Comparative ultrastructure of spermatozoa from two regular and two irregular New Zealand echinoids 

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Chapter 4. Swimming performance and internal pH of sperm

Introduction

Ocean acidification (OA) is having a pervasive effect across biological systems from cellular processes through to ecosystem function (Helmuth, 2009; Hale et al., 2011; Andersson et al., 2015; Yates et al., 2015). Broadcast spawning marine invertebrates release gametes into the surrounding water where changes in levels of fertilisation success are attributed to OA induced changes in gamete performance (Havenhand et al., 2008; Byrne, 2011; Schlegel et al., 2012). Both negative and positive effects of OA on sperm motility and swimming speed are evident across a range of species including bivalves (Havenhand & Schlegel, 2009; Vihtakari et al., 2013), cnidarians (Morita et al., 2010; Nakamura & Morita, 2012), polychaetes (Lewis et al., 2013; Schlegel et al., 2014), asteroids (Uthicke et al., 2013a), holothurians (Morita et al., 2010), as well as echinoids (Havenhand et al., 2008; Caldwell et al., 2011). The most common mechanism proposed to underpin the change in echinoid sperm performance under OA conditions is a change in sperm internal pH (pHi); although decreased motility has recently been linked to lower mitochondrial membrane potential (Schlegel et al., 2015).
Equipped with only the cellular machinery provisioned during spermiogenesis, sperm cells are described as being limited in their capacity to defend an optimal homeostasis and to tolerate environmental change (Melzner et al., 2009). Both activation and sperm motility are under tight control of pHi with pathways of both energy supply and energy utilisation being pH dependent (Cummins, 2009; Nishigaki et al., 2014). As an integrator of multiple cellular processes and mechanisms that affect or are effected by pHi, there is an intricate balance in the H+ loading and H+ extrusion from the cell towards maintaining an optimal steady state pHi (Busa & Nuccitelli, 1984; Putnam, 2001; reviewed in: Bevensee & Boron, 2008). The dilution of echinoid sperm in seawater results in the alkalinisation of pHi through the release of CO2 and H+, both at high levels in the gonad (Johnson et al., 1983; Trimmer & Vacquier, 1986). When pHi is above 7.2-7.3 there is an increase in both motility and respiration, with the maximum rate of increase at about pH 7.5 (Christen et al., 1982; Neill & Vacquier, 2004). The increased pCO2 and decreased pH of OA are suggested to have a narcotic effect on sea urchin sperm through an impaired ability to regulate pHi (Havenhand et al., 2008; Byrne, 2011; Reuter et al., 2011); although oxygen also plays a role in activation (Chia & Bickell, 1983).
A diminished capacity of sea urchin sperm to attain an optimal pHi may have consequences for overall sperm performance due to a suite of pHi dependent processes that are involved with activation and motility of sea urchin sperm. These include activity of the dynein ATPase flagellar motors that drive swimming (Gibbons & Fronk, 1972; Christen et al., 1986; Neill & Vacquier, 2004; Nishigaki et al., 2014), the regulation of the phosphocreatine shuttle that supplies energy to the motors (Golding et al., 1995; Ellington, 2001), and the pH sensitive lipases that provide substrate for mitochondrial ATP generation (Cardin & Meara, 1953; Mohri & Yasumasu, 1963). At the same time pHi also indirectly impacts a number of regulatory enzymes and secondary messengers (Brokaw, 1987; Nomura et al., 2005; Vacquier et al., 2014). Collectively, we might expect that a decrease in pHi of activation is potentially going to result in decreased motility and swimming speed.
Sperm quality and performance are of particular interest for agriculture, aquaculture as well as for human infertility, resulting in the development of techniques for quantifying key quality and performance measures (Cabrita et al., 2008). In this study we apply one of these techniques, computer assisted sperm analysis (CASA) (Partyka et al., 2012), to assess sperm motility in sperm of a broadcast spawning marine invertebrate in response to OA. Corresponding pHi analysis using fluorescent dyes and flow cytometry (Gillan et al., 2005; Hossain et al., 2011; Robles & Martínez-Pastor, 2013) allowed the testing of the hypothesis that under atmospheric CO2 conditions projected for 100 to 300 years in the future, under ‘business as usual’ emission scenarios, sea urchin sperm performance will be compromised due to impaired ability to defend an optimal pHi.

Methods

Seawater manipulation and chemistry

Protocols for the manipulation and analysis of elevated pCO2 experimental seawaters were based on recommendations from Dickson et al. (2007) and Riebesell et al. (2010). In brief, seawaters were bubbled with target CO2 concentrations to give 1) Control/Low for present day at 380 ppm (parts per million); 2) Medium for near-future concentrations based on IPCC scenarios of RCP 8.5 for the year 2100 (1000 ppm); and 3) High for far-future concentrations based on projections for the year 2300 (1800 ppm). Seawater for all experiments was obtained pre-filtered from Kelly Tarlton’s Undersea World and re-filtered prior to pCO2 manipulation through two nested, 1 micron filter bags (FSW). Air was initially dried and stripped of CO2 before being mixed with instrument grade CO2 (99.98%) using dial-a-gas mass flow controlled (Smart Trak2, Sierra Instruments) mixing setup based on the experimental system outlined by Fangue et al. (2010), and calibrated with an infrared CO2 analyser (Qubit S151). To ensure equilibrium of gas partial pressures between target gas mixtures and experimental seawaters, self-contained 20 L containers of FSW were bubbled for a minimum of 16 hours using a temperature controlled (20±1 °C) circulation system where gas mixtures were added through a venturi injector (Mazzei, MK-384) to optimise gas exchange efficiency.
Sets of treatment waters (Control, Medium and High) for experiments (OA seawaters) were handled as per SOP1 (Dickson et al., 2007). In brief, OA seawaters were taken from the bubbling buckets with water samples siphoned through a silicone hose over-filling 250 mL boro-silicate sample bottles by at least 50% for experiments and seawater analysis. A separate set of OA waters were prepared for each motility and pHi experiment, and were placed in a 20 °C waterbath (Grant W28) at least two hours prior to experiments to allow temperature equalisation. To minimise temperature and CO2 changes during the motility and pHi experiments, each set of bottles was held in a custom Styrofoam insulator from where aliquots were taken at least mid-water column with rapid replacement of the lid of both bottle and insulator. Water temperature (Thermoworks Thermapalm; certified calibrated accuracy 0.05 °C), and salinity (YSI 30) were measured prior to experiments.
Sets of OA seawaters were analysed for pHTotal and TA for further carbon chemistry calculations. All pHTotal analyses were performed within two hours of sampling and TA samples (250 mL) were preserved with a 50 µL aliquot of saturated mercuric chloride and stored at 4 °C in the fridge for later analyses. Spectrophotometric pHTotal determination used m-cresol purple (Sigma) to measure changes in absorbance at 434 nm and 578 nm wavelengths based on SOP6b (Dickson et al., 2007) and Fangue et al. (2010), with calibration performed using TRIS standard (SOP3a) (Dickson et al., 2007) with accuracy confirmed against certified reference standards to be 8.104 pH (Dickson et al., 2007). Open cell potentiometric total alkalinity (TA) measurements were run using a Mettler-Toledo T50 automated titrator based on SOP3b (Dickson et al., 2007) and Fangue et al. (2010) with accuracy of TA values confirmed using Scripps certified reference seawater (batch 108 at TA = 2218 µmol/kg). Titrations over equivalence points initially used 0.1 mL aliquots of a 0.6 M NaCl 0.1 M HCl acid titrant to a pH of 3.6, a 360 second stabilisation period, followed by aliquots of 0.05 mL of titrant to pH 3.0. Both TA and pHTotal values were calculated using formulae from SOPs 6b and 3b, based on MSExcel calculation spreadsheets modified from Gretchen Hofmann’s lab (University of California, Santa Barbara).
Carbonate chemistry for experimental waters was recalculated from measured parameters (salinity, temperature, pHTotal, TA) to experimental temperature points using the seacarb (v. 3.0.8) package in R (v. 3.1.2); this package was selected based on comparisons of ten ocean carbonate chemistry packages by Orr et al. (2015) using CO2 constants as outlined in “Guide for best practices in ocean acidification research” (Dickson et al.,2007). Seacarb uses the recommended formulations for first and second dissociation constants K1 and K2 (Lueker et al., 2000), Kf (Perez & Fraga, 1987) and Ks (Dickson,1990). Additionally the recommended (Dickson et al., 2007) boron/chlorinity ratio from Uppström (1974), and default values for atmospheric and hydrostatic pressures, silicate and phosphorous content were used. All our experiments were within the specified salinity (19-43 ppt) and temperature (2-35 °C) limitations for constants K1 and K2 (Orr et al., 2015).

Sea urchin collection and spawning

During the austral summer of 2014-2015 Evechinus chloroticus were collected, held, fed and spawned as outlined in Chapter 3, Section 3.2.1.

Sample preparation

Over a two week period sperm from thirteen different males were used in paired sperm motility and internal pH experiments (Figure 4-1). Sperm quality was visually assessed for high levels of motility using a Sedgewick rafter slide at 100x magnification. Sperm concentration for all experiments was standardised through haemocytometer counts from a standard dilution of 10 µL dry sperm mixed with 30 mL FSW. From these counts the volume of dry sperm to give a target sperm concentration of 1 million cells/ mL for experiments was calculated for each male. Multiple males were run on a single day and the order of OA seawaters was randomly selected for each male, the order being repeated for the paired motility and internal pH experiments.

Motility experiment

Optimised video capture for sperm motility analysis used an inverted microscope (Nikon Eclipse Ti at 5x magnification) adjusted for differential interference contrast (DIC) imagery. The importance of using a consistent within-experiment equipment setup and video capture frame rate are discussed in Boryshpolets et al. (2013) and Castellini et al. (2011). Motion was captured in a 250 image TIF-stack with an ANDOR iXon camera using proprietary IQ2 software with 2×2 binning to give a capture rate of 34 frames per second. A set of custom PVC, deep and relatively large-volume imaging chambers was designed to minimise boundary (or wall) effect from glass surfaces (Gee & Zimmer-Faust, 1997) and to maximise thermal inertia of the sample. The 3 mm thick PVC chambers with attached coverslip on the base had a volume of approximately 339 µL (Figure 4-2).
Figure 4-2: Custom sperm motility chambers made from 3 mm thick PVC plastic. Two chambers per plate of 12 mm diameter with coverslip glued to base (green dotted line) and coverslip on top (blue line) closes the chamber.
Motility recordings were performed as rapidly as logistically possible, with all video recordings started ~10 seconds after initiating each sperm dilution. The pre-calculated volume of dry sperm was added to a 14 mL aliquot of OA seawater in a 15 mL Falcon tube and agitated gently for 4 seconds to make a relatively homogenous sperm suspension of ~1 million cells/ mL. To minimise air bubbles when the chamber was closed with the top coverslip, a 350 µL aliquot of the sperm suspension was gently added to over-fill the imaging chamber and the top coverslip lowered in place to close. The chamber was immediately put on the inverted microscope stage with the focus pre-set for midway between the two coverslips and the 250 image TIF-stack acquisition started. Sperm dilutions were mixed in rapid succession producing back to back recordings with technical replication of 10 at each CO2 treatment (control, mid and high) (Figure 4-1). Preliminary analyses showed a high degree of technical replication was required to characterise variance within males.

Internal pH experiment

Internal pH (pHi) of sperm was examined using a flow cytometer and the pH sensitive dye carboxy-SNARF-1 (seminaphthorhodafluor), acetoxymethyl ester, acetate (Invitrogen, Molecular Probes C1272, Lot # 1151593) (referred to as SNARF-1 in this thesis). Within the assumptions and limitations, SNARF-1 provides a tool to produce calibrated, fine-resolution pHi measurements in E. chloroticus sperm cells using the optimum dye incubation protocols developed for SNARF-1 and E. chloroticus sperm (Chapter 3, Section 3.3.4). In summary, to minimise activation and metabolism of sperm prior to experimental incubations the dry sperm were minimally diluted and kept on ice. To ensure optimum dye performance, a new 1.25 mM SNARF-1 stock was prepared in anhydrous dimethyl sulfoxide (DMSO, from Sigma-Aldrich) every second day and stored in the dark at -18 °C between use. A standard 4 µL aliquot of dry sperm was added to 75.4 µL of FSW and 0.64 µL of stock SNARF-1 to give a final dye concentration of 10 µM in a brown 1.5 mL Eppendorf tube. A parallel incubation with the SNARF-1 replaced with FSW was used as a control (Figure 4-1). Incubations were mixed well by hand (holding the tube upright and gently flicking the tip of the tube until suspension appeared evenly mixed) and left in ice-water for 90 minutes in the dark.

Fluorescence acquisition

After the 90 minute incubation a predetermined volume of sperm (calculated for each male from sperm counts) was diluted in 3 mL of OA seawater to give a target sperm concentration of 1 million cells/ mL. Sample analysis was performed as outlined in Chapter 3, Section 3.2.4. In brief, immediately after mixing, samples were analysed in a flow cytometer (BD Biosciences FACS Calibur) optimised for E. chloroticus sperm cells with a combination of forward and side scatter (FSC & SSC respectively) used to identify the sperm cells. The SNARF-1 emission collected through detectors FL2 and FL3 (585/42 nm and 670 nm long-pass respectively) with voltages adjusted for autofluorescence and position on the axes, allowed identification of the stained and unstained sperm. An event rate of 350 to 150 events per second resulted in a single consolidated population of sperm cells identifiable by FSC & SSC, and data from approximately 5000 events was collected for each sample. As with the analysis of sperm motility, consecutive replicate dilutions (n 10) were mixed for each OA seawater from the SNARF incubation mix (Figure 4-1) and run in immediate succession followed by controls for: 1) seawater alone, 2) sperm without SNARF-1 and 3) sperm viability confirmed with 1 mM propidium iodide (PI). The SNARF-1 fluorescence ratio (R) calculated from FL3/FL2 is used in further calculations of pHi (see Section 4.2.7.3 for more detail).

Calibration

Calibration of SNARF-1 fluorescence allows quantification of pHi using a series of pH buffers across the expected pHi range and the ionophore nigericin to equalise hydrogen ion concentrations [H+] on either side of the cellular membrane (Thomas et al., 1979).
Correct buffer formulation is important for effectively limiting any nigericin induced shifts in SNARF-1 fluorescence ratio to changes in [H+]i. Calibration buffers and internal potassium concentration are discussed in Chapter 3 with a target buffer [K+] of 466 mM. A final buffer formulation giving [K+] of 472.5 mM (well within the variability of [K+]i among analytical techniques) consisted of 22.5 mM HEPES-KOH, 450 mM KCl, and 120 mM CoCl (modified from: Nakajima et al., 2005). The buffer series was prepared to target pH values (6.81, 7.00, 7.26, 7.51, 7.75, 8.00 and 8.11) with 472.5 mM KCl in 3 M HCl and 3.472 M CoCl to standardise the combined volume added to each buffer. Calibration buffers were stored in the fridge and two hours prior to experiments were put in the same 20 °C waterbath as OA seawaters. A calibration was performed for each male at the end of the pHi experiment (Figure 4-1).

Data analysis

CASA with ImageJ

The image analysis software ImageJ (v. 1.50a running on FIJI “Fiji Is Just ImageJ”; National Institutes of Health, Bethesda, MD, USA) was used to process videos and perform particle tracking with computer assisted sperm analysis (CASA); ImageJ produces outputs similar to commercially available systems (Boryshpolets et al., 2013). A customised batch-processing macro (inspired by: Purchase & Earle, 2012) incorporating the CASA plugin (Wilson-Leedy & Ingermann, 2007) was used to analyse a two second sub-stack (images 18 to 85) of the original TIF-stacks. Despite a pre-set microscope setup (Section 4.2.4), each sub-stack had to be independently adjusted for threshold by eye for comparable analyses. This was achieved by using a standard procedure to highlight a general low level background scatter by eye, the threshold was then increased by 5 points to leave a very fine background noise – a scatter of pixels that was removed through the CASA parameterisation. The CASA plugin was parameterised for identification of E. chloroticus sperm cells and definition of motion characteristics for motile and non-motile sperm (Appendix 2). For each sample, output data on sperm swimming performance include measures of motility and swimming speeds of motile sperm as described in Table 4-1.
Flow cytometry analysis
The flow cytometer output .fcs files were processed in FlowJo (version 10.0.8) by gating the single population of sperm cells using FSC & SSC. The SNARF-1 stained cells of this gated population were isolated by setting additional active gates on both FL2 and FL3 histograms based on the unstained control. A small proportion (average for FL2 and FL3 for each sample < 5%) of the samples were determined to be unstained and excluded from further analysis. Using the mean fluorescence intensities of the gated populations, the fluorescence ratio (R) was calculated from FL3/FL2 for all males across control and OA seawaters, and also for the calibration solutions. The PI fluorescence of non-SNARF-1 stained sample was checked for high levels of viability after the incubation period.

Statistical approaches

Statistical analyses were performed using the software RStudio (version 0.99.486) and ‘R’ (RCoreTeam, 2015), unless specified otherwise, with basic plotting done using the ggplot2 package. Data from seawater pHTotal and pHi calibrations were separately analysed with a one-way ANOVA. Assumptions of normality were checked with Q-Q plots and Shapiro-Wilk for levels of factor. Heteroscedasticity was checked with Levene’s test and post hoc test for comparisons between means using Tukey-Kramer’s (α = 0.05).

LnRR

For both motility and pHi measures, the magnitude of effect of elevated CO2 seawater on sperm was calculated on non-standardised data using the logarithmic response ratio (LnRR) and combined with an estimate of precision using confidence intervals (Nakagawa & Cuthill, 2007; Schlegel et al., 2012). This approach is generally considered to provide a more effective assessment of the biological importance of the data than using p-values alone (Nakagawa & Cuthill, 2007). The LnRR is defined by the natural-log proportional change in the means of the treatment groups and control group (LnRR = ln[x� treatment/x� control]). Technical replicates were averaged to the level of the individual and the LnRR was calculated for all sperm performance measures (CASA outputs from Table 4-1 and both pHi and the SNARF-1 fluorescence ratio (R)). Each parameter’s LnRR for all 13 individuals were then bootstrapped in the statistical software ‘R’ running boot package and the resulting mean and 95% normal confidence intervals returned from 100,000 iterations. Bootstrapping provides an estimate of precision with the large number of iterations reducing the effect of random sampling errors. This approach involves sampling with replacement under the assumption that the sample data provides a good estimate of the population sampled, with the distribution of bootstrap-sampled means used to infer the estimate of precision (Quinn & Keough, 2002; Kulesa et al., 2015). The confidence intervals provide a measure of the variance between individuals and results are interpreted as significant where CIs do not overlap with zero (Schlegel et al., 2015).

Multivariate analysis

Multivariate techniques allow the relationships between multiple variables to be examined at the same time, in response to the experimental treatments (different levels of pCO2) (Anderson et al., 2008). The permutational multivariate analysis of variance (PERMANOVA) simultaneously evaluates differences in both location and spread to test the null hypothesis that there is no difference in the attributes of the different groups in multivariate space . Compared to multpile analysis of variance (MANOVA), the PERMANOVA allows use of any distance measures and no assumption of normality applies as any distribution is removed through the use of permutations (Anderson et al., 2008).
For multivariate analysis, all data were standardised to a mean of zero and a variance of one to prevent distortion of analysis from 3 orders of magnitude difference between values for different motility parameters (Quinn & Keough, 2002). To remove the possibly redundant information of correlated variables and thereby reduce associated multi-collinearity expected from the CASA outputs (Fitzpatrick et al., 2012; Dormann et al., 2013; Kabacoff, 2015) the seven CASA motility parameters (Table 4-1) were condensed into principal components (PC) using principal component analysis (PRIMER v. 6, Plymouth Marine Laboratory). This procedure was performed using all technical replicates (n = 10 for the majority of analyses across all treatments, n = 9 where a replicate returned no result) for each treatment level and all 13 individuals. PCs with eigenvalues greater > 1 were selected; indicating that the PC explained more of the variation than the original variables.
All technical replicates were included in PCA for the generation of PCs; however, as motility and SNARF-1 analyses were on separate sub-samples from the same individual, and to avoid pseudo-replication, technical replicates for PCs and pHi were averaged for further calculation of the resemblance matrix using Euclidean distance. Multi-dimensional scaling (MDS) and principal coordinate ordination (PCO) plots were used to assess the distributions based on resemblance matrix ranks and distances respectively. Based on the resemblance matrix an initial (PERMANOVA) with two factors (male and OA seawater treatment) running 9999 permutations of residuals under a reduced model was used to assess the variability and differences between treatments. A lack of replication at the lowest level (i.e. due to the averaging of technical replicates to a single value) resulted in the automatic exclusion of the highest level interaction term due to being confounded by the variance of the residuals (Anderson et al., 2008). Multivariate dispersion using PERMDISP tested for homogeneity in the cluster dispersion around the centroids for the three OA seawater treatments. A final analysis of the resemblance matrix used PERMANOVA with pairwise comparisons of the factor ‘OA seawater’.

Table of contents
Abstract
Acknowledgements 
Table of contents 
List of figures
List of tables 
Chapter 1. General introduction 
1.1 Ocean acidification
1.2 Biological impacts
1.3 Impacts on sea urchin fertilisation and early cleavage
1.4 Sea urchin gamete activation & mitosis
1.5 Variation and resilience
1.6 Study species
1.7 Aims of research
1.8 Thesis structure
Chapter 2. Comparative ultrastructure of spermatozoa from two regular and two irregular New Zealand echinoids 
2.1 Methods
2.2 Results
2.3 Discussion
Chapter 3. Methods for the analysis of internal pH in sea urchin sperm
3.1 Introduction
3.2 Methods
3.3 Results & discussion
Chapter 4. Swimming performance and internal pH of sperm
4.1 Introduction
4.2 Methods
4.3 Results
4.4 Discussion
Chapter 5. Fertilisation success
5.1 Introduction
5.2 Methods
5.3 Results
5.4 Discussion
Chapter 6. General discussion
6.1 Summary and synthesis
6.2 Future directions
Chapter 7. Appendices 
7.1 Appendix 1. Hills Laboratories ICP-OES report
7.2 Appendix 2. Motility experiment and CASA parameterisation
7.3 Appendix 3. Summary of select echinoid fertilisation studies
7.4 Appendix 4. Relationship between sperm % motility and fertilisation success
List of references
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Sperm performance and fertilisation in the sea urchin Evechinus chloroticus under increasing pCO2

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