-4) suggest an important functional role (potentially housekeeping roles) for basic cellular maintenance in this species as they are expressed in all tissues and at all different stages of life. The hepatopancreas in adults showed the highest number of distinct expressed transcripts (1082) implying that it is probably the most important tissue as this is the master tissue that controls most body functions. A higher number of transcripts (808) were found to be expressed commonly between hepatopancreas and in PLs indicating expression of important genes that are required for all stages of life. Gill and antennal gland tissues shared

454 transcripts in common. This is likely because these 2 tissues are involved with ion exchange

(osmoregulatory function), gas exchange and some other common functions (Lignot et al., 2000).

Based on identified candidate genes in other species, we were able to target the most important genes related to metabolic process, ATP binding, transmembrane activities, regulation of ion transportation, response to environmental stresses and stress tolerance in M. australiense.

Efficient osmoregulatory capacity does not depend on ion exchange only; it also involves many other biological processes. Of the 32 osmoregulatory gene families identified here, 17 genes were

+ + + 2+ 2+ - identified to be involved with transport of the most important ions (Na , K , H , Ca , Mg , HCO3 and Cl-), all of which impact osmoregulation and influence salinity tolerance. Differential expression patterns (based on transcript abundance estimates) of candidate genes involved with

54 osmoregulation, clearly indicate the more important role of gill and antennal gland tissues to perform osmoregulation in adult individuals as the expression patterns of potential osmoregulatory genes in these tissues were high compared with that seen in the hepatopancreas (Table 2-5). Of interest however, was that postlarvae showed only moderate expression levels of the same candidate genes. Differential gene expression patterns showed a higher number of genes expressed differentially in post-larvae (supplemental Figure 2-3). This is likely related to rapid developmental changes that are occurring during earlier life history stages that require raised levels of expression of genes involved with growth, tissue development and other developmental processes. The heatmap also shows the highest level of expression pattern in hepatopancreas for adult prawns because this tissue is considered to be the master organ that controls most cellular activity.

Arginine kinase (AK) in gill tissue has been recognised as one of the most important genes associated with osmoregulatory performance. Expression levels of AK in crustacean gill tissue is generally very high in actively osmoregulating individuals (used in energy production and metabolism) (Abe et al., 2007; Kotlyar et al., 2000), but it also has a role in immune responses

(Kinsey and Lee, 2003; Yao et al., 2009). In the current study, AK levels in the four M. australiense cDNA libraries were highest in gill tissue (7098 expressed copies, Table 2-5) indicating that it may also function in energy generation for osmoregulation in M. australiense as well.

High expression levels of certain other genes including the calreticulin in the hepatopancreas and also in PL indicates that this gene family has a role in many biological processes ranging from

Ca2+ storage (more important for PL stage involved in calcifications and metamorphosis) (Barman et al., 2012; Xu et al., 2015), gene regulation (Duan et al., 2014; Gelebart et al., 2005), signalling

55 pathway and fast immune reaction against serious environmental stressors apart from osmotic regulation (Tian et al., 2011; Visudtiphole et al., 2010).

NKA (Na+/K+-ATPase), is also critical for osmoregulation in M. australiense. This gene is an important integral membrane protein involved in ion transport and ammonia excretion, so it has an associated role in osmoregulatory systems in many taxa, but particularly in aquatic crustaceans

(Tsai and Lin, 2007). The activity of NKA in osmoregulatory tissues, where it plays a major role in ion exchange, has been investigated in both hyper/hypo osmoregulating and osmoconforming crustacean taxa (Charmantier and Anger, 2011; Cieluch et al., 2004; Lucu and Towle, 2003). In this study, we identified different NKA isoforms and subunits of NKA ranging from 819-4263 bp in sequence length. Maximum expression level was evident in gill tissue where gills obviously provide the major osmoregulatory organs (Table 2-5). NKCC (Na+/K+/2Cl- cotransporter) located in the basal membrane drives Cl- and Na+ exclusion across salt-secreting epithelia and is involved in both Na+ and Cl- uptake and excretion powered by Na+ and Cl- turnover. Mediation of relative expression levels of NKA during hypo/hyper osmotic challenges (Luquet et al., 2005) has been demonstrated in a number of studies, in particular two different crab taxa Chasmagnatus granulates and Carcinus maenas (Genovese et al., 2000). Here we also found 212-4878 bp of the

NKCC gene family involved in Na+, K+ and Cl- transport activity and transmembrane transport.

The different functions of Carbonic Anhydrase (CA) in cellular components, respiration and metabolic process have been the focus of many case studies. Its pivotal role in the crustacean gills is associated with Na+ and Cl- regulation and it is directly involved in ion transport in M. australiense. Transcript lengths ranged from 218-3027 bp. This enzyme constitutes one of the primary components of osmotic regulation, and is highly expressed in the organs that are

56 responsible for osmotic and ionic regulation (Jasmani et al., 2010; Jasmani et al., 2008; Roy et al.,

2007).

Another significant gene involved with osmoregulation in crustaceans is V-type ATPase (VA) that is a crucial gene for regulation of ion balance and cell volume (Freire et al., 2008). The important role/s of this gene have been documented in many aquatic animals including in; euryhaline crabs,

Carcinus maenas and Eryocheir sinensis (Weihrauch et al., 2001) and two freshwater crayfish,

Cherax destructor and C. cainii (Ali et al., 2015). Here, we identified an M. australiense VA gene and recovered gene ontology terms indicate a role for this gene in many different complex mechanisms including acid/base balance, nitrogen excretion, ion exchange and proton regulation in the freshwater environment.

Some other candidate genes identified here were found to be involved with cellular response to stress, ATP binding, regulation of biological responses, response to stimuli and in oxidative stress.

Potential genes for these processes likely include: selenoprotein, aquaporin, USP, ATP-binding cassette, alkaline phosphatase (AP), integrin and interleukin (Table 2-3 and Table 2-4). Among these gene families, AP codes for well-known metallo-enzymes in mammals with diverse functions, mostly related to metabolic processes. Some studies however, have reported a role for this gene in osmoregulatory performance in blue crab, Callinectess apidus (Lovett et al., 1994), green crab, Scylla serrata (Park et al., 2001) and in the euryhaline crab, Cyrtograpsus angalatus

(Pinoni and Mananes, 2004). Aquaporin acts as a water channel for selective transfer of water and other molecules across cell membranes in gill and antennal gland tissues (Gao, 2009). This gene is directly involved in cellular osmotic challenge and cell volume regulation. Aquaporin is also one of the most-studied genes across taxa (invertebrates to mammals) because it has a reported

57 functional role in salinity tolerance (Barman et al., 2012; Boyle et al., 2013; Grosell 2006;

Nishimura and Fan, 2003; Pongsomboon et al., 2009).

In summary, the current study has generated a comprehensive genomic resource for identification of key candidate genes involved in osmoregulation in M. australiense and has also provided a platform for functional genomic studies of other freshwater prawn taxa more widely. 32 different potential genes were identified that are likely to play major functional roles in osmoregulation and salinity tolerance processes. The current study can provide a baseline for developing a more comprehensive understanding of the functional genomic basis of ion exchange and ion balance processes in invertebrate aquatic taxa. Results of this study could be used for functional genomic scans among different populations of the target species collected from different natural osmotic niches to better understand the functional genomic adaptive processes occurring in different natural populations. In the future, it will also be important to assess the gene expression patterns of the major candidate genes identified here under different salinity conditions in M. australiense to explore the interactions that contribute to effective ionic regulation in this species.

Data availability:

All of the de novo assembled sequences are available in a public data base (figshare) under the following accession number: https://dx.doi.org/10.6084/m9.figshare.3444992.v1

2.6 Supplementary files

Fig 2-4: Heatmap showing differential gene expression pattern (based on read counts) of different tissues of adult and post-larvae. AEB= Antennal Gland, GB= Gill, HIB= Hepatopancreas, PL= Post-Larvae. Y- axis represents the hierarchical clustering of differentially expressed transcripts, while red colored lines indicate highly expressed transcripts and green colored lines indicate lowly expressed transcripts.

3 Chapter 3. Understanding the genomic basis of adaptive response to variable osmotic niches in freshwater prawns: a comparative intraspecific analysis of Macrobrachium australiense

1. Moshtaghi et al. (submitted). Understanding the genomic basis of adaptive response to variable osmotic niches in freshwater prawns: a comparative intraspecific analysis of Macrobrachium australiense. Manuscript submitted in the Journal of Heredity.

Understanding the genomic basis of adaptive response to variable osmotic niches in freshwater

prawns: a comparative intraspecific analysis of Macrobrachium australiense

Azam Moshtaghi, Md. Lifat Rahi, Peter B. Mather and David A. Hurwood

Queensland University of Technology (QUT), Science and Engineering Faculty, School of Earth

Environmental and Biological Sciences, Brisbane, QLD 4001, Australia

*Corresponding author: Azam Moshtaghi, [email protected]

Email addresses:

AM: [email protected]

MDR: [email protected]

PBM: [email protected]

DAH: [email protected]

3.1 Abstract Understanding the molecular genetic basis that drives adaptive response to variable environmental conditions is a central goal of evolutionary biology. Here we sought to identify potential outlier

SNPs (single nucleotide polymorphisms) in 3 populations of a freshwater prawn species;

Macrobrachium australiense exposed to differing natural osmotic niches using a comparative transcriptomics approach to understand the molecular basis of osmoregulation. De novo assembly of approximately 542 million (75 nt) pair end reads collected from 10 individuals revealed 123,396 long contigs/transcripts of variable length, that showed 97.38% transcriptome assembly completeness. DGE analysis of major osmoregulatory genes revealed that Calreticulin, Na+/H+ exchanger and V-type (H+) ATPase showed the highest expression levels in the Blunder Creek population, while Crustacean cardiovascular peptide (CCP), Na+/K+-ATPase, Na+/K+/2Cl- Co-

+ - transporter (NKCC) and Na /HCO3 exchanger showed the highest expression levels in the

Bulimba Creek population. In total, 16 gene ontology (GO) term categories were functionally enriched among Blunder/Bulimba and Blunder/Stony population comparisons. We identified 4144 raw, and 835 high quality filtered SNPs, in the 3 M. australiense populations, of which 84 SNPs were identified as outliers. Outlier loci were detected in 4 important osmoregulatory genes that include: Calreticulin, Na+/H+ exchanger, Na+/K+-ATPase and V-type-(H+)-ATPase. All outliers in the osmoregulatory genes were located in non-coding regulatory regions (untranslated regions,

UTRs) of the gene. We hypothesise that outlier SNPs identified here in M. australiense populations exposed naturally to different osmotic conditions influence specific gene expression patterns that allow individuals to respond to local environmental salinity conditions.

Keywords: Macrobrachium australiense, Semi-ALD, outlier, osmoregulation, SNPs.

3.2 Introduction

Life is considered to have originated first in the sea and competition among early marine species forced some ancestral species to migrate to new environments (specifically to inland and continental freshwaters) (Root et al., 2003). Exposure to new osmotic conditions by migrants required rapid adaptation to permit persistence in the new environment. A common strategy for adaptation is that genetic plasticity can lead to adaptation by selection (Lee et al., 2011). This phenomenon is considered by many to be an interesting problems and one where evolutionary biologists seek to understand changes at the genomic level during local adaptation (Butlin, 2010).

Of the diverse array of extant animal taxa, only a few phylogenetic groups have been able to adapt successfully to low osmotic conditions (inland fresh waters) following a marine ancestry. One of the most critical challenges that animals need to deal with, in this new environment is the ability to maintain their ionic balance via osmoregulatory processes. Crustaceans are one of the major animal groups that have made this transition successfully and, this group now displays a wide diversity of osmoregulatory capacity (Freire et al., 2008). There are many crustacean species therefore that provide ideal candidate species for understanding the mechanisms underlying osmoregulatory capacity in variable osmotic gradients.

The freshwater (FW) prawn, Macrobrachium australiense is a good candidate for studying the genetic basis for control of osmoregulation under variable environmental salinity conditions

(Luikart et al., 2003). M. australiense can complete its entire life cycle in freshwater but can also tolerate brackish water conditions upto 17‰ salinity (Dimmock et al., 2004). Life history traits of inland water populations of this species show an abbreviated larval development (ALD) pattern, characterized by only a single larval stage (Short, 2004). In contrast, populations that inhabit coastal streams and creeks and that have access to brackish waters are characterized by a Semi-

ALD pattern (3-4 larval developmental stages). Length of larval duration is considered to be an adaptive response to sub lethal osmotic conditions (Short, 2004). Thus, M. australiense provides a good model for identifying and characterising key genetic loci and functional mutations that are responsible for osmoregulatory performance in different sub-lethal osmotic niches.

Sequencing methodologies) have made it relatively easy to either acquire a list of candidate polymorphic loci, or to search for SNPs and/or outliers (loci under selection or adaptive loci) in a set of candidate genes in contrast to using a blind shotgun approach to identify > 1000 SNPs from massive sequence data. SNP calling from transcriptomic data (using expressed sequences) provides a cost effective way for reducing genomic complexity while still retaining a significant fraction of the functionally relevant information (De Wit et al., 2015). An important additional outcome from this type of data is the potential to identify outlier loci from expressed sequences.

Another powerful feature of a transcriptomic data set is that it allows us to examine changes in gene expression patterns among natural populations or different individuals in a controlled experimental set up. Furthermore, transcriptomic approaches can focus on genetic variation in specific candidate genes that potentially may be under selection and this allows investigation of the molecular basis of local adaptation in wild populations (Helyar et al., 2012).

It has long been realised that differences in gene expression pattern play a significant role in population specific adaptation and also in population differentiation. A number of studies have confirmed that there is a genetic basis for differences in relative transcript abundance (Gracey et al., 2004), that can lead to adaptive divergence in wild populations (Eguavoen et al., 2015; Leder et al., 2015). Thus, combining gene expression pattern data (transcript abundance) and SNP detection from the same set of individuals from a transcriptomic data set can provide a novel way to examine the functional roles of population specific SNPs in controlling gene expression patterns. Under recent climate change and rising sea level scenarios, it is obvious that inland freshwater bodies face increasing threat from salinity intrusion and hence freshwater adapted biota may potentially need to address this issue as impacts increase over time. A population genomic study of different populations of the same species exposed naturally to different osmotic conditions can help us to understand the process of genomic response by which species can adapt to future climate change impacts. Genomic techniques are also being used increasingly to better understand the genetic factors that affect variation in economically important traits in many cultured species

(Brumfield et al., 2003), but wild populations are less well studied in this regard (Luikart et al.,

2003).

M. australiense wild populations are exposed naturally to different osmotic gradients. Natural populations therefore provide a genomic resource for identifying and characterising important osmoregulatory genes and functional or regulatory mutations in these genes that may have diverged during adaptation to inland freshwaters. These data can be used to investigate whether this process is controlled largely by mutations in regulatory sequences or mutations in functional protein coding regions. In the current study, we adopted a population genomic (transcriptomic) approach to investigate underlying genomic control of osmoregulation in a widespread Australian

65 endemic FW prawn species, M. australiense. Overall aim of the present study was to examine population specific expression patterns of candidate genes that influence osmotic regulatory capacity and also to detect outlier SNPs.

3.3 Materials and methods

3.3.1 Sample collection and maintenance

M. australiense individuals were collected from three natural populations: Blunder Creek

(17°76ʹ 4321ʺ S, 145°50ʹ 9073ʺ E), a tributary of the Herbert River in North Queensland,

Bulimba Creek (27°56ʹ 292ʺ S, 153°09ʹ 76ʺ E) and Stony Creek (26°87ʹ 87ʺ S,

152°73ʹ 1226ʺ E) tributaries of the Brisbane River that are exposed naturally to different osmotic conditions. The Stony Creek population (considered to be a pure freshwater population) is separated from the main channel of the Brisbane River due to Somerset dam that was built in 1935.

The Blunder Creek collection site was located upstream in the Herbert River system far from any tidal influence and prawns in this population can complete their entire life cycles in pure low ionic freshwater conditions. The Bulimba creek population is located only 10 kms from the Brisbane

River confluence, and only 2 kms upstream from tidal influence. Individuals in this population have direct access to brackish water. The Stony creek population has been confined to freshwater for less than 100 years because of the dam. Local salinity and conductivity conditions for each sampling sites were recorded as: 0‰ and 225 µS/m for the Blunder Creek, 0‰ and 430 µS/m for the Stony Creek, 0.5‰ and 678 µS/m for the Bulimba Creek, respectively. Salinity levels at all sites were very similar. But very large differences in conductivity were evident among sites contributing to different osmotic niches (sites are not different in terms of salinity but are remarkably different for total ionic content). Thus, the Blunder Creek population site can be

66 considered to be a pure FW ionic osmotic environment compared with the Stony and Bulimba sites. To confirm species identification, total genomic DNA was extracted from sampled pleopods and screened for a mtDNA CO1 gene fragment following methods outlined in a previous study on

M. rosenbergii (Hurwood et al., 2014). All of the sampled individuals were confirmed to be M. australiense.

3.3.2 RNA extraction, cDNA library preparation and Illumina sequencing

For transcriptomic analyses, 10 individuals were used for Illumina sequencing at the Molecular

Genetics Research Facility (MGRF) under the Central Analytical Research Facility (CARF) at

Queensland University of Technology (QUT). Live prawns were collected from the Bulimba and

Stony creek populations and brought back to the MGRF for RNA extraction. Live individuals were then dissected immediately to obtain fresh tissue to use for high quality RNA extraction. For the

North Queensland population, tissues (gill, antennal gland and hepatopancreas) from live animals were dissected in the field and preserved in RNAlater® solution (Ambion, USA). Due to the limited amount of available tissue material, tissues were pooled together from each individual before RNA extraction. Pooled tissues were crushed into a fine powder with liquid nitrogen and powdered tissues were used for RNA extraction using a TRIzol/chloroform extraction method.

This was followed by total RNA purification using an RNeasy Mini Kit (Qiagen, Germany) according to the manufacturer’s protocol. Total RNA samples were treated with Turbo DNA-free kit (Ambion, Life Technologies) for genomic DNA digestion. Integrity and concentration of extracted total RNA was checked using a Bioanalyzer 2100 (Agilent Technologies, version 6) and a Nano Drop 2000 spectrophotometer (Thermo Scientific), respectively.

RNA samples were stored at -80 °C for later use. In total, 4 µg of total RNA was used as the starting material for mRNA purification, using a Truseq Standard mRNA Sample Prep kit

(Illumina, USA). Purified mRNA was then fragmented into small fragments to initiate cDNA synthesis. After synthesizing both the first and second strands of cDNA, an Illumina barcoding adapter ligation step was performed chemically according to the manufacturer’s protocol. Adapter ligated cDNA was purified using the AMPure XP magnetic bead (Beckman Coulter, Beverly,

USA) system combined with several subsequent washing steps. Quality of constructed cDNA libraries were checked using a Bioanalyzer 2100 (Agilent), Qubit® 2.0 Fluorimeter (Invitrogen,

Life Technologies) and RT-qPCR (BJS Biotechnologies, UK). All libraries were normalized via dilution before the final sequence run. The resulting 10 cDNA libraries (3 for Blunder creek of

North Queensland, 3 for Stony creek and 4 for Bulimba creek) were subjected to the Illumina

NextSeqTM 500 Platform for 75 bp paired end sequencing.

3.3.3 Quality control of Illumina sequence data

High through-put Illumina sequencing produced >542 million raw reads from the 10 cDNA libraries. Initially, quality of sequence data was checked using FastQC software (Andrews, 2010).

Trimmomatic software was then used for quality filtering of raw sequence reads (Bolger et al.,

2014), to trim poor quality sequences at both ends. Only high quality and quality filtered reads

(Phred Score, Q≥30) were used in subsequent bioinformatics analyses.

3.3.4 Bioinformatics analysis

All high quality reads from the 10 cDNA libraries were pooled to make a single reference transcriptome for the three sampled populations. De novo assembly of high quality reads was performed using the Trinity software package (version 2014-04-13p1) applying perl script and default settings (Haas et al., 2013) to obtain contigs. Transcriptome assembly completeness then

68 assessed using CEGMA software (Parra et al., 2007). De novo assembled contigs were then blasted against the NCBI non-redundant database using BLAST+ (version 2.2.29) applying an e value of

1e-5 for significant blast hits. Resulting blasted files were loaded into Blast2GO pro software

(version 3.0) (Camacho et al., 2009) to map and annotate sequences and to determine the potential functions of each transcript based on gene ontology (GO) analysis. Annotated contigs were then resubmitted to Blast2Go pro software for InterProScanning in order to obtain InterProScan domains. The data set was checked carefully to identify and extract candidate genes with any potential role in osmoregulation and/or ion balance maintenance based on GO term categories.

3.3.5 SNP detection

Each cDNA library was mapped individually to the reference transcriptome using BOWTIE2

(Berdan et al., 2015; Langmead and Salzberg, 2012) applying default parameters. We used

PICARD software to mark and remove duplicate reads. De-duplicated reads were then realigned to the reference transcriptome using the software GENOME ANALYSIS TOOLKIT (GATK)

(Auwera et al., 2013; De Pristo et al., 2011) to correct mapping-related artefacts. SNPs were then called using the GATK-module Unified Genotyper, applying default settings from the GATK website. After the primary step of SNP calling we applied several levels of filtering. At the first level, GATK used a threshold of 30 (QUAL > 30) to filter all SNPs based on Phred-scaled quality score. Next, we filtered out variants of more than 10 % (DP ≥ 10) of the reads that had a mapping quality of zero. Then, variants were further removed if any of the following criteria were true: normalized quality score by the amount of coverage was < 2 (QD < 2.0), root mean square for mapping quality across all genotypes were < 40 (MQ < 40.0), Phred-scaled P-value for the Fisher exact test for stand base was > 60 (FS > 60.0) and Haplotype score was > 13.0 (Berdan et al.,

2015).

3.3.6 Outlier detection

Quality filtered SNPs obtained from GATK software were then loaded in VCFTOOLS to convert

SNPs into VCF format and these data were then loaded into the PGDSpider software program

(Lischer and Excoffier, 2012) to convert to appropriate data format for outlier loci detection. The data set was then loaded in BAYESCAN (Foll and Gaggiotti, 2008) software for Outlier detection.

3.3.7 Differential gene expression (DGE) and functional enrichment analysis

For DGE analysis, we estimated transcript abundance initially using RSEM software (Li and

Dewey, 2011). Transcript abundance data were then loaded subsequently into the edgeR

Bioconductor package for final DGE analysis (Haas et al., 2013). The Trinotate software package was used to perform functional enrichment analysis of differentially expressed transcripts

(Brekhman et al, 2015). At first, assembled contigs were blasted locally against three different protein databases: SwissPort, Uniref90 and Pfam. All GO term categories were then extracted for each gene feature including all parent terms using Perl script in Trinotate software. Final enrichment analysis results were obtained in the software package Bioconductor GOSeq that yielded enriched and depleted gene sets at FDR≤ 0.001 (Young et al., 2010).

3.4 Results

3.4.1 Illumina sequencing, de novo assembly and annotation

De novo assembly of high quality Illumina paired end sequences yielded a total of 123,396 non- redundant contigs (≥ 200 bp) with an N50 value of 2423 bp. In total, 30,764 contigs showed significant blast hits and 27,325 contigs received GO terms. Table 3-1 shows the detailed features of the assembly and annotation statistics for the M. australiense libraries. CEGMA (Core

Eukaryotic Genes Mapping Approach) based transcriptome assembly completeness test revealed

97.38% complete mapping, a result that confirms the relatively high quality of the M. australiense transcriptome data set. The top hit species distribution chart in figure 3-1 (supplementary figure) shows the highest top hit matches with a micro-crustacean, Daphnia pulex. In total, 138,438 GO terms were obtained for 30,764 transcripts that had significant blast hits (figure 3-2, supplementary figure shows the top most abundant GO term categories) and the GOs involved in three different processes including: biological process, molecular function and cellular components.

Table 3-1: Assembly, annotation and transcriptome completeness features of M. australiense.

Parameters Results Total Number of Reads 542,062,139 Number of Assembled Contigs 123,396 Number of bases assembled 124,731,462 N50 value 2423 Average contig length 1010.82 Transcriptome completeness 97.38%

Number of contigs blasted 30,764 Number of contigs mapped 27,325 Number of contigs annotated 19,337

3.4.2 Candidate gene identification and differential gene expression (DGE) analysis

In total, 32 different candidate gene families in the M. australiense data set were found to be involved with osmoregulation in this species (Table 3-6, supplementary table). The heatmap in figure 3-4 (supplementary figure) presents the expression patterns of 9,374 differentially expressed transcripts in the three different M. australiense populations sampled here. Table 3-2 shows transcript abundance (in TPM values where TPM stands for transcript abundance per million of reads) based expression pattern of the 15 most important osmoregulatory genes in the 3 sampled populations. The other 17 osmoregulatory genes identified here (Table 3-2) are known to play

either minor or indirect role/s in osmoregulation in the target species. Table 3-3 shows the

expression pattern of the top 20 expressed genes in the three M. australiense populations.

Table 3-2: Abundance estimation (DGE) of the most important 15 osmoregulatory genes between three different populations of Macrobrachium australiense.

Transcript ID Gene Name Bulimba Creek Stony Creek Blunder Creek c57163_g1_i2 Alkaline Phosphatase 416.34 296.47 152.13 c58766_g7_i1 Aquaporin 223.78 227.82 234.62 c58721_g2_i2 Arginine Kinase 1026.41 924.75 915.25 c57014_g1_i1 Calreticulin 586.13 659.97 1052.66 c44335_g2_i2 Carbonic Anhydrase 121.15 112.19 90.83 c51607_g1_i1 Crustacean Cardiovascular Peptide 1038.74 803.93 530.55 c55224_g2_i1 Mitochondrial Carrier protein 6.08 7.86 2.3 c60147_g2_i1 Na+/K+-ATPase 1068.71 573.99 510.96 c57874_g2_i4 Na+/K+/2Cl- Co-transporter 806.37 212.83 103.57 + - c60020_g1_i2 Na /HCO3 Exchanger 667.97 321.88 139.94 c59059_g1_i4 Na+/Ca+2 Exchanger 13.72 15.88 16.6 c59083_g1_i8 Na+/H+ Exchanger 618.89 626.93 1050.53 c46998_g1_i1 Na+/K+/Ca+2 Exchanger 1.95 0.54 0 c50128_g1_i1 Selanophosphate 23.69 26.2 19.4 c57275_g2_i2 V-type H+-ATPase 717.85 795.94 1414.22

Table 3-3: Top 20 genes/transcripts expressed in different populations of M. australiense.

Blunder Creek Population Bulimba Creek population Stony Creek Population Transcript ID Description TPM Transcript ID Description TPM Transcript ID Description TPM c7022_g1 Hypothetical 136595 c7022_g1 Hypothetical 125548 c7022_g1 Hypothetical protein 89473 protein protein c56593_g1 α-actin 26014 c57533_g1 Uncharacterized 40361 c54062_g2 GTPase protein 17611 c57533_g1 Uncharacterized 25104 c58711_g5 Phospholipase 19201 c58434_g3 Ca+2 binding protein 12239 c58434_g3 Ca+2 binding 18046 c55913_g1 α-elongation 14918 c54527_g1 Uncharacterized 11901 protein factor c58711_g5 Phospholipase 13670 c50364_g1 Cytochrome Cii 14190 c50364_g1 Cytochrome Cii 11364 c56593_g3 Actin 13476 c56488_g2 Uncharacterized 10995 c58711_g5 Phospholipase 10142 c57805_g1 Myosin 8117 c56488_g3 Uncharacterized 9836 c59176_g7 Uncharacterized 10058 c52370_g1 Uncharacterized 7821 c52418_g1 Uncharacterized 9754 c59176_g4 Hypothetical protein 9465 c50254_g1 Uncharacterized 7638 c52219_g1 Reproductive 8329 c55913_g1 α-elongation factor 8376 protein (male)

72 c59291_g1 Troponin 7545 c57602_g1 Cytochrome Ci 7810 c57602_g1 Cytochrome Ci 8311 c56488_g3 Uncharacterized 6995 c59871_g1 Receptor protein 7145 c60174_g1 Uncharacterized 6854 c55913_g1 α-elongation 6870 c54612_g1 Cytochrome Ciii 5992 c54612_g1 Cytochrome Ciii 5602 factor c52219_g1 Reproductive 6326 c56593_g1 α-actin 5906 c56097_g1 POU class protein 4259 protein (male) c52418_g1 Uncharacterized 6127 c51222_g1 60S ribosomal 5771 c56593_g1 α-actin 4204 protein c45223_g1 Myosin protein 5478 c47530_g1 Uncharacterized 5633 c57785_g1 Actin 1 4170 c58683_g4 Myosin protein 5234 c49357_g1 Ribosomal protein 5425 c56693_g3 Actin 4085 c53420_g1 40S ribosomal 4761 c60338_g1 Transitional 5398 c53495_g1 Uncharacterized 4047 protein protein protein c47530_g1 Uncharacterized 4626 c7100_g1 34 ribosomal 5390 c53063_g1 Pleckstrin homology 3882 protein protein c50178_g1 37 ribosomal 4507 c38441_g1 18S ribosomal 5099 c51222_g1 60S ribosomal 3773 protein protein protein c7100_g1 34 ribosomal 4441 c50924_g1 35 ribosomal 4977 c57948_g3 Protein inhibitor 3705 protein protein

3.4.3 SNP and outlier detection

Table 3-4 shows the number of raw and high quality SNPs identified in the three sampled M.

australiense populations. Number of raw SNPs was quite high but after several filtering steps, only

248-308 high quality filtered SNPs were obtained among the populations.

Table 3-4: SNP table for three Macrobrachium australiense populations.

Populations Blunder Creek Stony Creek Bulimba Creek Total number of raw SNPs 1292 1351 1501 Total number of filtered SNPs 248 279 308

Figure 3-1 represents the number of common and population specific outlier loci detected for

different populations. Bulimba/Stony comparison showed the lowest number (8) of outliers while

73 the Blunder/Bulimba comparison showed the highest number (17) of population specific outliers.

All populations shared 18 outlier contig (loci) in common.

Fig 3-1: Venn diagram of outlier contigs for different population comparisons (Blunder/Bulimba comparison in blue, Blunder/Stony comparison in yellow and Bulimba/Stony comparison in green).

3.4.4 Functional enrichment analysis

Significantly enriched GO categories for Blunder/Bulimba and Blunder/Stony population pairs are presented in the Table 3-5. In total, 16 different GO categories were significantly enriched for differentially expressed transcripts between Blunder/Bulimba and Blunder/Stony population pairs.

Table 3-5: Enriched GO terms for the Blunder/Bulimba comparison and the Blunder/Stony comparison.

GO-ID Term Category GO FDR P-value for FDR P-value for Level Blunder/Bulimba Blunder/Stony GO:0042302 Cuticle structural MF 23 0.00000 0.00000 constituent GO:0007602 Phosphorylation BP 09 0.00001 0.00001 GO:0018298 Protein BP 07 0.00001 0.00004 chromosome linkage GO:0009881 Receptor activity MF 07 0.00003 0.00001 GO:0005198 Structural MF 23 0.00002 0.00008 molecular activity GO:0006030 Metabolic BP 06 0.00005 N/A process GO:0009583 Stimulus BP 09 0.00006 0.00005 detection

GO:0005856 Cytoskeleton CC 12 0.00008 N/A GO:0008061 Chitin binding MF 06 0.00009 N/A GO:1901071 Glucose BP 06 0.00009 N/A metabolic process GO:0006040 Amino acid BP 06 0.0001 N/A metabolic process GO:0005576 Extracellular CC 12 0.0002 0.0002 region GO:0006022 Aminoglycan BP 06 0.0004 N/A metabolism GO:0031409 Protein binding MF 04 N/A 0.0007 GO:0007165 Signal BP 27 0.0001 0.0001 transduction GO:2000257 Protein activation BP 09 0.0007 N/A

3.5 Discussion The comparative population transcriptomic data set generated here for three populations of M. australiense exposed naturally to different ionic conditions provides an important genomic resource for understanding the functional role of key osmoregulatory genes. Mechanistic aspects of osmoregulation in aquatic environments requires: i) sensing an osmotic stress, ii) ionic regulation via active uptake or release of ions depending on the surrounding medium, iii) active ion transport, transfer signals, regulatory cellular volume increase or decrease depending on external environmental conditions, iv) and activating pathways and specific candidate genes to respond to the stressors. In total, 32 different candidate genes (Table 3-6, supplementary table) were found to be involved with osmoregulatory control in this species. The diversity of genes identified reinforces the complex nature of this trait in the target species. Currently the most important osmoregulatory genes identified in crustaceans are considered to be: Alkaline

Phosphatase, Aquaporin (AQP), Arginine Kinase, Calreticulin (CRT), Carbonic Anhydrase (CA),

Crustacean Cardiovascular Peptide (CCP), Mitochondrial Carrier Protein, Na+/K+-ATPase (NKA),

+ + - + - + +2 + + Na /K /2Cl Co-transporter (NKCC), Na /HCO3 Exchanger, Na /Ca Exchanger, Na /H

Exchanger (NHE), Na+/K+/Ca+2 Exchanger, Selanophosphate and V-type H+-ATPase (VTA) (Ali et al., 2015; Faleiros et al., 2010; Freire et al., 2008; Gao, 2009; Gillanders et al., 2003; Henry et al., 2003; Kotlyar et al., 2000; Luana et al., 2007; Tsai and Lin, 2007). These important osmoregulatory genes are well studied in a range of crustacean species and are known to be involved with: ion transport, ion exchange, cell volume control, signal transduction, activating pathways and sensing osmotic stress (Ali et al., 2015; Freire et al., 2008; Genovese et al., 2005;

Stillman et al., 2008; Faleiros et al., 2010). Other candidate genes that have a minor or indirect role in adaptive responses however, have remained unstudied or are only poorly characterised.

Table 3-2 showed the expression patterns (in terms of transcript abundance per million reads,

TPM) of the osmoregulatory genes that are considered to be the most important. More than 50 %

(8 out of 15) of the most important candidate gene families showed similar expression patterns (no large variation in expression pattern were detected) among the sampled populations. In contrast,

+ - Calreticulin, CCP, NKA, NKCC, VTA, NHE and Na /HCO3 exchanger showed large differences in expression among the three sampled M. australiense populations indicating that they likely play more important roles in osmotic adaptive responses in M. australiense under different natural ionic

+ - conditions. NKA, NKCC, VTA and Na /HCO3 exchanger are the main genes that are directly involved with active uptake and release of a range of ions used to maintain ion balance and achieve ion regulation (Barman et al., 2012; Faleiros et al., 2010). VTA is also involved with cellular volume control apart from maintaining ionic balance and is expressed at higher levels under pure freshwater conditions (Stillman et al., 2008). Calreticulin acts as a salinity stress biomarker and transfers osmotic signals while providing energy to address osmoregulatory stress (Barman et al.,

2012). We observed highest expression level for VTA, NHE and Calreticulin in the Blunder Creek population (exposed to pure, low ionic freshwater conditions) compared with the other two

76 populations. Potentially, this is because osmoregulation for M. australiense individuals is more energy expensive under pure low ionic freshwater conditions than in brackish water, and raised expression levels of Calreticulin may provide the additional energy required to address this challenge. In addition, higher expression level of NHE likely plays a pivotal role in absorbing ions from water and releasing H+ into water in Blunder Creek, an environment characterized by low ionic conditions. Another major challenge in low ionic freshwater conditions is to maintain cell volume and resist ion loss from the body and higher expression of VTA in this regard may assist in this process. These three gene families obviously play more important roles in M. australiense osmoregulation under freshwater conditions compared with their individual role(s) in brackishwater.

+ - CCP, NKA, NKCC and Na /HCO3 exchanger genes showed highest expression levels in the

Bulimba Creek population which indicates an important role for these four genes in salinity

+ - tolerance. NKA, NKCC and Na /HCO3 exchanger genes play a critical role in osmoregulation under brackishwater or under higher osmotic gradient conditions by actively pumping out excess ions gained from surrounding water (Ali et al., 2015; Anger 2003, Barman et al., 2012). Thus, individuals in Bulimba Creek may overcome the problem of gaining excess ions by expressing these three gene families at higher levels. In contrast, individuals also need to regulate their body fluid (haemolymph) concentration under a changing osmotic gradient. Relatively high expression levels of CCP in M. australiense individuals under brackishwater conditions potentially may promote production of more haemolymph (Zhao et al., 2015) that allows individuals to deal with this osmotic challenge.

The top 20 differentially expressed genes among the sampled populations in Table 3-3 shows that both specific mitochondrial and nuclear genes (Actin, Cytochrome, 18S, 37S, 40S, 60S RNA etc.)

77 were all highly expressed. Not all highly expressed genes in all populations were involved with osmoregulatory activities, even though they were exposed to different osmotic conditions. The role(s) of other genes that do not have a primary osmotic role and are likely to be involved with house-keeping activities specific to this species. Functional enrichment analysis of all differentially expressed genes (Table 3-5) revealed an absence of enriched GO categories in the

Bulimba/Stony comparison while significant enrichment was observed in both the

Blunder/Bulimba and Blunder/Stony comparisons. FW prawns collected from Bulimba and Stony

Creeks showed similar expression patterns and as a result no significantly enriched GO categories were detected. Significant differences were observed however, in comparisons of gene expression patterns between individuals from Blunder/Stony and Blunder/Bulimba population pairs. As a result, there were significantly enriched GO categories identified in these two comparisons. This distinction most probably reflects a more distant phylogeographical relationship between the

Blunder Creek population with the other two populations (separated geographically by >1800 kms).

Transcriptome wide SNP calling revealed 4,144 raw and 835 high quality filtered SNP variants among the 3 sampled populations (Table 3-4). Figure 3-1 shows both population specific outliers and common outlier loci in population comparisons. In total, 84 contigs were found to show outliers in at least one comparison. Nearly half of the outlier contigs (40 out of 84) were unique to only a single population comparison and 18 contigs were found to be outliers in all three comparisons. Considerably higher numbers of outliers were obtained when comparisons involved the Blunder Creek population. This confirms that the Blunder Creek population was more often different for gene expression patterns for potentially adaptive loci. In part, at least this may result from geographical isolation between the north (Blunder) and the two southern sampling sites

(Bulimba and Stony), but also likely reflects impacts of different osmotic environments. A relatively high number (18) of common outliers in all comparisons potentially indicate that genetic response to freshwater invasion of inland waters has occurred under strong selection pressure on specific expressed genes influencing osmoregulation in the newly invaded environment. Bulimba and Stony Creek are both tributaries of the Brisbane River and share many common environmental features as they are members of the same major catchment. While Bulimba and Stony populations of M. australiense have been physically isolated from each other over the last 80 years due to the dam construction, this time has not been sufficient from an evolutionary time perspective for novel adaptive mutations to develop.

Identification of outlier loci under selection can provide important insights for understanding local adaptation and speciation under different environmental or ecological conditions (Rundle and

ATPase, V-type-(H+)-ATPase and Calreticulin. These four genes are considered to be the major candidate genes involved with osmoregulation in a wide range of aquatic crustacean species. We compared sequences of the outlier contigs/genes between the different sampled populations to determine the physical locations of identified oulier SNPs (the mutations could either be in coding regions of the gene or in untranslated regions, UTRs). All outlier SNPs in the 4 important osmoregulatory genes in M. australiense were found to be present in UTR regions (outside protein coding regions) and were located prior to the start codon region. This suggests that they are likely to be regulatory mutations that control gene expression patterns of the functional genes.

Osmoregulation is a complex physiological trait and the genomic control of adaptive response under different osmotic conditions can be mediated by changes in gene expression pattern initially

(i.e. genes regulation) but longterm adaptation to a changed osmotic niche is more likely to involve adaptive mutations in functional parts of a gene. Here, we found most mutations (outliers) in M. australiense were located outside of coding regions (i.e. in UTRs), suggesting that osmoregulatory control under different environmental conditions is not controlled by changes in amino acid sequence in M. australiense, rather it is controlled by regulatory mutations that alter expression patterns of essentially identical functional genes. This implies that adaptation by M. australiense to various osmotic niches is likely to be an ongoing process and is currently largely controlled by changes in gene expression under different environmental conditions. M. australiense in general, can tolerate a wide range of variation in sub lethal salinity conditions naturally and so would unlikely be impacted severely by any climate change effects including long term changes in local salinity levels. The more critical issue however, will be to determine how quickly individuals can respond and whether they experience stress during this process that may affect their normal growth and development.

3.6 Supplementary files

Fig 3-2: Top hit species distribution chart (absolute values of blast match hits with different species).

Fig 3-3: Top most abundant GO term categories for different mechanisms: a) biological process, b) cellular component and c) molecular function (absolute number of expressed transcripts assigned to each GO categories).

Fig 3-4: Expression pattern of the differentially expressed transcripts for individuals collected from different populations (the red coloured transcripts are highly expressed or upregulated and the green coloured transcripts are downregulated). BuC = Bulimba Creek, StC = Stony Creek, BlC = Blunder Creek

Table 3-6: Summary of the identified candidate genes potentially associated with osmoregulation in M.

australiense from this study. (GenBank Accession Numbers will be available upon acceptance of the

paper).

Transcript ID Transcript/Gene Transcript Amino Acid Functional Roles of the Genes Based GenBank Name Length (bp) Length on Gene Ontology (GO) accession # c57163_g1_i2 Alkaline phosphatase 2684 548 phosphatase activity; metabolic process c53495_g1_i4 Arginine kinase 1856 436 ATP binding; kinase activity; phosphorylation c59694_g2_i2 Aquaporin (AQP) 4809 318 substrate-specific transmembrane transporter activity; transmembrane transport c55960_g1_i1 ATP-binding cassette 7159 1849 protein binding; cellular process; regulation of biological process; response to a stimulus c44335_g2_i2 Carbonic anhydrase 1444 467 carbonate dehydratase activity; zinc ion (CA) binding c57014_g1_i1 Calreticulin 1728 404 calcium ion binding

c59009_g2_i1 H+ transporting ATP 3809 604 proton-transporting ATP synthase synthase activity, ATP synthesis coupled proton transport c59162_g1_i2 Integrin 7438 1761 cell adhesion, stress response

c43728_g2_i1 Interleukin 2348 538 cellular response to unfolded protein c57027_g1_i1 Mitochondrial carrier 2758 310 Ion transport

c60147_g2_i1 Na+/K+-ATPase alpha 5197 1009 sodium, potassium-exchanging subunit potassium ion transport; sodium ion transport; cation transmembrane transport c54650_g4_i2 P38 map kinase 1550 365 MAP kinase activity; ATP binding; protein phosphorylation c57275_g2_i2 V-type proton (H+)- 4984 840 proton-transporting V-type ATPase, ATPase proton-transporting ATP synthase activity, proton-transporting ATPase activity, ATP hydrolysis coupled proton transport c54022_g1_i1 USP6 n-terminal-like 2310 640 regulation of GTPase activity

c58582_g2_i1 Sodium Hydrogen 6805 663 an integral component of membrane; Exchanger sodium: proton antiporter activity, sodium ion transport; regulation of pH; inorganic cation transmembrane transport c60020_g1_i4 Sodium bicarbonate 4475 1191 sodium, bicarbonate symporter cotransporter activity; anion, anion antiporter activity; sodium ion transport; chloride transport; bicarbonate transport

84 c59709_g1_i1 Sodium potassium 4882 1066 sodium, potassium, chloride symporter chloride cotransporter activity; sodium, chloride symporter activity; amino acid transmembrane transport; potassium ion transport; rubidium ion transport; chloride transmembrane transport c50128_g1_i1 Selenophosphate 2205 326 Involved in stress tolerance (SPS) c54700_g2_i1 Sodium myo-inositol 1775 410 Ion transporter, transmembrane cotransporter transport c45470_g2_i1 Sodium channel 1907 385 regulation of ion transmembrane protein 60e transport; sodium ion transmembrane transport regulation of ion transmembrane transport c59059_g1_i2 Sodium Calcium 6552 846 integral component of membrane; Exchanger 1 calcium, sodium antiporter activity; calcium ion transport; cell communication; transmembrane transport c51905_g1_i2 Selenoprotein s 1955 209 regulation of biological process; regulation of cellular process; response to stimulus c57071_g1_i2 Potassium voltage- 4762 618 potassium ion transport; gated channel protein transmembrane transport; signal shaker-like isoform 1 transduction by phosphorylation c45473_g1_i1 Potassium Sodium 1735 423 voltage-gated potassium channel hyperpolarization- activity; potassium ion transmembrane channel transport c58346_g1_i2 Potassium channel 3813 640 potassium channel activity; potassium subfamily K member ion transmembrane transport 16-like c52212_g1_i2 Mitochondrial 2243 296 response to oxidative stress and stress transcription factor tolerance isoform a c52854_g1_i1 Magnesium 3140 326 magnesium ion transmembrane transporter protein 1- transporter activity like c58746_g1_i2 Heat shock protein 90 3460 726 response to stress c56292_g1_i1 Chorion peroxidase 4116 922 response to oxidative stress; oxidation- reduction process c57861_g1_i4 Chloride channel 5229 826 chloride transport; ion transmembrane protein 3 transport; regulation of anion transport c53950_g2_i2 Calcium-binding 3224 697 transport; biological regulation, mitochondrial carrier regulation of cellular process protein 1 c58142_g8_i2 Bestrophin isoform a 2637 370 chloride transport; cellular water homeostasis

1. Moshtaghi et al. (submitted). An investigation of gene expression patterns that contribute to effective osmoregulation in Macrobrachium australiense: assessment of adaptive responses to variable osmotic gradients. Manuscript submitted in the Hydrobiologia.

An investigation of gene expression patterns that contribute to effective osmoregulation in

Macrobrachium australiense: assessment of adaptive responses to variable sub-lethal osmotic

conditions

Authors: Azam Moshtaghi*, Md. Lifat Rahi, Peter B. Mather and David A. Hurwood

Queensland University of Technology, Science and Engineering Faculty, Earth Environment and

Biological Sciences School

*Corresponding author: Azam Moshtaghi, PhD Candidate, Queensland University of Technology,

Science and Engineering Faculty, School of Earth Environment and Biological Sciences, 2 George Street,

Brisbane, Queensland 4001, Australia. E-mail: [email protected]

Md. Lifat Rahi, PhD Candidate, Queensland University of Technology, Science and Engineering Faculty,

School of Earth Environment and Biological Sciences, 2 George Street, Brisbane, Queensland 4001,

Australia. E-mail: [email protected]

Peter B Mather, Adjunct Professor, Queensland University of Technology, Science and Engineering

Faculty, School of Earth Environment and Biological Sciences, 2 George Street, Brisbane, Queensland

4001, Australia. E-mail: [email protected]

David A Hurwood, Senior Lecturer, Queensland University of Technology, Science and Engineering

Faculty, School of Earth Environment and Biological Sciences, 2 George Street, Brisbane, Queensland

4001, Australia. E-mail: [email protected]

An investigation of gene expression patterns that contribute to effective osmoregulation in

Macrobrachium australiense: assessment of adaptive responses to variable sub-lethal

osmotic conditions

4.1 Abstract The present study was conducted to assess osmoregulatory performance and gene expression patterns of five candidate osmoregulatory genes in a euryhaline Palaemonid prawn species,

Macrobrachium australiense, exposed to three sub-lethal salinities across six different time intervals up to 120 hours, using qRT-PCR. Relative gene expression patterns revealed comparatively higher expression levels of Na+/K+-ATPase (NKA) and Na+/K+/2Cl- Co-transporter

(NKCC) genes at raised sub-lethal salinities (5‰ and 10‰). At 0‰ vs 10‰, expression levels of these two genes were significantly higher (p < 0.05) and showed 2-3 fold and 4-6 fold higher

(higher in 10‰ than 0‰) expression levels for NKA and NKCC, respectively. Significantly different expression levels (p < 0.05) were also observed for the Na+/H+ exchanger (NHE) gene among salinity treatments up to 24 hours, after which expression levels remained essentially constant across all treatments. A similar pattern was evident for the V-type H+-ATPase (VTA) gene over the same time frame (significant differences up to 24 hours and then constant subsequently). The water channel regulatory gene, Aquaporin (AQP) showed very high expression levels initially (1 hour) that were significantly different among treatments (p < 0.05), gene expression levels then declined subsequently to the end of the experiment (120 hour). Results of this study indicate that the target genes examined here are likely to play key roles in adaptive responses to variable environmental salinity conditions in the wild.

Key words: Palaemonid prawn, gene expression, osmoregulation, M. australiense, qRT-PCR

4.2 Introduction

Environmental salinity concentration in aquatic environments is considered to be one of the most important abiotic factors that determine species boundaries, their natural distributions and their potential for adaptive radiation (Anger, 2013). Variation in salinity levels can lead to osmotic stress that potentially may alter osmotic concentrations in aquatic animal tissues and this can affect adversely natural distributions, physiology and long-term persistence of organisms (Vogt, 2013).

Osmotic stress has been considered to be an acute stress and in multi-cellular organisms it can be controlled by a number of functional genes in specific target tissues that prepare individuals to cope with external changes in environmental salinity (Boudour-Boucheker et al., 2016). During acclimation to osmotic stress, significant changes can occur in target aquatic organisms, including changes to protein structure and changes in permeability and function of gill epithelium cells; this can modify expression patterns of key genes that regulate internal body osmotic conditions (Nadal et al., 2011; Barman et al., 2012).

A vast number of crustacean taxa are well adapted to environmental osmotic challenges because many regularly transit between fresh and saline water environments, and are therefore able to osmoregulate successfully under a wide range of environmental salinities (Vogt, 2013). Recent studies of some crustacean taxa have revealed specialized attributes of their physiology and morphology that allow successful adaptation to variable sub-lethal saline environments (Freire et al., 2008; McNamara et al., 2015; Mykles and Hui, 2015). Specialized gill features have evolved in many crustacean taxa in response to associated selection pressures (e.g. lack or excess of ions in the external medium) that allow active transport and ion exchange against any osmotic gradient

(Lee et al., 2011; McNamara and Faria, 2012).

Paleamonid prawns are one of the most successful crustacean groups that have colonized freshwater environments multiple times during their evolutionary history and that can show efficient osmoregulatory performance under a wide range of salinity conditions. This group of prawns is known to have originated from an ancestral tropical marine clade that has shown a worldwide evolutionary tendency to colonize freshwater after first successfully invading estuarine environments (Augusto et al., 2009; Anger, 2013; Boudour-Boucheker et al., 2016). One of the most diversified and species rich genus in the palaemonid group is the genus Macrobrachium that includes approximately 258 extant species, globally (Wowor et al., 2009; Pileggi and Mantelatto,

2010). Macrobrachium spp. display a wide variety of life history strategies and occur in a diverse array of aquatic habitats and naturally occupy a wide range of environments (including salinities).

They also show a wide variety of behavioural patterns that allow them to deal with changing external environments (Jalihal et al., 1993; Murphy and Austin, 2005). Thus, Macrobrachium taxa provide excellent models for investigating the genomic basis of effective osmoregulation in aquatic crustaceans.

As a model for the current study, Macrobrachium australiense is appropriate for exploring the functional genomic basis of adaptive responses to variable environmental salinity conditions. This is the most widespread freshwater prawn species across mainland Australia and is also one of the few that has adapted successfully to a fully freshwater life style (Dimmock et al., 2004). Natural populations can also be found in brackish water (upper regions of estuaries) and under laboratory conditions they can tolerate salinity up to 17‰ (Sharma and Hughes, 2009). Moreover, they possess a moderate body size that can provide sufficient tissue material from different organs to investigate tissue specific functional roles in osmoregulation under a variety of environmental conditions. Gill tissue from M. australiense in particular provides an ideal target tissue (because

90 genes for ionic regulation are highly expressed there) to understand genomic responses (expression patterns of osmoregulatory genes) under a variety of environmental conditions (various salinity levels).

Recent genomic technological advances now offer great power for investigating the molecular basis of a variety of important ecological and evolutionary research questions at fine detail. In particular, a real time quantitative polymerase chain reaction (qRT-PCR) approach is a highly cost effective and reliable method for measuring gene expression patterns of specific candidate genes under different environmental conditions to better understand the functional genomic basis of a specific adaptive response. In a previous study that used a next generation sequencing (NGS) approach, 32 potential candidate genes were identified that are likely to play a role in osmoregulation and salinity tolerance in M. australiense (Moshtaghi et al., 2016). To date, the most important osmoregulatory genes in crustaceans are reported to be: Na+/K+-ATPase (NKA),

V-type H+-ATPase (VTA), Na+/H+ exchanger (NHE), Na+/K+/2Cl- Co-transporter (NKCC),

+ - Aquaporin (AQP), Na /HCO3 Co-transporter (NHC), Carbonic Anhydrase (CA), Claudin and

Calreticulin (Barman et al., 2012; Ali et al., 2015; Boudour-Boucheker et al., 2016; Moshtaghi et al., 2016). In general, the functional roles of these genes involve active ion transport and exchange, tolerance of osmotic stress, water channel maintenance and regulatory cell volume control. There are also a number of additional osmoregulatory genes but they are considered to play minor roles in adaptive responses to salinity changes in crustaceans (Nadal et al., 2011).

In the current study, a qRT-PCR based gene expression study was conducted on five osmoregulatory genes (NKA, NKCC, NHE, AQP and VTA) that are considered to be the major genes involved with adaptive response to variation in external salinity levels in crustacean taxa.

The objectives of the study were to measure relative gene expression levels of the five genes in M.

91 australiense exposed to three experimental sub-lethal salinity levels (0‰, 5‰ and 10‰) at different time intervals over a period of five days, to understand the potential roles that they play in the adaptive response.

4.3 Methodology

4.3.1 Sample collection and maintenance

Specimens of the target species (M. australiense) for this study was collected from Samford Creek

(27°23′22 S, 152°52′37 E) located at the SERF (Samford Ecological Research Facility) of QUT

(Queensland University of Technology). Water salinity, conductivity and temperature at the sampling site were 0‰, 448 µS/cm and 23ᴼ C, respectively at time of collection. In total, 183 live adult prawns varying in size (3-7 g in total body weight) were collected using a 3m seine net.

Individuals were then transferred in plastic containers with aeration to the Banyo Pilot Plant

Precinct of QUT. 24 rectangular 25L glass fishtanks were used for this experiment. Tanks were filled initially with 8L of tap water and sampled individuals maintained under continuous aeration for 5 days, removing any chlorine from the water. Natural creek water was sourced from the same location where the prawns were sampled and added to each tank to minimize stress, bringing the total volume to 20L per tank. Six to eight prawns (depending on individual size) were allocated randomly to each tank. Prawns were then acclimated to tank conditions over 14 days prior to the commencement of the salinity stress experiment. Individuals in each tank were fed once a day (late afternoon) with commercially available aquarium pellets (Ocean NutritionTMFormula 2).

4.3.2 Salinity stress experiment and tissue collection

Saline water was prepared to 40‰ by mixing sea salt (Tropic Marine® Pro-Reef) with de- chlorinated tap water. Saline water was then added slowly to each tank to establish three experimental salinity levels. Three different salinities (0‰, 5‰ and 10‰) were maintained for 92 this experiment with eight replicate tanks (total 24 tanks) used for each treatment. Salinity was raised by 2.5‰ per day in the 16 tanks to bring salinity levels up to 5‰ and 10‰, respectively

(increase in salinity for 5‰ tanks started two days after the 10‰ tanks to attain final treatment conditions simultaneously). The remaining eight tanks (0‰ salinity) were considered to reflect normal freshwater conditions (control) for the prawns. Gill tissue was collected from individuals at different time intervals after exposure to salinity stress. There were six collection times (T1-1hr,

T2 - 6hr, T3 - 24hr, T4 - 48hr, T5 -72hr and T6 - 120hr) over a 5 day period. Seven individuals were sampled (7 biological replicates) randomly from each salinity treatment for gill tissue collection at each sampling time. Gill tissue was preserved separately for each individual in

RNAlater® solution (Applied Biosystems, Warrington, UK) immediately after sampling.

Preserved tissues were kept at room temperature for 1 hour and then brought back to Molecular

Genetics Research Facility (MGRF) at Queensland University of Technology (QUT) where the tissues were preserved at -80ᴼ C temperature prior to use.

4.3.3 RNA extraction and cDNA synthesis

Preserved tissues were removed from -80ᴼ C and thawed on ice. Gill tissue was then removed from RNAlater® solution and transferred to mortars filled with liquid nitrogen. Tissue was crushed to a fine powder using pestles that helped to maximize RNA yield. Powdered tissues were then used for total RNA extraction using a Trizol/Chloroform method (Chomczynski and Mackey,

1995). This included purification via an RNeasy Mini Kit (QIAGEN, Germany) according to the manufacturer’s protocol. Genomic DNA was then removed from total RNA samples via a DNA digestion step using a TURBO DNA-freeTM kit (Ambion, Life Technologies, USA). Quality

(integrity and concentration) of extracted total RNA was checked with a Nano Drop 2000

Spectrophotometer (Thermo Scientific). Complementary DNA (cDNA) synthesis was performed

93 from total RNA (1 µg of total RNA was used for each sample) using a SensiFAST cDNA synthesis kit (Cat # BIO-65054, Bioline, UK) according to the manufacturer’s protocol.

4.3.4 Primer design and quantification of gene expression via real time quantitative PCR

(RT-qPCR)

Gene sequences for the five selected osmoregulatory genes (Aquaporin, Na+/H+ exchanger,

Na+/K+-ATPase, Na+/K+/2Cl- Co-transporter and V-type ATPase) and an 18S RNA gene sequence from M. australiense were available from an earlier study (Moshtaghi et al., 2016) to design specific primers for the current study (Table 4-1). Here, we used the 18S gene as a house keeping

(Boudour-Boucheker et al., 2016) gene to obtain relative gene expression values for each of the five osmoregulatory genes examined here. Primers for each gene were designed using Primer3 software (Untergasser et al., 2012).

Table 4-1: Specific primers used in the current study (after Moshtaghi et al., 2016).

Gene Name Primer Type Sequence Tm Product Size (bp) (ᴼ C) Aquaporin (AQP) Forward CTTCGCCTTCGGAGTCAC 55.7 193 Reverse GGATTCACCGTCGTCATACC 55.3 Na+/H+ exchanger Forward GTGGATCTTCTGCGCTTGTT 55.7 204 (NHE) Reverse CCAGGTGGTCGAAGAAGAGT 56.4 Na+/K+-ATPase Forward ACTGCTCTTGTGGATTGGTG 55.3 215 (NKA) Reverse GTCTTCTGCCTGCACATTCT 53.3 Na+/K+/2Cl- Co- Forward GGGTCACCAGGGTCCAGAT 59.1 180 transporter (NKCC) Reverse TAGCACCAGCAACAATTCCA 54.7 V-type H+-ATPase Forward GGCAACTTTTGAGAAACTTGAAA 52.5 197 (VTA) Reverse CCAGCAACAAATCCAATGTG 52.6 18S Forward GCGGTAATTCCAGCTCCA 55.0 200 Reverse AGCCTGCTTTGAGCACTCTC 57.6

Reactions were performed in a 20 µl reaction mix that included: 3 µl ultra-pure distilled water

(Invitrogen, USA), 5 µl cDNA (template), 1 µl forward primer, 1 µl reverse primer and 10 µl 2x

SensiFAST SYBR No-ROX Mix (Bioline, UK). Reaction mixtures were then transferred to the thermal cycler R-Corbett (RG-6000, Australia). Initially, reaction conditions included polymerase

94 inactivation at 95ºC for 2 minutes and then 40 cycles of: denaturation at 95ºC for 5 minutes, annealing at 60ºC for 20 seconds and extension at 72ºC for 20 seconds. Quantitative gene expression values for each gene from each sampled individual were obtained in terms of Ct (cycle threshold) values. Ct values were then used to obtain relative gene expression values using the

-[∆Ct sample - ∆Ct control] delta-delta Ct method (Pfaffli, 2001) that applies the equation: R = 2 . To compare relative gene expression patterns at different salinities, we analyzed gene expression values with Minitab software (Version 17) to test for statistical differences in gene expression pattern in three different salinity treatments for each gene. We used two-way analysis of variance

(ANOVA) applying the 0.05 level of significance.

4.4 Results The present study quantified relative gene expression patterns (using an RT-qPCR approach) of five selected osmoregulatory genes for an endemic, Australian freshwater prawn species, M. australiense. Expression patterns relative to the 18S RNA housekeeping gene were measured for selected candidate genes under three different salinity treatments over five days. Of the five analyses, all returned a significant two-way interaction except for NHE where no significant differences were recorded among salinity treatments across the duration of the experiment (Fig. 4-

3).

4.4.1 Na+/K+-ATPase (NKA) expression

The results of the two-way ANOVA showed that expression levels of NKA showed a significant interaction between salinity level and duration of exposure to saline conditions (F (5, 120) = 41.3, p=0.002 < 0.05). Levels of NKA gene expression were relatively high under raised salinity conditions (5‰ & 10‰) at T1. In the 10‰ treatment expression levels continued to increase until

T2 when levels plateaued after which there appeared to be a slight downward trend to T6 (Fig. 4-

1), but this was not statistically significant.

4.4.1.1 Within sampling times

The 5‰ treatment showed significantly higher NKA expression compared with the 0‰ treatment at all times (p<0.05), indicating that some degree of osmoregulatory stress may be occurring at a relatively low raised salinity level. Significant differences in relative gene expression pattern were also observed between the 10‰ treatment and both lower salinity conditions across the complete duration (T1 to T6) of the experiment (p < 0.05); approximately 2-3 fold higher than in the 0‰ treatment and 1.4-2.2 fold higher than in the 5‰ treatment.

4.4.1.2 Between sampling times

While NKA expression level in the 0‰ and 5‰ treatments did not vary significantly across the whole experiment (T1-T6). Expression levels in the 10‰ treatment however, increased significantly (p<0.05) from T1 to T2 (from approximately 2-fold to 3-fold) and then remained high for the remainder of the experiment.

3.5 0‰ 5‰ 10‰ D D D D D 3 c c c c c 2.5 C B 2 c B b B B B B b b b b 1.5 A b A A A A a a a A a a 1 a

Relative Gene Gene RelativeExpression 0.5

0 T1 T2 T3 T4 T5 T6 Sampling Time

Fig 4-1: NKA (Na+/K+-ATPase) mRNA expression patterns (relative to 18S expression) in the gill of M. australiense for three salinity treatments over a five day period. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times).

4.4.2 Na+/K+/2Cl-Co-transporter (NKCC)

There was a significant interaction between salinity treatment and sampling time for the NKCC gene (F (5, 120) = 62.5, p=0.000 < 0.05). As with NKA, expression at 0‰ did not change, while in the 5‰ treatment they increased to T3 following which they declined. As for NKA, the 10‰ treatment showed significantly higher expression, a difference that was maintained across the whole experimental period (Fig. 4-2).

4.4.2.1 Within sampling times

Significant differences (p<0.05) were observed in NKCC expression levels across the experimental period between treatments (0‰ vs 5‰, 0‰ vs 10‰ and 5‰ vs 10‰). While consistently higher NKCC expression was observed at 10‰ for the whole experimental period, with up to ~6 fold higher than was observed for freshwater (0‰), the degree of difference in expression level between 5‰ and 10‰ never exceeded ~1.5 fold.

4.4.2.2 Between sampling times

In the 5‰ and 10‰ salinity treatments, gene expression levels increased to T3 (1.7 and 2-fold, respectively) following which they gradually declined to the end of the experiment.

2.5 0‰ 5‰ 10‰ D DE DE CDE F c c CE 2 c c b c C B c B 1.5 b B b B B b b b 1 A A A A A A a 0.5 a a a a

Relative Gene RelativeGene Expression a

0 T1 T2 T3 T4 T5 T6 Sampling Time

Fig 4-2: Relative NKCC (Na+/K+/2Cl- Co-transporter) mRNA expression levels in M. australiense in 3 salinity treatments. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times).

4.4.3 Na+/H+ exchanger (NHE)

4.4.3.1 Within sampling times

Expression levels of the NHE gene were in relative terms, quite low in all three salinity treatments

(Fig. 4-3). Initially at T1, expression levels of this gene were high in both 5‰ and 10‰ salinity treatments relative to the 0‰ treatment, but levels slowly declined over time until no differences were evident among treatments by T6. NHE expression levels were significantly different between the 5‰ and 10‰ treatments only up to T3 after which they were not significantly different to the end of the experiment.

4.4.4 Between sampling times

There was no significant difference in NHE expression pattern among treatments (0‰, 5‰ and

10‰) over the sampling period (T1 to T6).

1.4 C 0‰ 5‰ 10‰ C b C 1.2 b B B c C C C B B B 1 A a b b B b A a a A a A b ab a A A a 0.8 a a a a 0.6

0.4

Relative Gene RelativeGene Expression 0.2

0 T1 T2 T3 T4 T5 T6 Sampling Time

Fig 4-3: Relative NHE (Na+/H+ exchanger) mRNA expression patterns in the gill tissues of M. australiense in 3 salinity treatments. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times).

4.4.5 Aquaporin (AQP)

4.4.5.1 Within sampling times

Very high levels of AQP expression patterns were observed at the first sampling time (T1) in the

5‰ and 10‰ treatments, after which expression levels declined rapidly by T2 and then remained constant (but trending down) until the end of the experiment (Fig. 4-4). Significant differences (p

< 0.05) in relative AQP expression levels were observed between the 0‰ and 10‰ treatments up to T4 following which (T5 to T6) gene expression levels among treatments were not significantly different. Although the difference was not significantly different (p > 0.05), for the first time, a

99 higher expression pattern was observed in the freshwater condition compared with the 5‰ salinity treatment from T4 to the end of the experiment (T6).

3.5 C 0‰ 5‰ 10‰ 3 c B 2.5 b E E 2 D b D b b E E E b 1.5 A b a A a A A D A D D A a ab 1 a a a ab b b a

Relative Gene RelativeGene Expression 0.5

0 T1 T2 T3 T4 T5 T6 Sampling Time

Fig 4-4: Relative AQP (aquaporin) mRNA expression levels in M. australiense at 3 salinity treatments.

Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at α=0.05 level

(lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times).

4.4.5.2 Between sampling times

Significantly higher AQP expression was observed in both the 5‰ and 10‰ treatments only at T1

(p<0.05) compared to the rest of the sampling periods (T2 to T6); while no significant difference was evident between the 5‰ and 10‰ treatments from T2 to T6. We did not observe any significant difference in AQP expression levels at 0‰ across the experimental period.

4.4.6 Vacuolar-type H+-ATPase (VTA)

4.4.6.1 Within sampling times

Expression results showed that VTA expression levels increased in the 5‰ and 10‰ treatments from T1 to T3, following which they then had declined by T4 and then all remained essentially

100 constant at the same level as the 0‰ treatment to the end of the experiment (Fig. 4-5). Significant differences (F (5, 120) = 27.1, p = 0.028 < 0.05) were observed in VTA expression levels between the 0‰ and 10‰ treatments from T1 to T3 with 1.5 to 3 fold higher expression levels observed in the 10‰ treatment compared with freshwater (0%). Significant differences (p < 0.05) were also observed between the 0‰ and 5‰ treatments (1.2 to 1.4 fold different) from T1 to T3.

2 0‰ 5‰ 10‰ 1.8 D D 1.6 C c c B b B 1.4 B C C C b b 1.2 b A B a A B a B a A A A A a a a 1 a a a a a a 0.8 0.6

0.4 Relative Gene RelativeGene Expression 0.2 0 T1 T2 T3 T4 T5 T6 Sampling Time

Fig 4-5: Relative VTA (V-type H+-ATPase) mRNA expression levels in 3 salinity treatments. Error bars represent +/- 1 SE. Different letters above bars indicate significant difference at the α=0.05 level (lowercase letters indicate differences among treatments within sampling time; uppercase letters indicated significant differences within treatments between sampling times).

4.4.6.2 Between sampling times

The highest VTA expression levels were obtained between T2 and T3 in the 10‰ treatment. There was no significant difference between VTA expression levels in the 0‰ and 5‰ treatments across the whole experimental period (T1 to T6).

4.5 Discussion As expected, gene expression levels of five target osmoregulatory genes did not show the same patterns and were affected differently by both sampling time and salinity treatments. Gene

101 expression patterns for the NKA gene were consistent among treatments across the whole experimental period (T1 to T6) with relative expression levels ranked as 10‰ > 5‰ > 0‰, showing a clear functional role of this gene in dealing with sub-lethal salinities above freshwater

(control conditions) (Fig. 4-1). The functional role of this gene is to allow active transport and absorption of Na+ and other cations into cells in freshwater, while at raised salinities this gene is considered to drive the reverse process where it pumps ions actively from the body (Freire et al.,

2008; Henry et al., 2012; McNamara et al., 2015). NKA is a well-studied gene (more than any other crustacean osmoregulatory gene) and studies have already been conducted on a wide range of crustacean species to identify, characterize and to understand the functional roles of this gene under different environmental conditions: crab (Genovese et al., 2005; Furriel et al., 2010; Leite and Zanotto, 2013), lobster (Havird et al., 2013), crayfish (Gao and Wheatly, 2004; Ali et al.,

2015), freshwater prawn (Faleiros et al., 2010; Barman et al., 2012; Boudour-Boucheker et al.,

2016) and marine shrimp (Pongsomboon et al., 2009; Chen et al., 2016). The general consensus is that NKA is considered to be a master gene for maintaining ionic balance in both freshwater and raised salinity conditions but it plays a more active role in raised salinity conditions to assist maintenance of internal osmotic balance. Specifically, Barman et al. (2012) observed 2 to 3.5 fold higher NKA expression under saltwater conditions compared with freshwater depending on the intensity of salinity stress in M. rosenbergii. Results of our study correlate well with findings of previous studies as we observed higher (1.4 to 3 fold higher) expression level of NKA in the 5‰ and 10‰ treatments compared with freshwater (0‰). As M. australiense is well-adapted to inland freshwaters, raised salinity conditions may increase osmotic problems and cause higher levels of stress and so, induce higher expression levels of NKA essentially as a rapid adaptive genomic response to minimize salinity-induced osmotic stress. Moreover, an increase in NKA expression

102 after exposure to higher salinity may also be related to synthesis of other enzymes necessary for dealing with the new/changed environmental conditions. Thus, Na+/K+-ATPase in M. australiense can be considered an important gene for both hypo- and hyper-ionic regulation.

In this study, we also observed a consistent pattern of raised NKCC expression levels with elevated salinity level in M. australiense (Fig. 4-2). Significant differences (p < 0.05) were observed among all treatments with 3 to 6 fold difference in expression level. Results clearly indicate a very important functional role for this gene under raised salinity conditions while in contrast, it may play only a very minor functional role in freshwater. Previous studies (Genovese et al., 2005;

Luquet et al., 2005; Barman et al., 2012; Moshtaghi et al., 2016) reported the functional role of

NKCC to be that it drives Cl- and Na+ transport across salt-secreting epithelia, specifically involved with Na+ and K+ excretion and Cl- uptake depending on the ionic concentration of the surrounding medium. NKCC exchanges one Na+ and one K+ for every 2 Cl- ions and therefore it plays a greater functional role in saline media (Genovese et al., 2005; Moshtaghi et al., 2016). Barman et al.

(2012) observed several fold higher expression of NKCC in M. rosenbergii when individuals experienced higher environmental salinity compared with low salinity conditions. The pattern for

M. australiense was similar, also indicating a more important role for NKCC under elevated environmental salinity compared with low ionic, freshwater conditions. Raised salinity (5‰ and

10‰) induced higher NKCC expression apparently to facilitate ion transport and exchange under the changed osmotic conditions.

While levels of NHE under saltwater conditions (5‰ and 10‰) were relatively low, they were always higher than in freshwater (Fig. 4-3). Significant differences were observed for the relative expression of NHE among treatments initially but this pattern decreased with time over the experiment. This gene is known to have an important functional role in maintaining osmotic

103 balance in low ionic environments (freshwater habitats) (Brett et al., 2005). Reported functional roles of NHE indicate that this gene is involved with a diverse array of physiological processes, including control of the cell cycle and cell proliferation, trans-epithelial Na+ movement, ion transport, exchange of monovalent ions for osmoregulation, and ion absorption in low ionic environments (Boudour-Boucheker et al., 2016; Moshatghi et al., 2016). Currently, there is only very limited information available from decapod crustaceans about this gene. To our knowledge, there have been only 3 studies conducted previously on palaemonid prawns that have considered the NHE gene (Barman et al., 2012; Boudour-Boucheker et al., 2016; Moshatghi et al., 2016).

Towle et al. (1997) conducted an experiment on a crab species (Carcinus maenas) and identified potential role(s) for this gene as being an electrogenic and electroneutral exchanger. In M. australiense, we observed a slight early increase in expression level in the 5‰ and 10‰ treatments

(T1-T3) followed by a gradual decrease in NHE expression levels until no differences were evident among treatments by T6. This suggests that the gene may be important for dealing with initial exposure to a raised sub-lethal salinity but after acclimation over a 120 hour period, expression levels declined as hyper-regulation was apparently no longer required. Earlier work is consistent with this hypothesis, suggesting that NHE is more important for osmoregulation in freshwater than under raised saline conditions (Boudour-Boucheker et al., 2016), indicating that this gene can be considered to have a primary role in hypo-ionic regulation. Further molecular studies will be required however, on the NHE gene to investigate potential function roles in other crustacean taxa under varying salinity environments.

As with NHE, expression levels of the Aquaporin (AQP) gene were initially (T1) very high after which expression levels declined sharply (by T2), with a further gradual decline from T2 to T4.

After this, expression levels in all treatments remained fairly constant and at relatively low levels

104 from T4 to the end of the experiment (T6). This gene also shows an initial response to salinity stress followed by a relatively rapid return to baseline levels.

Of interest, we observed higher expression (even though the difference was not significant) levels of this gene at 0‰ than at 5‰ at T4 to the end of the experiment. Aquaporin has been very well studied in vertebrate taxa with also a number of studies having been conducted on crustacean species (Freire et al., 2003; Augusto et al., 2009; Barman et al., 2012; McNamara and Faria, 2012;

McNamara et al., 2015). In general, results of these studies suggest that AQP acts as a master gene involved in selective transfer of water and other molecules across cell membranes in particular in the gill and antennal gland tissues in crustaceans, while it also maintains water balance and regulates cell volume (Gao and Wheatly, 2004; Anger, 2013; Boudour-Boucheker et al., 2013).

As a consequence of the change in osmotic gradient between the 5‰ and 10‰, we observed higher levels of AQP expression initially (up to T3). Following this (from T4), expression levels declined in the 5‰ treatment more than at 0‰ which may result from near iso-osmotic conditions between the external salinity level and a prawn’s haemolymph at 5‰. The initially higher expression levels may simply be a short-term response to a perceived salinity stress. While freshwater is the normal habitat for M. australiense, 0‰ can still impose some osmotic stress because the surrounding environment may be of lower ionic content compared with the internal body fluid. Thus, major challenges in freshwater conditions for maintaining osmotic balance include, restricting external water gain by regulating water channels and holding internal ions inside cells to avoid critical ion loss (Towle et al., 2011). This may explain why AQP expression was higher in the 0% treatment than at 5% in the current study.

In parallel, we believe that we can also explain the reason for higher expression of AQP in the highest salinity treatment (10‰) where the osmotic gradient difference was very high between the

105 environment and the body fluid. Major challenges for a prawn under this condition involve potential for excessive water loss and excess ion gain. These challenges may induce higher expression levels of AQP to regulate water channels in M. australiense in the opposite direction.

We also observed comparatively high VTA (V-type H+-ATPase) expression levels in the 5‰ and

10‰ treatments over the first 24 hours (T1 to T3), after which expression levels declined between

24 and 48 hours and then established a relatively constant expression pattern in all treatments from

T4 to the end of the experiment (Fig. 4-5). VTA is reported to be a key gene involved with osmoregulation that has been well studied in many organisms (Faleiros et al., 2010; Lee et al.,

2011; Towle et al., 2011; Ali et al., 2015; Moshtaghi et al., 2016). VTA is functionally involved in ion uptake and ion regulation, the control of cellular volume and also in maintaining ionic balance. In general, it is expressed at higher levels in freshwater compared with raised salinity conditions (Stillman et. al., 2008; Charmantier and Anger, 2011; Boudour-Boucheker et al., 2014).

While VTA is considered to play a more important functional role in freshwater environments

(0‰) in M. australiense, we initially observed higher expression levels of this gene in the 5‰ and

10‰ treatments (up to T3). This may be a perceived change in osmotic gradients with prawns in raised salinities needing to respond to a variety of stimuli to maintain their osmotic balance (ion exchange) and to regulate cellular volume. These factors may result in an increased requirement for energy to allow active regulation of ions and to control cell volume (Boudour-Boucheker et al., 2016). Initial higher expression levels of VTA over the first 24 hours of the experiment may be an adaptive response after which when osmotic control has been achieved, less energy is required to maintain osmotic equilibrium.

For all of the five genes examined here, we did not observe major differences in expression patterns among the treatments, among sampling times after T3 in both the 5‰ and 10‰ treatments.

106

Significantly higher expression levels over the experimental time period were observed for each gene only within the first 24 hours (T1 to T3). These results for M. australiense correlate well with a recent study conducted by Boudour-Boucheker et al. (2016) where they compared expression patterns of NKA, VTA and NHE at two different salinity levels on two different Macrobrachium species (one freshwater species and the other, a brackish water endemic) to understand environmental stress specific responses of each gene (i.e. which is more active under which condition) for osmoregulation. Here, we conducted our study on a single species (collected from a single catchment) exposed to three different sub-lethal salinity levels over different time intervals to better understand how the functional responses (in terms of gene expression changes) of individual genes occur with elevated salinity levels and with time.

Adapting to new environmental conditions potentially can be achieved via two potential pathways: adaptive mutations and/or changes in the expression pattern of existing copies of candidate genes

(Wray, 2013). In general, adaptive novel mutations are likely to require longer evolutionary time to evolve so, initial adaptive response are more likely to result from modification of the expression patterns (because gene expression is highly plastic and controlled by external environmental factors) of existing functional genes (Stillman et al., 2008; Wray, 2013). The standard theory of functional gene expression and regulation suggests that, genes are expressed at very high levels initially as a result of a specific perceived stress, but expression level should decline over time

(once organisms have acclimated to the new environmental conditions). When this point is reached, expression levels should decline (to conserve energy) and should reach a stable, lower constant level (Nadal et al., 2011). Here, we observed initial raised expression levels for all of our gene targets (up to T3), following which I observed sharp or in some cases gradual declines in expression levels normally from 24 to 48 hours (T3 to T4), and by the end of the experimental

107 period we observed fairly constant, relatively low expression levels for all genes (after T4). This clearly mirrors the expected pattern for standard theoretical models of gene expression.

Our model crustacean, M. australiense is widely distributed across mainland Australia with many natural populations found in the vicinity of coastal streams and creeks on the east coast of Australia

(Short, 2004; Bernays et al., 2015) where prawns would be exposed continuously to regular changes in sub-lethal environmental salinity levels. M. australiense apparently can modify expression levels of a set of key genes that allow effective osmoregulation in the wild when individuals are exposed to variable osmotic environments. Successful osmoregulatory performance however, does not only depend on efficient maintenance of ionic balance, it also involves mechanistic, morphological (changes to the ultra-structure of gill tissue and other osmoregulatory tissues) and physiological (changes in concentration and osmolality of body fluid haemolymph) responses (McNamara and Faria, 2012; Boudour-Boucheker et al., 2014;

McNamara et al., 2015). To better understand the overall adaptive process, a comprehensive study will be required that examines all three aspects (gene expression patterns, physiological and morphological changes) in the target species (M. australiense) in the future. This will allow a comprehensive model to be developed for how the target species can achieve effective osmoregulation under sub-lethal, raised salinity conditions.

4.6 Conclusions Osmoregulatory performance is considered to be the principal mechanism for maintaining ionic balance when sub-lethal environmental salinity levels vary. A study of relative gene expression patterns (based on qRT-PCR) of five key osmoregulatory genes in M. australiense has revealed remarkable differences and some consistent expression patterns of the selected candidate genes over an experimental time frame under three sub-lethal salinity conditions. Different genes

108 exhibited different expression patterns depending on the intensity of the salinity treatment and timing (or duration) of exposure to salinity stress. At the start of the experiment, we had considered it unlikely that all genes would exhibit similar expression profiles because functional roles vary and some may play primary vs secondary roles depending on the osmotic conditions experienced.

In essence, response to osmotic stress and the adaptive strategy adopted to deal with variation in external salinity levels by M. australiense can be explained by both: multiple allelic interactions

(epistatic) and pleiotropic interactions among the candidate genes; as many genes are likely to be engaged in the response and the same genes potentially have multiple functional roles that in combination have evolved to allow effective osmoregulatory capacity.

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5 Chapter 5: General Discussion

Currently, little information is available about how genes and their mutations, and indeed response from ‘whole genomes’, have evolved to provide efficient osmoregulation in FW crustaceans. Since a number of FW crustaceans constitute both wild and farmed resources that are important to human populations, understanding how these organisms deal with variation in their osmotic environments provides a starting point for their effective conservation and management. Investigations of the physiological aspects of biochemical pathways that support phenotypic differences in salinity tolerance among FW taxa can provide a foundation for developing this knowledge (Cnaani and

Hulata, 2011).

The most important farmed FW prawns are all members of the Genus Macrobrachium (Family

Palaemonidae). Macrobrachium spp. display a wide variety of life history strategies and occur in a diverse array of aquatic habitats (in both tropical and subtropical waters). This genus is present naturally on all continents except Antarctica with more than 250 extant species (Murphy and

Austin, 2005). . All have a common marine ancestor and so constitute a monophyletic clade.

Modern species are in various stages of colonizing FW with only 25 described species able to complete their entire lifecycles in FW. Evolution of FW life history therefore, most likely involves changes (functional mutations and/or changes to gene regulation patterns) in the same set of critical genes that confer ability (or otherwise) to deal with extreme osmotic conditions. M. australiense is a member of the relatively small sub-group that can complete their entire lifecycle totally in fresh water and is considered to be a good hyper-osmoregulator when exposed to environments with high osmotic stress. It has evolved a mechanism to reduce Na+ loss from its body by producing hypo-osmotic urine (Denne, 1968). Identifying CG loci and functional mutations within genes that affect salinity tolerance as well as developing an understanding of how they are regulated (gene

110 expression patterns) provides a basis for developing deeper understanding of how various

Macrobrachium taxa deal with variable osmotic environments.

To better understand how osmoregulatory performance in FW crustaceans allow individuals to acclimate and adapt to raised salinity conditions, we performed a transcriptomic analysis of M. australiense (using gill, antennal gland, hepatopancreas tissues) to identify candidate genes involved with osmoregulation. We used M. australiense (Palaemonidae) in this study as a model for other congeneric freshwater prawns in the genus Macrobrachium because this species is the most widespread FW prawn across mainland Australia and shows high phenotypic diversity and is one of only a small number of endemics that has adapted completely to a freshwater life cycle

(Dimmock et al., 2004). Individuals can tolerate up to 17 ‰ salinity and hence should possess copies of critical genes capable of dealing with both FW and raised saline conditions (Sharma and

Hughes, 2009).

From the transcriptome results, approximately 30% of the contigs were annotated successfully, implying that they represent functional genes documented in other taxa (Table 2-1). Thirty-two important gene families were identified that play potential roles in osmoregulation and ion regulation that potentially contribute to salinity tolerance in. M. australiense. This generated a set of candidate genes for further analysis and comparisons with other related species with different osmotic capacities so that an investigation of the key genes and their functional mutations could be explored in greater detail. Based on identified candidate genes in other species, we were able to target the most important genes related to important metabolic processes including: ATP binding, transmembrane activities, regulation of ion transportation, response to environmental stressors and stress tolerance in M. australiense. Efficient osmoregulatory capacity does not depend solely on ion exchange; it also involves many other biological processes. Of the 32

111 osmoregulatory gene families identified here, 17 genes were involved with transport of the most

+ + + 2+ 2+ - - important ions (Na , K , H , Ca , Mg , HCO3 and Cl ), all of which impact osmoregulation and directly impact salinity tolerance. The other 15 genes are involved with other aspects of osmoregulation and might play minor or secondary roles. Differential expression patterns clearly indicate major roles of gill and antennal gland tissues in adult M. australiense individuals for osmoregulation (Fig 2-1 of chapter 2).

NKA (Na+/K+-ATPase) is considered to be a master gene for osmoregulation in a wide range of crustacean species (Charmantier and Anger, 2011; Cieluch et al., 2004; Lucu and Towle, 2003).

Here we identified multiple isoforms and subunits of NKA with maximum expression in gill tissue.

Gills obviously provide a major osmoregulatory site in the target species and more widely in most aquatic taxa (Table 2-4). Another important gene affecting osmoregulation in crustaceans is V- type ATPase (VTA). This is a crucial gene for regulating ion balance and cell volume in crustaceans in low ionic FW environments (Freire et al., 2008). The important role/s of this gene have been documented in many aquatic animals including in: rainbow trout Oncorhynchus mykiss

(Lin and Randall, 1993), euryhaline crabs, Carcinus maenas and Eryocheir sinensis (Weihrauch et al., 2001) and two freshwater crayfish, Cherax destructor and C. cainii (Ali et al., 2015; Zare and Greenaway, 1998). Here, in M. australiense VTA gene was identified and the recovered gene ontology terms indicate a role for this gene in many different complex processes including acid/base balance, nitrogen excretion, ion exchange and proton regulation in the freshwater environment.

To date, the most important osmoregulatory genes identified in crustaceans are considered to be:

Alkaline Phosphatase, Aquaporin (AQP), Arginine Kinase, Calreticulin (CRT), Carbonic

Anhydrase (CA), Crustacean Cardiovascular Peptide (CCP), Mitochondrial Carrier Protein,

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+ + + + - + - + +2 Na /K -ATPase (NKA), Na /K /2Cl Co-transporter (NKCC), Na /HCO3 Exchanger, Na /Ca

Exchanger, Na+/H+ Exchanger (NHE), Na+/K+/Ca+2 Exchanger, Selanophosphate and V-type H+-

ATPase (VTA). In general, these genes have been well-studied in a great number of crustacean species and shown to influence: ion transport, ion exchange, cell volume control, signal transduction, activating pathways and sensing osmotic stress (Ali et al., 2015b; Stillman et al.,

2008; Faleiros et al., 2010). All have been linked to FW adaptation but VTA and NKA are considered to be the most critical for colonization of FW habitats in most aquatic taxa (Glenner et al., 2006; Tsai and Lin, 2007). Many studies have already been conducted on NKA and VTA genes but there is severe dearth of information on the other osmoregulatory genes.

Modern genomic technologies have made it relatively easy to either identify a list of candidate polymorphic loci, or to search for SNPs and/or gene outliers (loci under selection or adaptive loci) in a set of candidate genes. Here, identification of potential outlier SNPs in three populations of

M. australiense exposed to differing natural salinity regimes provides a foundation for narrowing down the critical mutations driving physiological responses to different sub-lethal salinity conditions. Individuals were collected from three natural populations with individuals from

Blunder Creek completing their entire life cycles in pure low ionic freshwater conditions while individuals from the Bulimba Creek population have direct access to brackish water and the Stony

Creek population has been confined to freshwater for less than 100 years because of a dam. While salinity levels at all sites were very similar, very large differences in conductivity level were evident among sites that contributed to different osmotic niches (sites are not different in terms of salinity but are remarkably different for total ionic content). So any differences detected between sites relate primarily to different osmotic niches influenced by variation in ion content but not necessarily Na+ and Cl-, specifically.

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The population from northern Queensland (Blunder Ck) has been physically isolated by distance from the Brisbane River sites (Stony and Bulimba Ck) for a long period of time, providing potential for unique mutations to accumulate differentially in both locations. As it is highly unlikely for gene flow between these sites, novel mutations at individual sites would not have been able to be exchanged. This could result in different gene copies due to essentially independent evolution of genes as part of the adaptive response to variation in local osmotic environments. The same argument does not hold however, between Stony and Bulimba Creeks because they have been connected physically (same catchment) until recent times.

As mutations occur randomly, the probability of changing amino acid sequences to produce better phenotypes is inherently low. Over short evolutionary timeframes therefore, it is more likely that selection would act more strongly on UTR regions if a gene has important functions because minor changes to regulatory functions are less likely to produce poor phenotypes than random changes to amino acid sequences in a coding region of a functional gene (Nadal et al., 2001). Here functional mutations were not detected in protein coding regions of the candidate osmoregulatory genes examined, but mutations were detected in regulatory sequences that shape and/or control gene expression patterns. So the pattern detected in M. australiense is as predicted given that the populations have evolved recently from a common ancestor so local adaptation has been driven largely by changes to gene expression patterns in the same functional coding sequences. Whether the same mutations in gene regulatory sequences are conserved across greater phylogenetic distances (among closely-related and more-distantly related Macrobrachium taxa) remains to be seen, but as evolutionary time extends, eventually chance mutations are likely to change functional sites in coding regions and their associated phenotypes.

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The major function of VTA (V-Type H+-ATPase) is to induce a H+ gradient for cations such as

Na+ that allows them to be transported via different mechanisms into the cell and would be under positive selection compared with in brackish/saline water (Beyenbach and Wieczorek, 2006; Lee et al., 2011). Here the Blunder Creek population under pure FW conditions showed the highest expression level of the VTA suggesting that this may be an adaptive response to concentrate ions inside cells. The lowest expression, as was expected, was seen in the Bulimba Creek site where individuals had direct access to brackish water (Table 3-2). NKA also showed lower activity expression in dilute media and this is probably related to more demand by cells to maintain their internal ion concentration at constant levels while haemolymph osmotic/ionic concentration tends to be higher than in external media in saline habitats. Expression levels of NKA in saline adapted populations are generally higher than in FW (Lee et al., 2011). Expression levels of NKA reached the highest levels in Bulimba Creek but potentially this is a costly strategy for maintaining ion transport to cells and the haemolymph.

While over 50% of the potential candidate genes examined here showed similar patterns of expression in all populations in the trials (Table 3-2), Calreticulin, CCP, NKA, NKCC, VTA, NHE

+ - and Na /HCO3 exchanger in contrast, showed large differences indicating that they likely play more important roles in osmotic adaptive response in M. australiense when individuals are exposed to different natural ionic conditions. Further studies including these genes under different experimental salinity conditions would be of great interest to generate a better idea of how these genes respond to changes in osmotic gradients over time.

+ - NKA, NKCC, VTA and Na /HCO3 exchanger are directly involved with active uptake and release of a diverse range of ions used to achieve internal osmotic balance (Barman et al., 2012). VTA, a gene that plays a primary role in cellular volume control in addition to maintaining ionic balance

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(Stillman et al., 2008), was expressed at significantly higher levels under pure freshwater conditions, in Blunder Creek individuals. This suggests that this gene is activated to drive ion update in low ionic conditions, while gene activity is limited in environments with naturally higher levels of environmental ions as was evident in Bulimba Creek where there is direct access to brackish water.

NKA should also be considered to be one of the most important genes that assist FW adaptation because expression levels in dilute media were reduced as a result of greater need for cells to maintain their internal ion concentration at a constant level while haemolymph ionic concentration tends to be higher than external media in saline habitats. Expression level of NKA individuals experiencing raised saline conditions is commonly higher than in FW conditions as been demonstrated in many fishes (Lee et al., 2011). A maximum expression level of NKA in the

Blunder Creek population was a result that suggests a relatively costly approach to dealing with a need for ion transportation by cells to the haemolymph.

The highest expression levels of VTA, NHE and Calreticulin genes in the Blunder Creek population, potentially, may result from a need for extra energy by M. australiense individuals under pure low ionic freshwater conditions to attain osmoregulatory stability. High expression levels of Calreticulin in particular, may provide the additional required energy to address this challenge. In addition, raised expression levels of some genes, NHE in particular, is likely to be associated with a need to absorb ions from the surrounding water and to release H+ into the external environment that is characterized by low ionic gradients. Another major challenge in low ionic freshwater conditions is to maintain cell volume to resist ion loss from the body. VTA may assist this process as it is highly expressed under these conditions. These three gene families obviously

116 play more important roles in osmoregulation in M. australiense under freshwater conditions compared with their role(s) in brackish water.

+ - CCP, NKA, NKCC and Na /HCO3 exchanger genes showed maximum expression levels in the

Bulimba Creek population implying they have an important role in osmoregulation in brackish water. In particular, they actively pump out any excess ions gained from the surrounding water

(Ali et al., 2015; Anger 2003; Barman et al., 2012). Higher expression levels of NKA, NKCC and

+ - Na /HCO3 exchanger gene families can address the problems with gaining excess ions from the environment in Bulimba Creek. In parallel, individuals also need to change their body fluid

(haemolymph) concentration under higher osmotic conditions. So far, three master genes have already been identified in crustaceans that are likely to control haemolymph balance including

CCP, DH and CHH (Anger, 2003; Moshtaghi et al., 2016). High expression levels of CCP in M. australiense individuals in brackish water conditions potentially promote production of extra haemolymph (Zhao et al., 2015) that may allow individuals to deal with this osmotic challenge.

Thus, higher expression of CCP might be providing a necessary supportive role to help cope with the ion-rich Bulimba Creek environment.

The top 20 differentially expressed genes among the sampled populations here (Table 3-3) shows that both specific mitochondrial and nuclear genes (Actin, Cytochrome B, 18S, 37S, 40S, 60S

RNA etc.) were also highly expressed but not all highly expressed genes in all populations were influenced by osmoregulatory activities, even though individuals were exposed to different osmotic conditions. The role(s) of other genes that do not have a primary osmotic control role are likely to be involved with house-keeping activities specific to this species. Functional enrichment analysis of all differentially expressed genes (Table 3-5) revealed an absence of enriched GO

117 categories in the Bulimba/Stony comparison while significant enrichment was observed in both the Blunder/Bulimba and Blunder/Stony comparisons.

Differential gene expression (DGE) analysis of major osmoregulatory genes revealed that

Calreticulin, NHE and VTA showed highest expression levels in the Blunder Creek population, while CCP, NKA, NKCC and NHE showed the highest expression levels in the Bulimba Creek population. In total, 16 GO categories were functionally enriched among Blunder/Bulimba and

Blunder/Stony population comparisons. A comparative population genomic scan for searching for adaptive loci (outliers) revealed their presence in four important osmoregulatory genes.

In total, from transcriptome wide SNP calling, 84 contigs were found to show outliers in at least a single comparison. Nearly half of all outlier contigs were unique to only a single population comparison while 18 contigs were found to be outliers in all three population comparisons (Fig 3-

1). Considerably higher numbers of outliers were obtained when comparisons involved the

Blunder Creek population. This confirms that the Blunder Creek population was more often different for individual gene expression pattern for potentially adaptive loci. In part, at least this may result from geographical isolation between the north (Blunder) and the two southern sampling sites (Bulimba and Stony), but also likely reflects impacts of different osmotic environments. A relatively high number (18) of common outliers in all comparisons potentially indicate that genetic response to freshwater invasion of inland waters has occurred under strong selection pressure on specific expressed genes influencing osmoregulation in the newly invaded environment. Bulimba and Stony Creek are both tributaries of the Brisbane River and share many common environmental features as they are connected physically in the same catchment. While Bulimba and Stony populations of M. australiense have been physically isolated from each other over the last 80 years

118 due to dam construction, it is unlikely that there has been sufficient evolutionary time for novel adaptive functional mutations to occur between the two populations.

Identification of outlier loci under selection can provide important insights for understanding local adaptation and speciation under different environmental or ecological conditions (Rundle and

Nosil, 2005). With the advent of modern genomic tools, there is now well-accepted and developed methodology that can identify adaptive genetic loci using transcriptome scanning and examination of a large number of loci to detect outlier loci (Storz, 2005). Here we found, four different outlier loci in identified target candidate (osmoregulatory) genes: Na+/H+ exchanger, Na+/K+-ATPase, V- type-(H+)-ATPase and Calreticulin. These four genes are considered to be the major candidate genes involved with osmoregulation in a wide range of aquatic crustacean species. A comparison of sequence alignments revealed that all outlier SNPs in the four important osmoregulatory genes were present in UTR regions (outside protein coding regions) and were located prior to the start codon region. This suggests that they may underly gene regulatory differences that control gene expression patterns of the same functional genes.

Since all mutations identified were in non-coding UTR regions of functional genes, this suggests that differential evolution of osmoregulatory control under different environmental conditions results from modification of the expression pattern of the same (otherwise identical) functional genes. This implies that adaptation by M. australiense to different sub-lethal osmotic niches is likely to be an ongoing process and is currently largely controlled by changes in gene expression patterns when environmental conditions vary. So, we can predict that the adaptive response of M. australiense to variable osmotic niches is not due to changes in amino acid sequences of the candidate genes, but rather it is being controlled by regulatory mutation located outside the coding region of the candidate genes that have a direct role on gene expression pattern. As M. australiense

119 can tolerate a wide range of salinity fluctuation, this species is unlikely to be impacted severely by salinity intrusion due to climate change effects.

In general, a wide range of CGs is involved with osmotic regulation under raised salinity conditions compared with under low salinity conditions (Berger and Kharazova, 1997; Wieser and

Krumschnabel, 2001). It is highly unlikely that all genes would exhibit similar expression profiles because functional roles vary and some may play primary vs secondary roles depending on the osmotic conditions experienced. In M. australiense, we observed the interacting effects of many candidate osmoregulatory genes underlying successful ionic balance.

There are a number of stages to the adaptive response to osmotic stress at the cellular level; the first step is sensing a stress using specialized cells and sensors in specific tissues that lead to appropriate reactions via signal transduction to connect sensors to activate target molecules to allow cellular adaptation. During environmental stress (during the first hours of exposure to an osmotic stress), the transcriptional rate of some key candidate genes with a role in rapid stress management is elevated and this leads to more gene products that facilitate adaptation to stressful conditions. Modification of chromatin structure at this stage can lead to epigenetic changes which mean changes in gene expression patterns are passed through generations without change in protein function (Nadal et al., 2011).

Generally, when an osmotic stress is sensed, adaptive responses first require activation of cell- cycle checks and allocation of major energy and metabolic processes to stress management. These responses divert energy from cell growth under stressful conditions. Reactions of stress-response genes have been investigated in a large number of taxa from different groups for example in yeast, the expression levels of more than 600 genes were reduced after exposure to different

120 environmental changes (Nadal et al., 2011). These genes work together as a network for sufficient and optimal reaction to osmotic stress, to improve individual survival rate and to facilitate the chance for adaptation to long term environmental change. Gene transcripts with osmoregulatory roles interact with cellular stress response signalling pathways that are all involved in six molecular processes including, 1) stress reaction signalling pathway, 2) appropriate osmolyte accumulation,

3) regulation of energy metabolism, 4) transfer of lipids molecules to protect the cell membrane,

5) implementation of actin into cytoskeleton and 6) stability of mRNA and protein products (Fiol,

2006).

Based on available published gene ontology information on potential candidate osmoregulatory genes and our experiments here, a simple conceptual schematic model was developed that describes the potential interactions of these key genes and the phenotypes they produce involved in salinity tolerance in M. australiense as a useful foundation for understanding the molecular basis of osmoregulation in FW prawns in Macrobrachium more widely.

Based on both recent published information and the current study, five groups of genes have been categorized that are likely to be involved with successful osmoregulation performance in M. australiense (Fig. 5.1). The general consensus is that osmosensors control effector mechanisms in response to environmental osmotic changes. Published data about the role of osmosensors in osmotic regulation is currently very limited, at least in Macrobrachium.

Some studies suggest that an osmosensory signal transduction network, that includes a variety of osmosensors with different sensitivity, are present that detect extracellular osmolarity (plasma osmolarity) and that initiate an appropriate physiological reaction to the osmotic stress (Fiol and

Kültz, 2007). After sensing an osmotic stress, signal transduction pathways are activated to initiate

121 an appropriate and rapid response. In fact, this pathway is essentially a connection sensor for cellular adaptation between the signalling pathways with target molecules inside the cell. Some specific electromagnetic/sensing organs (antennal glands, gills, hepatopancreas) are present in crustaceans that can detect osmotic stress and these play a major role in signal transduction. When osmotic stress is detected by an animal, changes at the cellular level occur in the electromagnetic organs that are rapidly followed by activation of stress responsive genes that initiate effector processes that include modifying translation rates and physiological features of ion channels and ion transporters.

Changes are mediated by the actual candidate genes identified here. The same genes play primary roles in this cascade of responses that ultimately allow individuals to meet the osmotic challenges they face. While different genes play different roles, their integration leads to several phenotypes that constitute specific adaptive responses to the environmental stress. Osmosensors for salinity stress associated with osmoregulatory capacity in M. australiense include; Ca2+-sensing receptor

(CaSR) and Aquaporin (AQP) as a signalling pathway and to regulate function (Fiol and Klutz,

2007). During osmotic stress, high expression of AQP is costly energetically, but it deals with the osmotic challenge and expression levels are consequentially higher in FW acclimated fish species compared with SW adapted ones (Inokuchi et al., 2008)

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Fig 5-1: A model for integration of potential candidate genes involved in osmoregulation in M. australiense. 123

5.1 Group 1 Genes (Stress sensing and signalling)

Alkaline Phosphatase (AP), Aquaporin (AQP), Acyl-CoA binding protein (ACBP), Bestrophin

(BEST), Carbonic Anhydrase (CA), Catechol-o-methyltransferase (COMT), Calreticulin (CRT),

Hedgehog signalling pathway (HOG), Mitogen Activated Protein Kinase (MAPK) and

Mitochondrial carrier protein (MTCP) constitute the most important genes with osmosensory and signalling pathway transduction functions. Localization of osmotic sensors are either within cell membranes or are intracellular. The role of Protein Kinase family genes including AMP-activated protein kinase (AMPK) is significant in regulation of ATP consumption during this stage

(Karagounis and Hawley, 2009). AMP acts as a switch that regulates energy metabolism for different processes under stress conditions (Li et al., 2016). MAPK is involved in signalling pathways to induce a rapid stress response to osmotic stress. This gene in tilapia gill can alter/modify the structure of protein via post-transitional modifications (Fiol et al., 2006) In M. australiense, MAPK is a family of several proteins with a role in signal transduction linked to osmotic stress. This gene family has already has been identified in yeast, animals and plants cells all with similar functions (Arbabi and Maier, 2002; Nguyen et al., 2016). AP is another protein

+2 - that acts as a promoter for Ca and PO4 and is involved in salinity adaptation (Lovett 1994). AQP is also important because it provides water channels for selective transfer of water and other molecules across cell membranes in the gill and antennal gland (Agre et al., 2002; Boyle et al.,

2013; Grosell, 2006; Nishimura and Fan, 2003). CA also has an important role in ion

+ - + - regulation/osmoregulation because it supplies H and HCO3 for Na /Cl exchange. Its role in gas exchange has already been documented in a large number of crustacean species (Serrano and

Henry, 2008; Havird et al., 2013, Herny et al., 2012). Calreticulin acts as a biomarker that transfers osmotic signals while providing energy to address salinity stress (Barman et al., 2012). Different

124 organisms possess different specific stress sensors to initiate and maximize their response to unprecedented external environmental conditions. One of the well-known sensors is the HOG signalling pathway (high osmolarity glycerol). Under hyperosmotic conditions in yeast, the HOG signalling pathway is activated by osmosensors that alter elongation and remodelling of chromatin following which HOG can bind to osmostress genes and change mRNA processing/stability

(Nadal et al., 2011). Mitochondrial carrier protein is also involved in the osmotic signalling pathway in some species.

5.2 Group 2 Genes (Ion regulation)

Ion transporters/exchangers are involved in active transport of ions into and out of cells during osmotic stress (Barman et al., 2012; Kang et al., 2013; Kotlyar et al., 2000; Towle and Weihrauch,

2001). They include, Na+/HCO3- cotransporter (NBC), Na+/Ca+2 exchanger (NCX), Na+/H+ exchanger (NHE), Na+/K+-ATPase (NKA), Na+/K+/2Cl- cotransporter (NKCC), Cl- channel, K+ channel, Na+ channel, Na+/K+ channel, Mg2+ transporter, Ca2+/Na+ channel and H+/Cl- channel.

NKA referred to as the ‘sodium pump’ is an electrogenic transmembrane enzyme located in the basolateral membrane of the gill where it controls secretion of Na+ ions to the outside and transfer of K+ into cells. This gene plays a critical role in ion transport in crustaceans and high expression level of this gene have been documented in a number of decapods exposed to marine conditions including P. marmoratus and P. trituberculatus (Bradley and Bradley 2013; Havird et al., 2014;

Jayasundara et al., 2007). When metabolism is depressed, NKA activity strongly increases

(Ramnanan and Storey, 2006). As an integral membrane protein, this gene is involved in ion transport and ammonia excretion, so it has an associated role in osmoregulatory systems in many taxa (Tsai and Lin, 2007). NKA expression level in SW species is generally higher than in those with a FW ancestor (Lee et al., 2011). Furthermore, this pump is involved in signal transduction

125 to regulate the AMPK cascade pathway and cell volume and has an effect on transport of other ions and for these activities it can use up to 70 % of available ATP energy (Schiener-Bobis, 2002;

Morris, 2001). Ion channels including Na+ and K+ channels play a critical role in ion transmembrane transport and regulation of the main ions in osmoregulation.

2+ + 2+ - + - NKCC, NHE, Ca /Na exchanger, Mg transporter, Cl channel protein, Na /HCO3 exchanger,

Na+/Ca2+ exchanger, H+/Cl- exchanger. NKCC (Na+/K+ /2Cl- cotransporter) located in the basal gill membranes drive Cl- and Na+ exclusion across salt-secreting epithelia and are involved in both

Na+ and Cl- uptake and excretion powered by Na+ and Cl- turnover. Mediation of relative expression levels of NKCC during hypo/hyper osmotic challenge (notably in weak hyper- osmoregulators) (Luquet et al., 2005) has been demonstrated in a number of studies, in particular two different crab taxa Chasmagnatus granulatus and Carcinus maenas (Genovese et al., 2000;

Riestenpatt et al., 1996). A similar Na+ uptake function has been reported for NKCC because it actively moves Na+, K+, 2Cl- from the extracellular fluid to the cytosol of cells. NKCC has been shown to be more important in Na+ regulation in brackish media. Apical NKCC is very active in

M. australiense and controls inward flow of Na+ supplied by apical K+ channels where this channel reuses K+ and increases polarization of the apical cell membrane, inducing Cl- influx into the haemolymph via Cl- channels in the basolateral gill area. All of these drivers can amplify the Na+ inward gradient.

5.3 Group 3 Genes (Cell volume control)

Acyl-CoA binding protein (ACBP), Alkaline Phosphatase (AP), Aquaporin (AQP), Integrin (ITG),

Oligosaccharyltransferase Complex (OST), Selenoprotein (SP), Selenophosphate1(SPS1), V- type-(H+) ATPase (VTA) are involved in salinity tolerance processes, GTPase activity and

126 adaptation under osmotic stress (Gillanders et al., 2003; Kotlyar et al., 2000; Shekhar et al., 2013;

Pongsomboon et al., 2009). AK is a phosphotransferase that is involved in metabolic processes, phosphatase activity and supports ion transport activity. During regulation of ATP levels and osmoregulation, AK play a significant role more specifically in species that require active ion transport and in critical cell processes that need ATP as the main energy supply (Kotlyer et al.,

2000; Towle and Weihrauch, 2001).

VTA, Selenoprotein and the OST complex are the most important members of this group. The other important member is VTA (HAT) that is highly conserved during evolution of crustaceans as it constitutes the main ion pump in FW taxa (Krischner, 2004; Faleiros et al., 2010). Up regulation of this gene has been documented in species that have been acclimated to FW conditions

(Havird et al., 2013). This membrane enzyme transfers ions into osmoregulatory tissues and when being expressed it accounts for approximately 30% of overall ATP requirements (Toei et al.,

2010). The OST complex and Selenoprotein are also involved in maintaining haemostasis and cellular process regulation during osmotic stress, respectively (Pongsomboon et al., 2009;

Gillanders et al., 2003).

5.4 Group 4 Genes (Water channel regulation)

Members if this group play a major role in maintaining osmotic pressure balance between extracellular and intracellular media and include CFTR, AQP, Integrin, NHE, ILF, SPS1. CFTR and ABCC genes that increase osmoregulatory capacity under osmotic challenge. The role of these genes has been discussed above.

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5.5 Group 5 Genes (Body fluid maintenance)

ATP Binding Cassette C12 (ABCC), Bestrophin (BEST), Crustacean cardiovascular peptide

(CCP), Na+ channel, K+ channel, Na+/K+ channel constitute the most critical genes in this category.

In addition, some of these genes are structural and alter cytoskeleton components to enhance cell survival under stressful osmotic conditions. These include Annexin that is a significant Ca2+ binding protein that regulates components of the actin-based cytoskeleton that influences osmotic adaptation in gill cells, at least in the gills of fish. Furthermore, the actin-based cytoskeleton is important for NKCC activity and gill permeability via MRCs (Fiol and Kultz, 2007). In crustaceans, there are also some pivotal processes that occur under hyperosmotic conditions to enhance survival during osmotic stress that include: oxidation-redaction, glycin/L-serin metabolism, carbohydrate metabolism, cellular protein modulation (CPM) and phosphorylation.

CPM and phosporylation are related to post-translational regulation. For example, reducing oxidation in cells is vital for maintaining homeostasis and can influence appropriate responses to stimuli (Lv et al., 2013).

After sensing an osmotic change by osmosensors, structural genes are activated to rearrange the cell cytoskeleton to regulate cell membrane permeability and lipid membrane composition.

Following this, cell protective and immune genes regulate movement of the ions into and out of the cell. Specific genes are responsible for immune response and for protection/repair of cells under hyperosmotic conditions.

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5.6 Interaction of the five most important osmoregulatory genes in M. australiense under salinity stress and their role with respect to FW adaptation

Osmoregulatory performance is considered to be the principal mechanism for maintaining ionic balance under environmental salinity fluctuation. Based on the available literature, co-operation among a set of the genes that work together in unison is crucial for effective osmoregulation in both FW and SW conditions. Based on an indepth literature review and our study 1 & 2, we have generated a list of candidate genes upon which to conduct further investigations. Study 2 revealed that ion regulation under different osmotic niches is largely controlled by changes in gene expression pattern in M. australiense. We selected five important osmoregulatory genes to better understand the role of these genes under different salinity conditions over time (Study 3) using a

RT-qPCR approach. Relative gene expression patterns (based on RT-qPCR) of five key osmoregulatory genes in M. australiense showed remarkable differences in expression patterns over a five day experimental timeframe under sub-lethal salinity conditions. The following section describes the inferred interaction of the five genes in M. australiense under three different sub- lethal osmotic conditions.

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Fig 5-2: Expression levels of the five major osmoregulatory genes in M. australiense over a five day period (a – 0ppt, b – 5ppt and c – 10ppt).

Figures 5-2a, b and c show the patterns of gene expression of the five critical osmoregulatory genes in M. australiense at three sub-lethal salinities (0‰, 5‰ & 10‰) over a five day experimental

130 period. Note that the x-axis has been adjusted to reflect linear time over the five day period.

Relative low and consistent expression for all five genes at 0‰ salinity clearly indicates that M. australiense is well-adapted to freshwater conditions and apparently does not experience any osmotic stress at 0‰. Amongst the five genes studied, the lowest expression level for NKCC was observed at 0‰ salinity indicating a negligible or only very minor role for this gene in freshwater

(5.2a). In contrast, relatively higher expression levels of NHE, VTA and Aquaporin were observed over the whole experimental period at 0‰ salinity indicating important functional roles for these genes in freshwater.

At 5‰ salinity, expression levels of NHE, VTA and Aquaporin were relatively low (Fig. 5-2b), indicating less important roles for these genes under elevated salinity conditions. Even though initial expression levels for Aquaporin and VTA were realtively high, they declined over time indicating that initial exposure to osmotic stress most probably induces their expression. VTA and

NHE are genes well known for their functional role in driving absorption of ions from the surrounding environment in freshwater but not in salt water. Consistently high expression levels were observed for NKA and NKCC under raised salinity levels (5‰ and 10‰) indicating important functional roles for these two genes in M. australiense under raised sub-lethal salinity conditions. As discussed earlier, NKA acts as a master gene for ionic balance (osmoregulation) in both freshwater and in saltwater (pumps ion in or pump ions out depending on the external ionic gradient) (Charmantier et al., 2009; McNamara and Faria, 2012). Thus, high expression of NKA was evident under each of the three osmotic conditions examined in M. australiense in the current study. Relatively higher expression of NKCC (both at 5‰ and 10‰ salinities) indicates an important role of this gene at elevated salinity levels.

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Relatively high expression levels were observed in M. australiense for all five genes across the whole experimental period at the highest sub-lethal salinity examined at 10‰ (Fig. 5-2c). We also observed rapid fluctuation in expression levels of the genes at this salinity clearly indicating that

10‰ is very stressful for M. australiense and requires rapid response from many genes. As a result of higher levels of stress imposed on M. australiense, a rapid response is required from these genes to address the imposed stress. While high levels were observed initially, over time, they declined for Aquaporin, NHE and VTA suggesting that their major role maybe to deal with stresses imposed from raised salinity conditions. Consistently very high levels of NKA and NKCC expression here also indicate important functional roles for these two genes in M. australiense under raised salinities. The functional role of Aquaporin however, is to deal with regulation of water channels and to maintain cellular volume iso-osmotic with the surrounding environment (Freire et al., 2008;

Furriel et al., 2010). While Aquaporin is known to be less important in raised salinity environments, relatively high expression of this gene at 10‰ in M. australiense probably occurs to maintain regulatory cellular volume under the changed osmotic conditions as well.

5.7 Conclusions

Here we conducted a comparative population and functional genomic study of three populations of an endemic Australian freshwater-adapted (ALD) species of freshwater prawn (M. australiense) to understand how effective osmoregulation is achieved at the molecular level under different sub- lethal salinity conditions. A major outcome of the study was recognition that mutations in coding regions of key osmoregulatory genes were not detected but mutations were only detected in regulatory sequences of the same genes that potentially shape and/or control gene expression patterns under different external sub-lethal salinity conditions. So at least for M. australiense, adaptation to different sub-lethal salinity conditions that apparently impose osmotic stress on

132 individuals is achieved primarily by modifying gene expression patterns of essentially identical coding gene sequences of key osmoregulatory genes.

In the current study, we obtained a huge amount of sequence data (≈70% of the assembled contigs) that did not receive any blast hit matching with the existing public databases. Many of these transcripts could be smaller fragments from different parts of existing genes or these could contain many important lineage specific genes (known as taxonomically restricted or orphan genes). These genes can also play very important role in adaptation process. It will be interesting to make an attempt in the future to discover how many of the unidentified transcripts (without blast hits) are under the orphan category. However, the results of the current study provide the basic genomic data required for developing simple molecular models that can characterize how freshwater prawns in the monophyletic genus, Macrobrachium, achieve effective salinity adaptation under a variety of sub-lethal osmotic conditions.

Gene expression profiles are effective instruments for identifying potential CGs that, in this case, may contribute to salinity tolerance via altering osmoregulatory performance. Here we employed

M. australiense essentially as a model for identifying key functional genetic loci that contribute to salinity tolerance in general, more widely in FW prawns. In the future, it is proposed to extend the analysis to other Macrobrachium taxa with different natural tolerance ranges to raised salinity conditions to identify which genes (and mutations within the same genes) have been most important during repeated independent colonization of freshwater by this decapod group.

The current study contains the first comprehensive EST transcriptomic dataset recovered from the freshwater prawn, M. australiense. This species has provided an ideal foundation model for understanding the molecular basis of freshwater adaptation and colonization by other

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Macrobrachium species. Availability of expressed putative genes that are potentially associated with salinity tolerance will also facilitate genomic approaches in other Macrobrachium species that may have applications for improving productivity of farmed freshwater prawn species

(notably M. rosenbergii). Detectable differences in key gene expression patterns identified here will likely present novel genes of interest to other researchers, in particular those candidate genes and functional mutations that affect osmoregulatory capacity more widely in decapod crustaceans.

Climate warming will cause significant changes to many rivers and FW lakes, (including changes to their ionic balance) making them in some cases less productive and so the information developed here provides an important resource for investigating osmoregulation systems and salinity/FW adaptation more widely in aquatic species. In particular, ESTs provide a powerful and effective method for predicting expressed protein components encoded by genes in uncharacterized genomes. Ultimately the information may also be applied potentially to developing more saline- tolerant FW prawn culture lines that can withstand, and acclimate to, increased ionic concentrations in their farm environments.

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