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Variation of the interphase heterochromatin in Artemia (Crustacea, Anostraca) of the Americas is related to changes in nuclear size and ionic composition of hipersaline habitats/ Variacao da heterocromatina interfasica em Artemia (Crustacea, Anostraca) das Americas esta relacionada a mudancas no tamanho nuclear e composicao ionica em habitats hipersalinos.

1. Introduction

Heterochromatin is a characteristic component of the eukaryotic nucleus which, as opposed to euchromatin, is highly compacted, non-coding and contains highly repetitive DNA sequences (Swanson et al., 1981; Brutlag, 1980). The heterochromatin in the interphase nucleus can be visualized as easily discernible heteropycnotic bodies, called chromocenters, which can vary in number and size. In the brine shrimp Artemia, a conspicuous inhabitant of hypersaline lakes and lagoons, the chromocenter number may have a diagnostic value as an indicator of species or populations (Badaracco et al., 1987; Abreu-Grobois and Beardmore, 1989; Colihueque and Gajardo, 1996; Papeschi et al., 2000; Gajardo et al., 2001; Torrentera and Abreu-Grobois, 2002; Papeschi et al., 2008). Previous studies indicate that the heterochromatin in Artemia varies in quantity and quality both within and among species (Gajardo et al., 2002). For instance, the New World Artemia species, A. franciscana and A. persimilis, have a significantly different chromocenter number (Badaracco et al., 1987; Abreu-Grobois and Beardmore, 1989; Colihueque and Gajardo, 1996; Papeschi et al., 2000; Gajardo et al., 2001; Torrentera and Abreu-Grobois, 2002; Lipko et al., 2004). While the former exhibits a high number of these structures (5-18 chromocenters) the latter has lower numbers (<5 chromocenters). Such variation in the amount of heterochromatin is also associated with the presence of repetitive AM sequences in the genome, with around a tenth less of these sequences being observed in A. persimilis in comparison with A. franciscana (Barigozzi et al., 1984; Badaracco et al., 1987). In addition, chromosome studies of these species have revealed an interspecific variation in the diploid chromosome number, consisting of 42 in A. franciscana and 44 in the case of A. persimilis.

The evidence available on plants and animals indicates that variation in the DNA content per genome, usually positively associated with the increase in the amount of heterochromatin (Rayburn et al., 1985; Kao et al., 2001; Bosco et al., 2007), may produce changes at the cellular (termed nucleotypic effect), or organismal level (Gregory and Hebert, 1999). In other words, the phenotype expression would not just depend on the interaction between genotype and environment, but also on the expression of the DNA quantity, irrespective of its informational content (Swanson et al., 1981; Hartl, 2000). For instance, at the cellular level, this modification may affect either the cell or nucleus size, or the duration of the cell cycle. These changes in DNA quantity have been related to variations in the biomass content, breeding season and even the physiological responses of organisms (Swanson et al., 1981; Hartl, 2000).

Artemia is a crustacean that can deploy numerous physiological adaptations; these enable it to tolerate abrupt abiotic changes in the brines inhabited by different species or populations that involve mainly changes in temperature, salinity, ionic concentration and dissolved oxygen. Such changes are driven by the high evaporation rate or the rain regime (Gajardo and Beardmore, 2012). Among the adaptations developed by Artemia to tolerate abiotic changes are a high osmoregulatory capacity, the efficient utilization of dissolved oxygen and the conditional switch in offspring quality between cyst (oviparous reproduction) and nauplii (viviparous reproduction) depending on unfavorable and favorable environmental conditions, respectively. Although most of these adaptive traits have a relatively well-known physiological and molecular basis (Gajardo and Beardmore, 2012), changes in heterochromatin could be another factor in the Artemia repertory adopted to withstand extreme conditions. This has been mentioned in other animal and plant studies, where some of their ecological features are associated with variation in the heterochromatin content (Walker et al., 1991; Ceccarelli et al., 1992, 2002).

In this study the interphase heterochromatin content in different American Artemia populations belonging to the A. franciscana and A. persimilis species were determined through the analysis of interphase nuclei from nauplii cells. This parameter was related to variations in nucleus size in order to explore the existence of nucleotypic changes. We also investigated the relationship of these heterochromatic changes with the ionic composition of the brines inhabited by these populations as a proxy of their adaptive nature.

2. Material and Methods

2.1. Populations studied

Twelve populations of Artemia from different locations in America were analysed: Salina la Colorada Chica (SCC, Argentina), Laguna Amarga-Torres del Paine (TPA, Chile), Laguna de Los Cisnes (CIS, Chile), San Francisco Bay-1258 (SFB, USA), Great Salt Lake (GSL, USA), Salar de Llamara (LLA, Chile), Chaxas (CHX, Chile), La Rinconada (RIN, Chile), Palo Colorado-Los Vilos (LVI, Chile), Salinas de Pichilemu (PCH, Chile), Rio Grande (RGB, Brazil), and Macao (MAC, Brazil) (as shown in Table 1). The populations from the United States (SFB and GSL) and Argentina (SCC), A. franciscana and A. persimilis, respectively, were used as reference species (Gajardo et al., 2001). According to previous studies (Colihueque and Gajardo, 1996; Papeschi et al., 2000; Gajardo et al., 2001), the chromocenter numbers of the remaining populations from Chile (n = 7) and Brazil (n = 2), are known to vary. The Chilean populations were obtained from laboratory cultures originating from live, wild animals.

2.2. Obtaining interphase nuclei

The interphase nuclei were obtained from nauplii by the squash method following the Colihueque and Gajardo (1996) protocol. Larvae were collected either from newly hatched cysts incubated in artificial seawater or from offspring of natural crosses of adults reared in the laboratory under standardised conditions of salinity (35%), temperature (~22 [degrees]C) and light (~1000 lux). The chromocenters of the nuclei were stained using a fluorescent dye (Hoechst 33258) which displays a high affinity for interphase heterochromatic regions (Latt and Wohlled, 1975). The nuclei were photographed at 1000x using a 7 mpx digital camera mounted on an epifluorescence Nikon Labophot microscope. Before taking the photographs, excitation of the fluorochrome was undertaken with UV light through an appropriate filter (UV-2A, 330-380 nm). Five nuclei of each nauplii were photographed at random, totaling from 20 to 58 nuclei per population.

2.3. Heterochromatin quantification in the interphase nuclei

We use a computer-based image analysis to accurately determine the amount of interphase heterochromatin. This method permits increased objectivity, since it can register the totality of the heterochromatin distributed in the nucleus, regardless of its number, size, shape and associations. In this context, the interphase heterochromatin content was estimated using the IMAGEJ version 1.38 software (National Institute of Health, Bethesda, USA). The "count particles" function of the program was used to determine the following parameters: 1) chromocenter number per nucleus (N-CHR) and 2) relative area, as a percentage of the chromocenters per nucleus (R-CHR), representing a relative assessment of interphase heterochromatin. All the analyses were undertaken in grayscale, that fluctuates between 0 and 255 (where 0 = black and 255 = white), whereby a chromocenter was defined as any nuclear structure below the 150 threshold of the grayscale (intense black) and a size above 50 pixels. The relative area (RA) filled by chromocenters in the nucleus was calculated using the formula: RA = (AC/TA) x 100, where AC corresponds to the area covered by the chromocenters in pixels, and TA was the total area of the nucleus in pixels. The nucleus size (S-NUC) was established using the formula for the area of a circumference A = [pi] [r.sup.2], where A is the final area in [micro][m.sup.2] and r is the radius of the nucleus. The absolute diameter of the nucleus was determined using a reference scale incorporated into a micrometer ocular with 0.5 pm sensitivity. The scale bar was subsequently used to calibrate the actual diameter of each nucleus with the SIGMASCAN PRO 5.0 program (Systat software Inc., Chicago, USA), using the image size calibration function.

2.4. Ionic composition of brines

The ionic concentration of brines for SCC, TPA, CIS, SFB, GSL, LLA, RIN, LVI, and PCH populations (as shown in Table 2) was obtained from previous studies (Clarke, 1924; Adams, 1964; Stube et al., 1976; Post, 1980; Gomez-Silva et al., 1990; Amat et al., 1994; Schalamuk et al., 1999; Zuniga et al.,1999; Campos et al., 1996; Lopez et al., 1996; Ruiz et al., 2007; Jones et al., 2009; de Los Rios and Soto, 2009; de Los Rios and Salgado, 2012). Although such conditions are obviously particular to the year and season, it is assumed they represent the water composition of that particular site, depending on their marine (Athalassohaline) or inland (Thalasohaline) characteristics. In order to compare all sites, the values were represented in a Piper's diagram (Piper, 1944), using the DIAGRAMMES program, version 6.1 (Laboratoire d'Hydrogeologie, University of Avignon, Avignon, France). This method displays specific cations ([Ca.sup.2+], [Mg.sup.2+], [Na.sup.+], [K.sup.+]) and anions (HC[O.sub.3.sub.-], C[O.sub.3.sub.2-], [Cl.sup.-], S[O.sub.4.sub.2-]) as a percentage of the total cations and anions, respectively, in a trilinear diagram. Thus, chemically similar waters are grouped in the same position, as follows: a) waters containing sulphate and/or chloride, rich in calcium or magnesium; b) waters containing bicarbonate, rich in calcium or magnesium; c) waters containing chloride and/or sulphate, rich in sodium; and d) waters containing bicarbonated sodium. The percentage of each ion (as shown in Table 2) was calculated based on its value in meq/L, using the following formula: percentage of the ion X = (([SIGMA] meq/L of total ions in water/meq/L ion X) x 100.

2.5. Statistical analyses

The data obtained from the different populations was subject to a two-way analysis of variance (ANOVA), followed by a Tukey's multiple comparison test to carry out a post hoc pairwise comparison of means. Pearson's product-moment correlation analysis was applied to establish the following associations: 1) N-CHR vs. R-CHR; and 2) R-CHR vs. S-NUC. The correlations between R-CHR and percentage of a particular ion were established for the following ions: [Cl.sup.-], S[O.sub.4.sub.2-], [Na.sup.+], [K.sup.+], [Ca.sup.2+] y [Mg.sup.2+]. The significance of correlations was calculated through a Student's t-test. The same statistical test was used to calculate differences between means. The STATISTICA program, version 5.1 (Statsoft, Inc., Tulsa, USA) was used to undertake these analyses.

3. Results

The interphase nuclei from nauplii cells, which display the chromocenters observed in the 12 populations studied, are shown in Figure 1. Quantification of these heterochromatic areas per nucleus indicated that the mean N-CHR values varied widely and significantly among populations (ANOVA, [F.sub.[11,440]] = 31.08, p<0.001), from 0.81 [+ or -] 1.17 to 12.58 [+ or -] 3.78 (as shown in Table 3), with a variation index of 15.5 fold. Thus, there were populations whose means were low (SCC), medium (CHX, LLA, LVI, PCH) or high (RIN, CIS, RGB, MAC, SFB, GSL, TPA). In 30 out of 66 pairwise comparisons, differences in means were statistically significant (Tukey's test, p<0.05). The analysis of this parameter at species level also indicated significant variation among populations for A. franciscana (ANOVA, [F.sub.[8,304] = 10.94, p<0.001) and A. persimilis (ANOVA, [F.sub.[2,136]] = 152.52, p<0.001). With regard to the reference populations, the mean N-CHR in the SFB population (A. franciscana) was significantly higher than in the SCC population (A. persimilis) (10.54 [+ or -] 3.55 vs. 0.81 [+ or -] 1.17, Student's t-test, p<0.05). The mean N-CHR obtained in previous studies (as shown in Table 1) revealed less chromocenters (two to seven) in some populations, in contrast to the result found in this study, such as the LLA and LVI populations.

The mean R-CHR values also varied significantly among populations (ANOVA, [F.sub.[11,440]] = 115.05, p<0.001), from 0.19 [+ or -] 0.34% to 11.78 [+ or -] 3.71%, but with a higher level of variation (62 fold) than the N-CHR parameter. Within this distribution, populations presented mean values that were grouped into low (SCC), medium (CHX, LLA, LVI, RGB, PCH), or high (RIN, MAC, SFB, CIS, GSL, TPA) categories (as shown in Table 3). The pairwise comparison of means for the R-CHR parameter indicated that 50 out of 66 had significant differences (Tukey's test, p<0.001). The result of this parameter at species level also indicated significant variation among populations for A. franciscana (ANOVA, [F.sub.[8,304] = 49.74, p<0.001) and A. persimilis (ANOVA, [F.sub.[2,136]] = 297.07, p<0.001). In the case of the reference populations, mean R-CHR in the SFB population was significantly higher than in the SCC population (8.34 [+ or -] 3.32% vs. 0.19 [+ or -] 0.34%, Student's t-test, p<0.05). The mean N-CHR among A. franciscana and A. persimilis did not differ significantly (9.19 [+ or -] 4.03 vs. 8.95 [+ or -] 5.55, Student's t-test, p>0.05). However, the mean R-CHR was significantly lower in A. franciscana than in A. persimilis (4.59 [+ or -] 3.21 vs. 8.31 [+ or -] 5.76, Student's t-test, p<0.05).

The mean S-NUC values ranged from 155.43 [+ or -] 88.32 [micro][m.sup.2] to 372.32 [+ or -] 160.03 [micro][m.sup.2]. The mean differences among populations were statistically significant (ANOVA, [F.sub.[11,440]] = 19 67, p<0.001), but only 23 out of 66 pairwise comparisons were significant (Tukey's test, p<0.05). At species level, this parameter also indicated significant variation among populations for A. franciscana (ANOVA, [F.sub.[8,304]] = 21.26, p<0.001) and A. persimilis (ANOVA, F[[.sub.2,136]] = 5.53, p<0.01). In addition, the S-NUC parameter, showed much less variation than the R-CHR parameter according to the variation index (2.39 vs. 62 fold). The mean S-NUC was significantly higher in A. franciscana than in A. persimilis (253.94 [+ or -] 125.10 [micro][m.sup.2] vs. 205.76 [+ or -] 90.48 [micro][m.sup.2], Student's t-test, p<0.05).

According to the regression analysis (as shown in Table 4), there was a positive and significant relationship between N-CHR and R-CHR in eight out of 12 populations (r = 0.297-0.792, Student's t-test, p<0.05,). All populations pooled exhibited a significant correlation between both parameters (r = 0.641, Student's t-test, p<0.05). This pattern was also observed for populations of A. franciscana (r = 0.525, Student's t-test, p<0.05) and A. persimilis (r = 0.807, Student's t-test, p<0.05). The coefficient of determination ([r.sup.2] = 0.41) in the relationship of pooled data revealed that only 41% of R-CHR was determined by N-CHR, reflecting a low level of determination between both parameters. Likewise, there was a significant association between R-CHR and S-NUC in five out of 12 populations (Student's t-test, p<0.05), either negative, in four populations (CHX, r = -0.643; RIN, r = -0.464; RGB, r = -0.443; PCH, r = -0.540), or positive, in one population (CIS, r = 0.367). All populations pooled showed a negative and not statistically significant association (r = -0.032, Student's t-test, p>0.05) between both parameters. A similar pattern was observed for populations of A. franciscana (r = -0.009, Student's t-test, p>0.05) and A. persimilis (r = -0.005, Student's t-test, p>0.05).

Piper's diagram (see Figure 2) grouped populations studied into two classes according to the predominant ions in each location: 1) waters containing sodium and/or chloride such as SCC, LLA, RIN, LVI, PCH, SFB and GSL populations; and 2) water with irregular ionic composition, represented by TPA and CIS populations, where the maximum percentage of S[O.sub.4.sub.2-] was relatively high (23-67%). With regard to the correlation between the ion concentration of each location and R-CHR (as shown in Table 5), the pooled data indicated a positive and significant association (Student's t-test, p<0.05) for the [Mg.sup.2+] ion and negative and significant association (Student's t-test, p<0.05) for the [Cl.sup.-], Na+ and [Ca.sup.2+] ions. At species level, this analysis indicated that there were different associations for some ions, particularly, the S[O.sub.4.sub.2-] and [Ca.sup.2+] ions displayed negative and significant correlations in A. franciscana, in contrast to A. persimilis.

4. Discussion

Analysis of the interphase heterochromatin in Artemia has been based mostly on counting the number of chromocenters using visual methods. Despite the taxonomic value attributed to this cytogenetic trait (Gajardo et al., 2001), some authors have stated that these studies have probably been subject to considerable experimental error (Papeschi et al., 2008). This situation may occur due to following sources of error: 1) the chromocenters tend to merge in the interphase nucleus, and thus the actual chromocenter number counted in different nuclei might differ; 2) the chromocenter counts are often carried out regardless of size, thus small chromocenters may be compared to large ones, despite the fact that the former may have less heterochomatin than the latter; this situation leads to a misinterpretation of the amount of heterochromatin present in the nucleus; and 3) some researchers may exclude the tiny chromocenters on the final count, based on a subjective decision. To address this problem, we included the determination of the relative amount of heterochromatin per nucleus in this study (i.e. R-CHR parameter), to ensure more reliable heterochromatin quantification in the nucleus. Indeed, large interpopulation differences were observed in the mean percentage of heterochromatin per nucleus, compared to the traditional method based on counting the number of chromocenters (62 fold vs. 15.53 fold), which confirms the robustness of this methodology. In other species, such as Arabidopsis (Soppe et al., 2002), a similar strategy has been adopted to improve the quality of this analysis.

The relationship between the percentage of interphase heterochromatin per nucleus and nuclear size showed no significant associations at species level. However, our results indicate that, in some cases, significant associations occur at a population level. For example, CHX, RIN, PCH and RGB populations exhibited a significant reduction in nuclear size associated with an increase in the amount of interphase heterochromatin, while the opposite was observed for the CIS population. This finding suggests the existence of a nucleotypic effect in Artemia from the Americas, mediated by variation in the amount of heterochromatin present in the nucleus. However, the effect would be specific to certain populations. It is important to note that the natural habitats of these population may vary widely throughout the year, for example, in temperature (18-29.8 [degrees]C for RIN) (Gomez-Silva et al., 1990) and salinity (30-120 ppt for PCH) (Gajardo et al., 1998). Therefore, the associations observed between both parameters may reflect their adaptations to particular ecological conditions. On the other hand, the results showing reduction in nucleus size to be negatively correlated with heterochromatin content appears intriguing, although this effect has recently been demonstrated (Wang et al., 2013) in another organism, specifically in the Arabidopsis mutant Crowded Nucleus (CRWN). In the case of increasing nuclear size, the evidence available in animals and plants has revealed a positive correlation, although mainly with nuclear DNA content (Swanson et al., 1981; Jovtchev et al., 2006).

The strong and positive association between the content of interphase heterochromatin and magnesium concentration in the brine sites is a significant result. In other words, heterochromatin variation would induce changes that extend from the cell to the organism level, expressed as the differential ability to survive in brines with different ionic composition. Although this effect must be substantiated in further studies, especially relating the actual ion composition of the brine to the moment when samples of Artemia are obtained, this is a plausible working hypothesis worthy of future study. The two populations (TPA and CIS) considered in this study, located in Chilean Patagonia (below latitude 50[degrees] S) are atypical for Artemia standards (Clegg and Gajardo, 2009). According to data previously collected (Campos et al., 1996; Zuniga et al., 1999; De Los Rios and Soto, 2009), both hypersaline sites would differ from the traditional description of environments classified as Athassalohaline (inland waters) and Thassalohaline (marine waters). For instance, magnesium content (9.85-46.35%) is considerably higher (up to 2.5 times) than that of sea water. Despite the fact that the adaptive role of heterochromatin might still be considered controversial, evidence now available indicates that its role in the cellular function cannot be ignored, for example, this element is involved in the stabilization of the chromosome structure, chromosome segregation and gene silencing (Grewal and Jia, 2007). Furthermore, variation in heterochromatin content has been associated with the particular distribution of the species in response to divergent environments (Walker et al., 1991; Ceccarelli et al., 1992, 2002), or with the biomass level of the organisms (Edelman and Lin, 1996). Previous studies showing a latitudinal variation in chromocenter numbers in A. franciscana would be an indirect indication of such heterochromatin abilities (Gajardo et al., 2001). In fact, the Artemia used in this study, collected from Torres del Paine, present a larger relative heterochromatin content in the nucleus than that of the other populations analysed. Clarification of this paradox requires further analysis, but a preliminary hypothesis would be that particular environmental and water conditions may have effectively selected particular genomic and phenotypic features for this population. For instance, cyst size of this population, larger than that of other Chilean populations, appears to be associated with the particular ionic composition of the water in this site (Castro et al., 2006). In addition, analysis of the association between amount of interphase heterochromatin and the ionic concentration of the brines revealed a species-specific negative association with the sulphate and calcium ions, only in the case of A. franciscana. Given that the brine habitats of populations of this species are mainly Thassalohaline waters, whose ionic composition is characterized as being rich in chloride and sodium and poor in sulphate and calcium ions (a result that was confirmed by the Piper's diagram), this association could be explained by the scarce presence of these ions in their natural habitats.

The result indicating interspecific or interpopulation differences in the relative amount of interphase heterochromatin found in Artemia is significant. The origin of this variation may have other causes, such as difference in genome size, or the particular evolutionary process of the karyotype. In the case of differences in genome size, greater amount of heterochromatin is expected in species with a large genome size than in those with a smaller genome size, a pattern that has been demonstrated in evolutionary closely related organisms (Rayburn et al., 1985; Kao et al., 2001; Bosco et al., 2007). Although this hypothesis is interesting, to date it is not possible to contrast Artemia species, since data is only available for the genome size of A. franciscana, reaching a value of 0.97 pg per haploid genome (De Vos et al., 2013). Differences in the evolutionary process of the karyotype could also be taken into account given that A. franciscana appears to be at more advanced stage of evolution than A. persimilis (Parraguez et al., 2009). Thus, it is expected that the former would accumulate more heterochromatin in the chromosomes than the latter. With the exception of the TPA and CIS populations of A. persimilis, which presented large amounts of interphase heterochromatin, most populations of both species studied followed this pattern. Hence in our case, this hypothesis seems to be consistent.

According to the evidence available in Arabidopsis, nuclear changes induced by heterochromatin cannot be ruled out, given the identification of various genes that specifically control such processes (Fransz et al., 2003; Wang et al., 2013). For example, in the ddml mutant, it is observed that the heterochromatin content is significantly reduced in the nucleus (-30%) in comparison to the wild type. This is reflected at the cytological level in lower chromocenter numbers with small sizes. Likewise, in the CRWN4 mutant the chromocenters are notoriously disorganized or dispersed when compared to those observed in a normal nucleus. Based on these cytological patterns, it is possible to classify the organization of the nucleus in Arabidopsis according to the appearance of chromocenters, either as adispersed phenotype (CRWN4 mutant) or as acompact phenotype (wild type). It is important to note that the arrangement of chromocenters in the nucleus of some Artemia populations analysed in this study may match these phenotypes. For example, LLA, CHX and RGB populations would represent a dispersed chromocenter phenotype, while LVI population would correspond to a compact chromocenter phenotype (see Figure 1). Thus, we cannot discard the possibility that these phenotypes may reflect the existence of a particular organizational process of the heterochromatin in the nucleus, across different Artemia populations, whose control may depend on the action of specific genes. In addition, chemical and physical factors may also be involved in the packing status of the chromocenters in Artemia populations, given that natural populations of this organism may be subject to many abiotic stressors, such as changes in temperature, ionic composition and salinity (Gajardo and Beardmore, 2012). Although the participation of these factors has not generally been demonstrated in Artemia, experimental data collected in other organisms reveals their existence. For instance, in NIH 3T3 mouse cells treated with valproic acid produce decondensation in the chromatin structure of the nucleus, including the heterochromatin areas (Felisbino et al., 2014); while in Malpighian tubule cells of the blood-sucking insect, Triatoma infestans, heterochromatin decondensation occurs either after treatments with heavy metals (copper and mercury) or heat shocks (Mello et al., 1995, 2001). Moreover, heterochromatin decondensation after heat shock treatements in Malpighian tubule cells of a vector of Chagas' disease in Brazil, Panstrongylus megistus, infected and non-infected by Trypanosoma cruzi, has been also reported (Garcia et al., 2011). In our case we believe that such factors would not have affected the results obtained since the samples analysed are from laboratory cultures that were kept under standardised conditions of temperature and salinity. However, the chemical or physical factors that might be affecting the condensation of heterochromatin in the interphase nucleus in natural populations of Artemia emerge as an interesting topic for future studies.

Further studies on heterochromatin in Artemia aimed at providing more in depth information about its organization and structure may help to clarify the biological meaning of variation across populations. This type of analysis may contribute additional insight into the source of variation in the interphase heterochromatin, both at intraspecific and interspecific levels in Artemia.

5. Conclusion

The amount of interphase heterochomatin per nucleus that was estimated based on chromocenter number and relative area of chromocenter, in A. franciscana (n = 9) and A. persimilis (n = 3) populations from different locations of the Americas, varied significantly both within and between species. The relationship between relative area of chromocenter and nuclear size revealed a significant association, either negative or positive, in five out of twelve populations analysed. All populations pooled or categorised by species did not show a statistically significant association between both parameters. There was a significant association between relative chromocenter area and ionic concentrations of natural brines in the populations studied, with a positive correlation for magnesium and negative correlations for chloride, sodium and calcium. When populations were categorised by species, a negative and significant correlation with sulphate and calcium ions was found for A. franciscana. These findings suggest the existence of a nucleotypic effect on nuclear size in some populations of Artemia from the Americas, mediated by the variation in the amount of interphase heterochromatin of the nucleus. Moreover, the association of this parameter with the ionic composition of natural brines suggests that it could be involved in the ability of this organism to survive in these environments, which are habitually subject to strong physicochemical changes.


The suggestions and constructive comments of all those who helped to improve the final version of this manuscript, are gratefully acknowledged. Reference samples of A. franciscana and A. persimilis were provided by the Artemia Reference Center, Ghent, Belgium. The English language editing of the manuscript by Susan Angus, is also appreciated. The authors are indebted to Dr P.M. Galetti Jr for kindly reviewing the Resumo in Portuguese. This study was financed by Direccion de Investigacion of the Universidad de Los Lagos, project R03/11.


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M. Parraguez (a), *, G. Gajardo (a)

(a) Laboratorio de Genetica, Acuicultura and Biodiversidad, Departamento de Ciencias Biologicas y Biodiversidad, Universidad de Los Lagos, Av. Alcalde Fuchslocher, 1305, PO Box 933, Osorno, Chile

* e-mail:

Received: February 26, 2016--Accepted: April 30, 2016--Distributed: August 31, 2017 (With 2 figures)

Caption: Figure 1. The interphase nuclei of nauplii cells, displaying the chromocenters stained by Hoechst 33258 fluorescent dye (bright bodies) observed in the 12 studied population of Artemia. Populations: (a) Salina la Colorada Chica (SCC), (b) Laguna Amarga-Torres del Paine (TPA), (c) Laguna de Los Cisnes (CIS), (d) San Francisco Bay-1258 (SFB), (e) Great Salt Lake (GSL), (f) Salar de Llamara (LLA), (g) Chaxas (CHX), (h) La Rinconada (RIN), (i) Palo Colorado-Los Vilos (LVI), (j) Salinas de Pichilemu (PCH), (k) Rio Grande (RGB) and (l) and Macao (MAC). Bar represent 5 gm.

Caption: Figure 2. Piper's diagram displaying ionic concentration (%) of brine waters of nine American Artemia populations studied in this work. The populations were the following: Salina la Colorada Chica (SCC), Laguna Amarga Torres del Paine (TPA), Laguna de Los Cisnes (CIS), San Francisco Bay (SFB), Great Salt Lake (GSL), Salar de Llamara (LLA), Palo Colorado Los Vilos (LVI), La Rinconada (RIN) and Salinas de Pichilemu (PCH). Antofagasta (ANT) sample was included in the analysis as reference of sea water.
Table 1. List of Artemia populations analysed in this study. Location,
geographic coordinates and mean chromocenter number previously
reported is shown.

Population                          Country     Code   Species

1. Salina la Colorada Chica         Argentina   SCC    A. persimilis

2. Laguna Amarga-Torres del Paine   Chile       TPA    A. persimilis

3. Laguna de Los Cisnes             Chile       CIS    A. persimilis

4. San Francisco Bay-1258           USA         SFB    A. franciscana

5. Great Salt Lake                  USA         GSL    A. franciscana

6. Salar de Llamara                 Chile       LLA    A. franciscana

7. Chaxas                           Chile       CHX    A. franciscana

8. La Rinconada                     Chile       RIN    A. franciscana

9. Palo Colorado-Los Vilos          Chile       LVI    A. franciscana

10. Salinas de Pichilemu            Chile       PCH    A. franciscana

11. Rio Grande                      Brasil      RGB    A. franciscana

12. Macao                           Brasil      MAC    A. franciscana

Population                          Geographic          Mean
                                    coordinates         chromocenter
                                                        reported (a)

1. Salina la Colorada Chica         38[degrees]22'46"S    1.0
2. Laguna Amarga-Torres del Paine   50[degrees]58'32"S    17.7
3. Laguna de Los Cisnes             53[degrees]10'35"S    7.0
4. San Francisco Bay-1258           37[degrees]32'53"N    16.8
5. Great Salt Lake                  41[degrees]6'56"N     9.0t
6. Salar de Llamara                 21[degrees]21'00"S    13.8
7. Chaxas                           23[degrees]17'6"S     NA
8. La Rinconada                     23[degrees]26'21"S    9.5[dagger]
9. Palo Colorado-Los Vilos          32[degrees]4'27"S     9.9
10. Salinas de Pichilemu            34[degrees]30'5"S     6.7
11. Rio Grande                      5[degrees]06'00"S     10.1
12. Macao                           5[degrees]05'51"S     9.6

(a) According to Gajardo et al. (2001); [dagger] Papeschi et al.
(2008); [double dagger] Unpublished data from Laboratorio de Genetica,
Acuicultura and Biodiversidad, Universidad de Los Lagos.

Table 2. Ionic concentration and the transformation to percentages of
brine waters inhabited by Artemia populations from the Americas
according to previous studies.

                         Ionic composition
Country     Population   [Cl.sup.-]            S[O.sub.4.
                         mg/L      %           mg/L      %

Argentina   see          174,000   94.69       13,020    5.24
Argentina   see          173,597   95.27       11,500    4.67
Chile       TPA          14,600    46.30       22,900    53.70
Chile       TPA          8,960     25.31       26,000    54.32
Chile       TPA          9,650     25.78       26,600    52.55
Chile       TPA          12,263    55.25       374       1.25
Chile       TPA          12,264    57.54       330       1.15
Chile       TPA          8,290     51.25       389       1.78
Chile       TPA          9,466     46.73       673       2.46
Chile       TPA          17,020    25.86       60,380    67.86
Chile       CIS          11,700    55.71       6,770     23.84
Chile       CIS          12,900    59.06       6,780     22.96
Chile       CIS          5,693     74.30       73        0.71
USA         SFB          179,200   89.14       29,530    10.86
USA         GSL          111,100   90.85       14,850    8.98
USA         GSL          177,600   91.49       21,540    8.21
USA         GSL          181,000   89.78       27,000    9.90
USA         GSL          718,000   92.06       8,000     7.59
Chile       LLA          134,722   76.35       56,256    23.58
Chile       LLA          37,451    72.40       19,200    27.45
Chile       LLA          29,302    74.18       13,632    25.52
Chile       LLA          43,600    77.17       17,440    22.83
Chile       RIN          152,780   88.49       26,880    11.51
Chile       LVI          23,480    89.70       3,550     10.03
Chile       LVI          53,300    90.23       7,580     9.49
Chile       LVI          53,300    90.10       7,600     9.50
Chile       PCH          121,958   96.44       6,091     3.56
Chile       PCH          54,780    90.23       7,690     9.37
Sea water   ANT          54,840    90.20       7,710     9.38

                         Ionic composition
Country     Population   [Na.sup.+]        [K.sup.+]

                         mg/L      %       mg/L    %

Argentina   see          108,340   94.32   1,110   0.57
Argentina   see          111,600   93.75   4,156   2.06
Chile       TPA          27,300    84.01   1,390   2.52
Chile       TPA          19,000    81.11   1,868   4.70
Chile       TPA          21,900    81.79   1,930   4.25
Chile       TPA          580       23.14   95      2.24
Chile       TPA          5,330     54.57   35      0.21
Chile       TPA          10,240    74.13   109     0.47
Chile       TPA          25        0.70    106     1.78
Chile       TPA          34,160    82.64   2,250   3.21
Chile       CIS          15,500    87.42   430     1.43
Chile       CIS          16,300    89.06   324     1.04
Chile       CIS          4,119     80.08   129     1.48
USA         SFB          NA        NA      NA      NA
USA         GSL          NA        NA      NA      NA
USA         GSL          101,450   81.86   4,140   1.97
USA         GSL          105,400   80.64   6,700   3.02
USA         GSL          41,100    81.80   2,300   2.70
Chile       LLA          107,042   95.18   3,094   1.62
Chile       LLA          31,013    92.58   914     1.61
Chile       LLA          23,513    91.93   737     1.70
Chile       LLA          35,810    92.67   2,130   3.25
Chile       RIN          75,120    78.65   2,780   1.72
Chile       LVI          12,750    75.31   410     1.43
Chile       LVI          32,480    77.48   1,320   1.86
Chile       LVI          32,500    77.46   1,300   1.83
Chile       PCH          NA        NA      NA      NA
Chile       PCH          31,380    78.29   1,100   1.62
Sea water   ANT          29,850    77.38   1,090   1.67

                         Ionic composition
Country     Population   [Ca.sup.2+]    [Mg.sup.2+]

                         mg/L    %      mg/L      %

Argentina   see          500     0.50   2,800     4.61
Argentina   see          857     0.83   2,112     3.36
Chile       TPA          20      0.07   2,300     13.40
Chile       TPA          6       0.03   1,752     14.16
Chile       TPA          6       0.03   1,971     13.93
Chile       TPA          2       0.09   987       74.54
Chile       TPA          55      0.65   2,300     44.57
Chile       TPA          7       0.06   1,850     25.35
Chile       TPA          0       0.00   1,816     37.51
Chile       TPA          330     0.92   2,089     13.23
Chile       CIS          11      0.07   1,038     11.08
Chile       CIS          7       0.05   952       9.85
Chile       CIS          16      0.37   491       18.07
USA         SFB          NA      NA     NA        NA
USA         GSL          NA      NA     NA        NA
USA         GSL          300     0.28   10,400    15.89
USA         GSL          300     0.26   11,100    16.08
USA         GSL          189     0.43   4,000     15.07
Chile       LLA          332     0.34   1,700.4   2.86
Chile       LLA          441     1.51   759.4     4.29
Chile       LLA          836     3.76   352.8     2.61
Chile       LLA          450     1.34   560       2.74
Chile       RIN          730     0.88   9,460     18.75
Chile       LVI          380     2.58   1,860     20.38
Chile       LVI          1,260   3.46   3,810     17.21
Chile       LVI          1,300   3.56   3,800     17.15
Chile       PCH          NA      NA     NA        NA
Chile       PCH          1,210   3.47   3,520     16.62
Sea water   ANT          1,220   3.64   3,530     17.32

Country     Population

Argentina   see          Schalamuk et al. (1999)
Argentina   see          Ruiz et al. (2007)
Chile       TPA          Unpublished data [double dagger]
Chile       TPA          Unpublished data [double dagger]
Chile       TPA          Unpublished data [double dagger]
Chile       TPA          Campos et al. (1996)
Chile       TPA          Campos et al. (1996)
Chile       TPA          Campos et al. (1996)
Chile       TPA          Campos et al. (1996)
Chile       TPA          Zuniga et al. (1999)
Chile       CIS          Unpublished data [double dagger]
Chile       CIS          Unpublished data [double dagger]
Chile       CIS          de Los Rios and Soto (2009)
USA         SFB          Clarke (1924)
USA         GSL          Adams (1964)
USA         GSL          Stube et al. (1976)
USA         GSL          Post (1980)
USA         GSL          Jones et al. (2009)
Chile       LLA          Lopez et al. (1996)
Chile       LLA          Lopez et al. (1996)
Chile       LLA          Lopez et al. (1996)
Chile       LLA          Zuniga et al. (1999)
Chile       RIN          Gomez-Silva et al. (1990)
Chile       LVI          Amat et al. (1994)
Chile       LVI          Zuniga et al. (1999)
Chile       LVI          de Los Rios and Salgado (2012)
Chile       PCH          Unpublished data [double dagger]
Chile       PCH          Zuniga et al. (1999)
Sea water   ANT          Zuniga et al. (1999)

[section] The percentage of each ion was calculated based on its value
in meq/L, using the following formula: percentage of the ion X =
(([sigma] meq/L of total ions in water/meq/L ion X) x 100; [dagger]
Data from .Antofagasta (northern Chile) was included as reference of
sea water; [double dagger] Data from Laboratorio de Genetica,
Acuicultura and Biodiversidad, Universidad de Los Lagos.

Table 3. Summary of the interphase heterochromatin content and nucleus
size parameters (mean [+ or -] SD) in Artemia populations.

Population   Species          No. of     N-CHR (n)
                              (No. of

SCC          A. persimilis    32(6)      0.81 [+ or -] 1.17 (a)
TPA          A. persimilis    58(7)      12.58 [+ or -] 3.78 (d)
CIS          A. persimilis    49(10)     9.95 [+ or -] 3.06 (cd)
SFB          A. franciscana   26(5)      10.54 [+ or -] 3.55 (c,d)
GSL          A. franciscana   24(8)      11.71 [+ or -] 3.67 (d)
LLA          A. franciscana   38(4)      6.83 [+ or -] 4.47 (b)
CHX          A. franciscana   20(5)      5.3 [+ or -] 2.36 (a,b)
RIN          A. franciscana   41(8)      11.75 [+ or -] 3.14 (d)
LVI          A. franciscana   38(5)      7.89 [+ or -] 3.65 (b,c)
PCH          A. franciscana   53(5)      8.35 [+ or -] 2.82 (b,c)
RGB          A. franciscana   33(4)      9.84 [+ or -] 3.15 (c,d)
MAC          A. franciscana   40(3)      10.12 [+ or -] 4.64 (c,d)
Pooled       A. persimilis    139(23)    8.95 [+ or -] 5.55 (x)
Pooled       A. franciscana   313(47)    9.19 [+ or -] 4.03 (x)
Pooled       Both species     452(70)    9.12 [+ or -] 4.54
Variation    Across                      15.53
index        populations

Population   R-CHR (%)                 S-NUC (um2)

SCC          0.19 [+ or -] 0.34 (a)    212.98 [+ or -] 84.05 (a,b)
TPA          11.78 [+ or -] 3.71 (e)   178.02 [+ or -] 61.55 (a)
CIS          9.49 [+ or -] 4.36 (d,e)  233.87 [+ or -] 112.55 (a,b)
SFB          8.34 [+ or -] 3.32 (c,d)  212.53 [+ or -] 60.58 (a,b)
GSL          8.34 [+ or -] 2.51 (e)    300.18 [+ or -] 57.96 (b,c)
LLA          1.89 [+ or -] 0.91 (b)    207.96 [+ or -] 95.42 (a,b)
CHX          2.09 [+ or -] 1.06 (b)    155.43 [+ or -] 88.32 (a)
RIN          6.94 [+ or -] 2.74 (c)    372.32 [+ or -] 160.03 (e)
LVI          2.11 [+ or -] 1.44 (b)    253.57 [+ or -] 68.20 (b)
PCH          3.93 [+ or -] 2.09 (b)    172.54 [+ or -] 42.07 (a)
RGB          2.87 [+ or -] 1.46 (b)    369.41 [+ or -] 164.51 (c,e)
MAC          5.98 [+ or -] 2.82 (c)    237.62 [+ or -] 191.52 (a,b)
Pooled       8.31 [+ or -] 5.76 (x)    205.76 [+ or -] 90.48 (x)
Pooled       4.59 [+ or -] 3.21 (y)    253.94 [+ or -] 125.10 (y)
Pooled       5.74 [+ or -] 4.50        239.12 [+ or -] 117.58
Variation    62.00                     2.39

Population means in each column bearing different letters are
significantly different from each other (Tukey's test, p< 0.05).
Species means (pooled data) with different letters indicate
significant differences (Student's t-test, p< 0.05). N-CHR=
chromocenter number per nucleus, R-CHR= relative area in percentage of
chromocenter per nucleus, S-NUC = nuclear size.

Table 4. Correlation values between heterochomatin content (N-CHR and
R-CHR) and nucleus size (S-NUC) in Artemia populations.

Population       No. of     N-CHR vs. R-CHR   R-CHR vs. S-NUC

SCC              32         0.792 (0.000) *   0.126 (0.478)
TPA              58         0.032 (0.757)     -0.202 (0.123)
CIS              49         0.465 (0.001) *   0.367 (0.009) *
SFB              26         0.335 (0.094)     -0.322 (0.107)
GSL              24         0.313 (0.000) *   -0.158 (0.282)
LLA              38         0.406 (0.011) *   -0.145 (0.364)
CHX              20         0.071 (0.755)     -0.643 (0.002) *
RIN              41         -0.063 (0.669)    -0.464 (0.002)
LVI              38         0.639 (0.000) *   -0.100 (0.546)
PCH              53         0.609 (0.000) *   -0.540 (0.000) *
RGB              33         0.442 (0.009) *   -0.443 (0.009) *
MAC              40         0.297 (0.008) *   -0.235 (0.331)
A. persimilis    139        0.807 (0.000) *   -0.005 (0.947)
A. franciscana   313        0.525 (0.000) *   -0.009 (0.897)
Pooled           452        0.641 (0.000) *   -0.032 (0.407)

Values in brackets are p-values according to Student's t-test; * p <

Table 5. Correlation values between ions concentration and
heterochromatin content (R-CHR) in Artemia populations.

                  [Cl.sup.-]   S[O.sub.    [Na.sup.+]   [K.sup.+]

A. persimilis
No. of samples    13           13          13           13
Correlation (r)   -0.845       0.350       -0.431       0.289
                  (0.000) **   (0.241)     (0.141)      (0.338)
A. franciscana
No. of samples    15           15          12           12
Correlation (r)   -0.549       -0.546      -0.341       0.537
                  (0.034) *    (0.035) *   (0.278)      (0.072)
No of samples     28           28          25           25
Correlation (r)   -0.705       0.276       -0.478       0.233
                  (0.000) **   (0.155)     (0.016) *    (0.263)

                  [Ca.sup.2+]   [Mg.sup.2+]

A. persimilis
No. of samples    13            13
Correlation (r)   -0.476        0.419
                  (0.101)       (0.154)
A. franciscana
No. of samples    12            12
Correlation (r)   -0.653        0.443
                  (0.021) *     (0.149)
No of samples     25            25
Correlation (r)   -0.640        0.496
                  (0.001) **    (0.012) *

Values in brackets are p-values according to Student's t-test; * p <
0.05; ** p < 0.001.
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Title Annotation:Original Article
Author:Parraguez, M.; Gajardo, G.
Publication:Brazilian Journal of Biology
Date:Jul 1, 2017
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