Cells of multi-cellular organisms evolve toward uni-cellularity in the form of cancer and, if humans intervene, continue to evolve in cell culture. During this process, gene dosage relationships may evolve in novel ways to cope with the new environment and may regress back to the ancestral uni-cellular state. In this context, the evolution of sex chromosomes vis-a-vis autosomes is of particular interest. Here, we report the chromosomal evolution in ~ 600 cancer cell lines. Many of them jettisoned either Y or the inactive X; thus, free-living male and female cells converge by becoming ‘de-sexualized’. Surprisingly, the active X often doubled, accompanied by the addition of one haploid complement of autosomes, leading to an X:A ratio of 2:3 from the extant ratio of 1:2. Theoretical modeling of the frequency distribution of X:A karyotypes suggests that the 2:3 ratio confers a higher fitness and may reflect aspects of sex chromosome evolution.https://doi.org/10.7554/eLife.28070.001
Multicellular life relies on a group of cells working together for a common interest. To study these cells, researchers take them out of the organism and grow them in the laboratory. Instead of growing as part of organs and tissues, the cells normally have a free-living lifestyle. Because multicellular life evolved from single-celled organisms, laboratory-grown cells can be considered as life forms that are evolving backward from a multicellular to a single-celled existence.
Normally, the cells that make up most of the tissues in the human body have 22 pairs of chromosomes known as autosomes and a pair of sex chromosomes. The cells of women have two X sex chromosomes, one of which is inactive, while those of men have one X and one Y chromosome. However, free-living single cells do not need to distinguish between male and female cells.
Xu, Peng, Chen et al. have now studied the chromosomes of cancer cells taken from over 600 people and grown in the laboratory. As the cells evolved in response to their free-living lifestyle, they became ‘de-sexualized’; male cells lost their Y chromosome, while female cells abandoned their inactive X chromosome. The cells then evolved toward a new state in which they possessed two active X chromosomes and three sets of autosomes. This new configuration suggests that the current X chromosome to autosome ratio may not be optimal for fitness and hence sheds some light on how mammalian sex chromosomes evolved.
It is currently thought that as cancerous tumors grow, their cells evolve to favor their own interests over the common interests of the rest of the organism. In this way, they develop characteristics more like those of single cells. Further research is therefore needed to investigate whether changes occur to the chromosomes of cancer cells growing within the body, and whether this gives them an advantage over normal cells.https://doi.org/10.7554/eLife.28070.002
Genomes of multi-cellular organisms evolve to ensure the survival and reproduction of the whole organisms. With human interventions akin to domestication, hundreds of cell lines survive as free-living cells that are not organized into tissues, organs or individuals (Alberts et al., 2002). Evolution in such a quasi-unicellular state may be very different from the evolution as multi-cellular entities. Most cell lines are cancerous in origin but a few are derived from normal tissues (Hayflick, 1998). Regardless of their origin, they have all evolved characteristics for survival in the unicellular state that is distinct from their natural environments. Cell lines derived from cancer tissues are usually karyotypically less stable than normal cell lines (Lengauer et al., 1997). While this instability may impose a cost, it also permits cancer cell lines to evolve new karyotypes, including polyploidy, more readily than normal cell lines could.
Tumorigenesis has been increasingly viewed as a process of evolution, rather than merely pathological conditions (Nowell, 1976; Merlo et al., 2006). This ‘ultra-microevolutionary process’ is subjected to similar rules including mutation, genetic drift, migration and selection that govern organismal evolution (Wu et al., 2016). While this process usually ends when the organism dies, cell lines in the cultured state will continue to evolve. Much like the diversity unleashed by domestication, cultured cell lines, which can be considered ‘domesticated’, may be informative about the evolutionary potentials at the cellular level.
In this quasi-unicellular state, gene dosage has been observed to change extensively as polyploidy, aneuploidy (full or partial) and various copy number variations (CNVs) are common in cancer cell lines (Roschke et al., 2003). Since these cell lines are derived from somatic tissues of men or women (referred to as male and female cells, for simplicity), they should be different in their sex chromosomes in relation to the autosomes (A’s). Nevertheless, the possibility of separate evolutionary paths has not been raised before. Somatic cells have an inactive X chromosome in females and a Y chromosome in males (Charlesworth, 1991). Since cell lines presumably do not need sexual characters, we ask how the X:A relationship might have evolved in both male and female cells. More generally, we ask whether the evolution in this relationship may shed light on the emergence of mammalian sex chromosomes and their subsequent evolution.
In this study, we analyze 620 cancer cell lines that have been genotyped using SNP arrays (Greenman et al., 2010). Among them, 279 are derived from female tissues and 341 from male tissues. We observed the elimination of the Y and the inactive X chromosome, followed by the evolution toward a new equilibrium with two active X chromosomes and 3 sets of autosomes (2X:3A). We discuss the implication of these findings for the evolution of sex chromosome, the transition between uni- and multi-cellularity and cancers biology.
The most common form of genomic changes in cell lines is the loss of heterozygosity (LOH) when one of the two homologous chromosomes is eliminated (Roschke et al., 2003). We therefore examine single nucleotide polymorphisms (SNPs) across the 620 cell lines for occurrences of LOH on each autosome and the X chromosome. Male and female cell lines are separately analyzed.
Figure 1A shows the LOH frequency for each autosome (black dots) and the red dot represents the sex chromosomes (X in female and Y in male). For autosomes, the percentages of LOH are remarkably similar between sexes, with a correlation coefficient of 0.94 among 620 cell lines. There is a slight tendency for the smaller autosomes to have higher LOH rate (R =~−0.4, p=~0.046, Figure 1—figure supplement 1). The median percentage of LOH is about 13% for autosomes. However, the losses of X (37% in females) and Y (40% in males) stand out. Given its rank as the 7th largest chromosome, the X is not expected to be lost in more than 15% of cell lines, based on the regression analysis of Figure 1A. Since the expression from the X is not lost, we infer that it’s the inactive X(or Xi) that is eliminated.
Female lines lose the inactive X (Xi) and male lines lose the Y chromosome at a higher rate than other chromosomes. The two sexes may thus be expected to converge toward having a single sex chromosome. Furthermore, given that spontaneous LOH is not infrequent and the loss cannot be regained, long-term cultures might evolve to complete LOH for sex chromosomes as well as autosomes. The genome-wide low rate of LOH suggests selection holding back such changes. The strong correlation between sexes further reflects a balance between the production and elimination of LOH’s, likely involved natural selection.
A most unexpected finding is that, accompanying the loss of the Y or Xi, an extra X chromosome is often gained. Figure 1B shows approximately equal numbers of male cell lines with one or two X chromosomes (partial X aneuploidy not counted). This extra X is active because the inactivating XIST lncRNA is silenced in male cell lines (Figure 1C), consistent with previous findings (Guttenbach et al., 1995). XIST does not become activated in free-living cells that do not already express this. The expression of X-linked genes is higher in those male lines with two X’s than in those with one X and the up-regulation occurs along the length of the X chromosome (Figure 1D).
The pattern is more complex in female lines which, in their original state, contain an Xa and an Xi, the latter expressing XIST (Chow et al., 2005; Plath et al., 2002; Ng et al., 2007). We use only female lines that show LOH of the whole X chromosome (~37% of female lines) in counting Xa’s for the following reason. In order to count active Xa’s, we require the absence of XIST expression in the line such that all X’s can be assumed active. Figure 1E shows that female lines with LOH indeed rarely express XIST, presumably because LOH lines that survive lost the inactive X and kept the active Xa. In contrast, non-LOH lines tend to express XIST, thus obscuring the counting of Xa’s. Of the 103 LOH female lines, 30 lines have single Xa and 31 lines have whole extra X’s as shown in Figure 1F. Much like male lines of Figure 1B, Figure 1F also shows roughly half of female lines to have gained an extra Xa.
Cancer cell lines usually have high rates of aneuploidy and could be heterogeneous within a given line, thus making its status difficult to assess. To assess the level of within-line heterogeneity, we chose two representative cell lines to count the X chromosomes in individual cells using fluorescent in situ hybridization (FISH). The two lines are A549 (a male cell line from adenocarcinomic alveolar basal epithelium) and HeLa (a female cervical cancer cell line). Neither line expresses XIST (Supplementary file 1), suggesting that all X chromosomes are active. Figure 2A–B shows results from individual A549 and HeLa cells with two and three X’s. Figure 2C–D shows the X karyotype distributions. While there is a modest degree of heterogeneity within each line, almost all cells have two or more active X chromosomes. While labor intensity of assays and cell availability limited our sample size, we nevertheless can conclude that within-cell line heterogeneity does not seem to undermine our conclusions.
With an extra copy of the active X, the ‘expression phenotype’ is expected to change. The ratio of the median gene expression on the X to that on the autosomes(EX/A) is of particular interest. EX/A has been reported to be around 0.5 ~ 0.8 for normal mammalian tissues (Xiong et al., 2010; Deng et al., 2011; Kharchenko et al., 2011). We assayed EX/A by separating lines derived from cancerous and normal tissues. Figure 3A shows that EX/A distributions center on ~ 0.84 in normal cell lines and on one in cancerous cell lines. Given the controversy in the assay of EX/A, we also varied the threshold for counting expressed transcripts (see Materials and methods). By varying the threshold (Figure 3B), EX/A ranges from 0.78 to 1.05 in normal cell lines but is consistently higher by approximately 15% in cancer cell lines. The same pattern is seen in the RNA-seq data (Figure 3—figure supplement 1).
While sex chromosomes evolve, autosomes should also evolve. Since the generation of aneuploidy may happen independently for each autosome, a key question is whether selection operates on the autosomes as a set. Does natural selection favor cells that have full sets of autosomes?
Figure 4A shows the distribution of chromosome number across the 620 cell lines we studied. Apparently, cancerous cell lines acquire autosomes during evolution. The distribution of ploidy (n = 22) number shows peaks at 2 and 3, indicates that many cell lines appear to be in transition between full diploidy and triploidy of 44 and 66 autosomes. Similarly, the majority of sublines of HeLa cells we examined have 55–75 chromosomes centering about the triploid count of 69 (Figure 4—figure supplement 1). Indeed, autosomes appear to exist as a full complement with n = 22. Although autosomes may evolve as a set, cells most likely add one autosome at a time. It is hence desirable to track each chromosome individually. Single cells were individually isolated from a HeLa cell line and subsequently grown to a sub-line of 106 cells. We subjected six such sub-lines to whole genome sequencing such that each chromosome could be tracked individually. Smaller chromosomes are indeed more erratic in their numbers in cell lines. Only the largest 14 chromosomes (13 autosomes and X), which together account for ~75% of the genome, are used to test the convergence of autosomes. The cutoff is based on the observation that chromosome 13 is the largest autosome yielding viable trisomic new-borns (Taylor, 1968; Patterson, 2009; Kleijer et al., 2006). We reason that, if whole organisms can survive trisomy, the fitness consequence of the particular aneuploidy would probably be very small at the cellular level.
In all 6 lines, each of the 13 autosomes has 2–4 copies, ranging from an average of 2.62 to 3.23 (Supplementary file 2). If each autosome behaves independently, the number of autosomes that increase by x copies (x = 0, 1, 2 etc.) should follow a Poisson distribution with a mean of λ. Two different lines, with λ = 10/13 and λ = 16/13, are shown in Figure 4B and C. In the former, all cells have x = 0 or x = 1 and, in the latter, all cells have x = 1 or x = 2 (Supplementary file 2). The data suggest that each autosome increases by one copy and only after all of the 13 autosomes have gained an extra copy do further increases continue. Figure 4—figure supplement 1 shows the composite distribution of the five lines with λ < 1. The pattern, like that of Figure 4B, is statistically significant (p=0.0021 by the χ2 test) with an excess at x = 1. These results suggest that the larger autosomes evolve cohesively as a set. With autosomes evolving as a cohesive unit, X:A can be represented by whole numbers of 1:2, 2:3 etc.
We now summarize the evolution of cell lines by their C(Xa:A) genotypes. C(Xa:A) is the number of active X chromosomes and the ploidy number of autosomes (in multiples of 22) and is equal to C(1:2) in normal cells. For the purpose of counting active Xa’s, data from most male lines are usable. For female lines, only data from the LOH lines of the X can be used. Between the two sexes, C(Xa:A) distributions are very similar and the combined distribution is used in the analysis (Figure 4—figure supplement 2).
Shown in Figure 4D, most lines have the C(1:2) or C(2:3) genotype which together account for 2/3 of the lines. Given that C(1:2) is the starting genotype, its common occurrence at 37.4% is not surprising. The high frequency of C(2:3), however, is unexpected. To reach C(2:3) from the starting point of C(1:2), cells should evolve to either C(2:2) or C(1:3) first, but neither genotype is commonly seen in these cells lines. In contrast, C(2:3) at 29.2% is the second most common genotype. If we include the two genotypes, C(2:4) and C(3:3), that are derivatives of C(2:3), this inclusive C(2:3) cluster is the most common genotype. The model p the next section helps to interpret the observation.
The pathways of chromosomal evolution can be diagrammed as a series steps in Figure 5A. Each node represents a C(Xa:A) genotype, the abundance of which is reflected in the size of the node. Thicker arrows indicate faster transitions which add/delete one X while the thinner arrow denotes the slower transition of adding/deleting the whole set of autosomes. The fitness of each genotype, W, is assumed to be determined by the Xa/A ratio. In general, one would expect the wild type (W1) to be the fittest genotype and we particularly wish to know whether that is indeed the case here.
We first model the evolution under strict neutrality where all nodes have the same fitness. For simplicity, genotypes are grouped into 3 clusters centering around the 3 dominant genotypes, C(1:2), C(2:2) and C(2:3), the frequencies of which are x1, x2 and x3, respectively. Each cluster consists of the dominant genotype as well as the less common ones adjacent to it (see Figure 5A). For instance, x2 is the sum of the frequencies of C(2:2) and C(3:2) and x1 is those of C(1:2), C(1:1) and half of C(1:3). The frequency of the last one, being adjacent to both C(1:2) and C(2:3), is split between the two clusters. Tallying up the numbers in Figure 4D, we obtain x1 = 0.41, x2 = 0.092 and x3 = 0.482 with a total of 0.984, excluding the marginal genotypes. The analysis below can be expanded to account for each genotype separately. The transitions between clusters are defined as follows:
where u and v are the transition rates and is the frequency of cluster i at time T. Let be the vector of , expressed as
When T >> 0,
where z = ab + b+1. The genotype frequencies evolve toward the equilibrium, [ab, b, 1]/z, which depends on a and b, but not u and v. We posit that a > 1 and b > 1 because, as the chromosome number increases, the probability of chromosome gain/loss increases as well. By - Equation 2, x1(T) > x2(T) > x3(T) when T >> 0. In short, the relative frequency should be in the descending order of C(1:2), C(2:2) and C(2:3) if there is no fitness difference among genotypes. This predicted inequality at T >> 0 is very different from the observed trend.
Equation 2 assumes that cell lines have been evolving long enough to approach this equilibrium. A more appropriate representation should be X(T) where T reflects the time a cell line has been in culture. It is algebraically simpler if T is measured by the rate of chromosomal changes, u or v, rather than by the actual cell generation (Equation 1, Figure 5B and legends). We also assume u > v as u involves only the X but v involves the whole set of autosomes. With the initial condition of X(0) = [1,0,0], Figure 5B shows that the C(2:3) cluster approaches the equilibrium more slowly than the other two clusters. Therefore, the observed high frequency of the C(2:3) cluster (x3 = 0.482 vs. x1 = 0.41 and x2 = 0.092) is incompatible with a neutrally evolving model of chromosome numbers. The discrepancy is true at all time points and is more pronounced at smaller T’s.
Rejecting the neutral evolution model, we now incorporate fitness differences into Figure 5A with W1 = 1 [for C(1:2) and C(2:4)], W2 = 1 + s [for C(2:2)] and W3 = 1 + t [for C(2:3)] where s and t can either be positive or negative. Here, we add a fourth genotype, C(2:4). In the supplement, we model 4 genotypes with x1 – x4 for the frequencies of C(1:2), C(2:2), C(2:3) and C(2:4) respectively. An expanded transition matrix is used to model selection, followed by a normalization step (Supplementary file 3, Equation S1). The solution in the form of X(T)=X(0) MT is given in Supplementary file 3 (Equation S2) and the equilibrium X(T) is given in Supplementary file 3 (Equation S3).
We are particularly interested in whether t > 0 in the 4-genotype model, that is, whether C(2:3) has a higher fitness than the wild type, C(1:2). We observe that [x1, x2, x3, x4] = [0.374, 0.087, 0,292, 0.128] where x3 = 0.292 is more than 3 times higher than x2 = 0.087 and is close to x1 = 0.374. Equation S3 shows that s < 0 is necessary for x2 to be smaller than x3, and t > 0 is necessary for x3 to be close to x1 (see Supplement). Figure 5C is an example in which s = −0.5 and t = 0.5. The equilibrium at T >> 0 is indeed close to the observed values.
In conclusion, it appears that the extant state in multicellular organisms of C(1:2) is not the fittest genotype for free-living mammalian cells. The observed genotypic distributions suggest that C(2:3) may have a higher fitness than the wild type, C(1:2).
Free-living mammalian cells like all living things speed up the evolution when the environment changes. The practice of cell culturing, however, is to slow down the evolution to preserve cell lines’ usefulness as proxies for the source tissues. Nevertheless, changes are inevitable and the evolution of sex chromosomes is but one example. It should be noted that cell lines derived from cancerous tissues and normal tissues are different in one important aspect. Cell lines derived from normal tissues generally do not undergo karyotypic changes at an appreciable rate (Shirley et al., 2012; Frazer et al., 2007; Pickrell et al., 2010). They are therefore much less responsive to selection in cultured conditions that favor new karyotypes. Cancer cell lines, having been through more rounds of passages, have generally experienced stronger selection more frequently than normal cell lines.
Our observations suggest that the extant X:A relationship (C(1:2)) may not be optimal for free-living mammalian cells. The highest fitness peak, instead, appears to be closer to the karyotype of C(2:3) as free-living cells reproducibly evolve toward this new karyotype. The fitness peaks in free-living cells being different from that of the multi-cellular organisms is not unexpected. With many possible conflicts between individual cells and the community of cells (i.e., the organism), the interest of the community may lie in its ability to regulate the growth potential of its constituents. Free-living cells, on the other hand, are driven by selection to realize their individual proliferative capacity relative to other cells.
The convergence among these many cell lines to C(2:3) is unexpected in the context of cancer evolution. The TCGA project (reference) has shown that cancer evolution is a process of divergence, not convergence. Indeed, only two genes have been mutated in more than 10% of all cancer cases and tumors of the same tissue origin from two different patients may often share no mutated genes at all (Wu et al., 2016; Kandoth et al., 2013). Therefore, the karyotypic convergence reported here is rather unusual.
We note that C(2:3) toward which cultured cells evolved happens to be the smallest possible increase in the X/A ratio from C(1:2). The higher fitness of C(2:3) than C(1:2) in free-living cells may lend new clues to the debate about the evolution of mammalian sex chromosomes (Kharchenko et al., 2011; Lin et al., 2012). With X-inactivation, it has been suggested that EX/A could have been reduced, or even halved (Xiong et al., 2010; Lin et al., 2012). The debate is about whether, and by how much, EX/A might have increased in evolution. Our observation that free-living cells continue to evolve toward C(2:3) raised the possibility that the evolutionary increase in EX/A has not been complete, in comparison with the ancestral EX/A.
Finally, this study of cancerous cell lines may also have medical implications. The common view that tumorigenesis is an evolutionary phenomenon posits that individual cells in tumors evolve to enhance self-interest (Nowell, 1976; Merlo et al., 2006; Chen et al., 2015; Chen and He, 2016). A corollary would be that tumorigenesis may have taken the first few steps toward uni-cellularity. This extended view is supported by many expression studies as well as the higher likelihood of obtaining cell lines from tumors than from normal tissues (Hayflick, 1998). An alternative view, posits that tumors remain multi-cellular in organization (Almendro et al., 2013). These different views have been critically examined recently (Wu et al., 2016). It is possible that cancer cells in vivo may have been gradually evolving toward a new optimum. In that case, cancer cells in men and women are converging in their sex chromosome evolution and become more efficient in proliferation in this new de-sexualized state.
The processing of clonal expansion and whole genome sequencing of HeLa lines are described at Zhang et. al. (https://www.biorxiv.org/content/early/2017/10/05/193482). For each line, the copies of each chromosome are estimated according to the average sequencing depth by Control-FREEC, a tool for assessing copy number using next generation sequencing data (Boeva et al., 2012).
Genome-wide SNP array data on cancer cell lines and a normal training set were downloaded from The Wellcome Trust Sanger Institute under the data transfer agreement. Among the 755 cancer cell lines, 620 (from 279 females and 341 males) with available gender information were used for genotype information analysis in the present study. The details of these cell lines are shown in Supplementary file 4 . The processed data are in PICNIC output file format, which includes information on genotype, loss of heterozygosity and absolute allelic copy number segmentation (Greenman et al., 2010). Greenman et. al. developed the algorithm, PICNIC (Predicting Integral Copy Number In Cancer), to predict absolute allelic copy number variation in cancer (Greenman et al., 2010). This algorithm improved the normalization of the data and the determination of the underlying copy number of each segment. It has been used for Affymetrix genome-wide SNP6.0 data from 755 cancer cell lines, which were derived from 32 tissues. The Affymetrix Genome-Wide SNP Array 6.0 has 1.8 million genetic markers, including more than 900,000 single nucleotide polymorphism probes (SNP probes) and more than 900,000 probes for the detection of copy number variation (CN probes).
The genome-wide gene expression data for 947 human cancer cell lines from 36 tumor types were generated by Barretina et al (Barretina et al., 2012), as part of the cancer cell line Encyclopedia (CCLE) project using Affymetrix U133 plus 2.0 arrays and are available from the CCLE project website (CCLE_Expression_Entrez_2012-09-29.gct, http://www.broadinstitute.org/ccle/home). The expression profiles of 768 cell lines with gender information, representing 337 females and 431 males, were used in this study. These cell lines were partially overlapped with the lines used in Greenman et. al. Additionally, RNA-seq data from 41 lymphoblastoid cell lines from 17 females and 24 males were downloaded from GEO database (GSE16921) (Cheung et al., 2010). The details of these cell lines are shown in Supplementary file 5.
Human genomes harbor single nucleoid polymorphisms (SNPs) at a density of about 0.5–1 SNP per kb. When a large segment of chromosome is lost in somatic cells, the corresponding region would be devoid of SNPs, referred to as loss of heterozygosity (LOH). LOH regions may regain the copy number but the lost heterozygosity cannot be regained.
We used the genotype information and the allelic copy number estimation generated from PICNIC to infer LOH as well as copy number of a specific chromosome. As for a chromosome, if ≥ 95% of SNP sites were homologous we considered that there was a LOH (loss of heterogeneity) event for this chromosome. Similarly, if ≥ 95% of detected alleles on the chromosome had a constant copy number of 0, 1, 2, 3 or 4, the copy number would be considered as the copy number of the chromosome. The copy number of the Y chromosome was estimated separately. In females, although all sites on Y chromosome should have yielded 0 copies, only ~ 60% of sites detected by the Y chromosome probes showed a copy number of 0. This result indicated that several X homologous regions on the Y were covered by ~ 30% of Y probes. Therefore, Y chromosome loss was defined as when more than 60% of SNP probes from the Y chromosome showed a copy number of 0.
The expression level of XIST can be used as a proxy to distinguish the active X chromosome from the silent one as this gene was expressed on the inactive X chromosome and functioned in cis (Richardson et al., 2006). According to Greenman’s and Barretina’s studies, 496 cancer cell lines have both copy number and expression data. As expected, XIST was silenced in male cell lines, as well as in females with whole X chromosome LOH (Figure 1C). Based on X chromosome LOH and copy number information, we identified five genotypes, including XaO (female lines with one X = 20 lines), XaXa (female lines with isodisomy of X = 17 lines), XaXb (female lines with heterozygous for the X = 28 lines), Xa[Y] (male lines with one X = 53 lines) and XaXa[Y] (male lines with two X's = 69 lines).
All male (341 lines) and female cell lines with whole X chromosome LOH (103 lines) were employed for C(Xa:A) calculation. C(Xa:A) was defined as the ratio of absolute X copy number to that of all autosomes.
EX/A was defined as the ratio of the expression of X-linked genes to that of autosomal ones. The median values of expressed X-linked and autosomal genes were used to calculate EX/A in both cancerous and normal cell lines. For the datasets from the Affymetrix U133 + 2.0 array, genes with signal intensities ≥ 32 (log2 ≥ 5) were considered to be expressed. While as for RNA-seq data, genes with RPKM values ≥ 1 were considered to be expressed.
Previous studies have shown that EX/A value may be affected by gene set used (Deng et al., 2011). In addition, several silent genes in normal tissues have been shown to be expressed in tumor tissues (Hofmann et al., 2008). Those genes were dominant on X chromosome, which could result in an increase of EX/A. To exclude the possibility that EX/A ratios may be biased in cancerous cell lines, gene sets for EX/A calculation were first selected in normal cell lines by three criteria, with the same sets then selected in cancerous cell lines. The three filtering criteria for gene set selection were RPKM > 0, 1, and 5 in normal cell lines (Figure 2C).
To explore the impact of extra X chromosome on gene expression levels of X-linked genes, 53 cell lines with Xa[Y] and expression data, 69 cell lines with XaXa[Y] and expression data were used. T-test with Benjamini and Hochberg adjusting method was employed to determine genes, the expression of which are significantly changed due to an extra X copy. 648 detected X-linked genes are plotted in Figure 2A. The free statistical programming language R was used for the statistical analysis (version 3.0.1).
HeLa cells (from the Culture Collection of the Chinese Academy of Sciences, Shanghai, China) were cultured in DMEM (Life Technologies ,CA, United State) supplemented with 10% fetal bovine serum (FBS), 100 U/ml of penicillin, and 100 μg/ml of streptomycin. A549 cells (from ATCC) were cultured in RPMI-1640 (Life Technologies) with 10% fetal bovine serum (FBS), 100 U/ml of penicillin, and 100 μg/ml of streptomycin at 37°C with 5% CO2. Approximately 2 × 106 cells were seeded and cultured in 10 cm dishes with 10 ml growth medium as described above. To synchronize the cells, 200 μl of thymidine (100 mM) was added to the cells. After incubating for 14 hr, the cells were washed twice with 10 ml PBS and then supplemented with 10 ml growth medium containing deoxycytidine (24 μM). After incubating for 2 hr, 10 µl nocodazole (100 μg/ml) was added to the cells. The cells were incubated for an additional 10 hr.
After synchronization, cells were harvested and treated with 4 ml hypotonic solution (75 mM, KCl) pre-warmed to 37°C for 30 min. The cells were then fixed via three immersions in fresh ﬁxative solution (3:1 methanol:acetic acid) (15 min each time). The fixed cell suspension was spotted onto a clean microscope slide and allowed to air dry. We used the ‘‘XCyting Chromosome Paints’’ and ‘Xcyting Centromere Enumeration Probe’ (MetaSystems, Germany) for whole X chromosomes and centromere of X chromosome fluorescence in situ hybridization (FISH) analysis, respectively. Following the manufacturer's instructions, 10 µl of probe mixture was added to the prepared slide. The slide was then covered with 22 × 22 mm2 cover slip and sealed with rubber cement. Next, the slide was heated at 75°C for 2 min on a hotplate to denature the sample and probes simultaneously, followed by incubation in a humidified chamber at 37°C overnight for hybridization. After hybridization, the slide was washed in 0.4 x SSC (pH 7.0) at 72°C for 2 min, then in 2 x SSC and 0.05% Tween-20 (pH 7.0) at room temperature for 30 s, before being rinsed briefly in distilled water to avoid crystal formation. The slide was drained and allowed to air dry. Finally, 5 µl DAPI (MetaSystems) was applied to the hybridization region and covered with a coverslip. The slide was processed and captured using fluorescence microscopy as recommended (Olympus FV1000, 100X objective). The number of Xs were counted for each individual cell. A total of 343 HeLa cells and 170 A549 cells were screened.
The identification of HeLa cells was confirmed by genome sequence method and the identification of A549 cells was confirmed by karyotype profile. The mycoplasma contamination status was tested by DNA staining for both HeLa and A549.
In the model, autosomes are treated as an integrated set, labeled ‘A’ and counted as a set. There may be two reasons to do so. One is mechanistic if the entire haploid set of chromosomes increases a unit. While this may happen in organismal evolution, we consider the mechanism dubious for cell lines. In the absence of meiosis, whole-sale changes should involve the entire diploid set (diploids, tetraploids and octoploids, as in human hepatocytes).
We therefore suggest that chromosomes are gained and lost individually. They evolve more or less as a cohesive set in the long run thanks to natural selection that imposes a cost on uneven sets. For autosomes, the dynamics is portrayed in Figure 4B–C. When autosomes are gained, say from nA = 2 to nA = 3 (nA being the number of autosomal haploid set), the imbalance within the autosomal set appears to be tolerated only to a point. Let ∆ij (i, j = 1, 2,. . 13 for largest 13 autosomes) designate the difference in the number between autosome i and autosome j. In a balanced set, all ∆ij = 0 which represents a fitness peak when all autosomes have the same number. During evolution, ∆ij = 1, having a reduced fitness, can be tolerated but not ∆ij >= 2. (The constraint appears to be loosened for the smaller autosomes.) Thus, the autosomal set evolves between integers of nA = 1–3, with the occasional nA = 4 (see Figure 4D). Obviously, moving nA from one whole number to the next is a slow process. In Equation (1) of the main text, u represents the change in the number of X and v represents the change in nA. In testing the model, we let u = 10 v but the results are not sensitive to the ratio.
Assuming all genotypes have whole numbers of X and A, we assign a fitness to all genotypes of Figure 4A. Under neutrality, these genotypes have the same fitness. The main goal of the modeling work (see Figure 5B and C) is to test the fitness neutrality of these ‘whole number’ genotypes.
When we attribute the observed genetic changes associated with cells’ unicellular existence, we do include all environmental factors that make the unicellular existence possible. Without these factors, cell lines cannot live. An analogy is the study of the evolution of social structure, which is also conditional on many environmental factors (e.g., food supply) but one often uses ‘social structure’ as an all-encompassing term. Since the unicellular existence requires a number of environmental factors (which the cell culture community has been keen to identify), it is not possible to separate ‘unicellularty’ and the environments needed to sustain the unicellular existence. It is also important to point out that these environmental factors are often antagonistic to the multicellular living.
In this study, we use the model to compare the observations with the neutral expectation. Although we could reject the neutral model and conclude the direction of selection, we refrain from estimating the strength of selection for two reasons – both biological and technical.
First, the most important demonstration is that the wildtype C(Xa:A)=1:2 is not the fittest genotype. We believe this conclusion in itself is very novel because all evolutionary theories posit the wildtype to be at a local fitness optimum. We could conclude that the wild type C(Xa:A)=1:2 is less fit than C(Xa:A)=2:3 based on Figure 5B.
Second, while we could conclude C(Xa:A)=1:2 to be less fit than C(Xa:A)=2:3, estimating the strength of selection is an entirely different proposition. In this case, the main unknown is vT in Figure 5B and C. In other words, we do not know how close each cell line is to the equilibrium. Given the various histories of these cell lines, we suspect that the value may range between 0.1 and 10. The differences are qualitatively consistent but the actual values will require knowing the precise culture history of each cell line.
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Diethard TautzReviewing Editor; Max-Planck Institute for Evolutionary Biology, Germany
In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.
Thank you for submitting your article "Free-living human cells reconfigure their chromosomes in the evolution back to uni-cellularity" for consideration by eLife. Your article has been reviewed by two peer reviewers, and the evaluation has been overseen by Diethard Tautz as the Reviewing and Senior Editor. The following individual involved in review of your submission has agreed to reveal his identity: Harmit S Malik.
The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.
This is a highly provocative manuscript that argues that the chromosomal competition of cells in multicellular organisms is selectively constrained (X:A = 1:2) from achieving what would be a fitness optimum were these cells allowed to be selected purely for their unicellular survival (X:A = 2:3). This convergence is highly unexpected given the divergent sources of the various cell lines analyzed and given that they each carry different driver mutations. Although only a few instances are characterized at the karyotypic level by FISH or chromosomal paints, the SNP array based conclusions are highly significant and unexpected.
However, in spite of the potentially very interesting finding, the reviewers felt that a much clearer presentation is needed in some places. In fact, because of the partial lack of detail in the present manuscript, it is not yet clear whether it can be accepted after these details and clarifications are made. Hence the revised version will need re-review.
Most of the concerns stem from the very cursory style of writing of the manuscript. Please note that the imposed word limit is not necessarily strict, but only a recommendation. Alternatively, some more extended descriptions of details could be shifted to supplementary material. Generally it is necessary to do a better explanation of the methods and the rationale, and most importantly, the caveats.
1) What you refer to as non-cancer cell lines have in fact been immortalized either by telomerase or EBV; they are thus poor surrogates for 'normal cells' but closer to normal than cancer cells that have already undergone karyotypic changes. A table listing all the cell lines is required, including a discussion of the associated caveats.
2) What is the rationale for the expectation that X chromosomes should only be lost at 15%- if this is based on size, this can be better explained using Figure 1A.
3) The models that invoke or do not invoke selection can be made more useful where there are means to derive selective coefficients. It appears this is what you headed towards with the transition model, but falls short and instead just concludes that selection must play a role to arrive at a X:A= 2:3 ratio. (see also point 10 below).
4) How have all the cells been cultured? If under serum free or serum rich conditions, then does this factor explain some of the divergence (spread) in the X:A ratios observed. Table 1 that lists the cell lines could make this clear, and this will also allow falsification of the hypothesis that in fact the cells are convergently evolving towards serum-rich media conditions rather than to 'unicellularity' (generically speaking). Sparse description of media and growth conditions is one of the glaring omissions in the present manuscript.
5) It is not acceptable that a critical piece of the data from the HeLa cells is from reference to another unpublished manuscript. At the very least this manuscript should be provided to the reviewers, or the relevant data included in this manuscript (see also point 8 below).
6) The assumption that autosomes are gained individually seems unjustified (even if it’s only for the sake of argument) – more likely failure to undergo proper chromosome segregation led to ploidy changes following individual autosomes might have been lost. While the rationale to only focus on large autosomes seems reasonable, data on the smaller autosomes and their variance should be made available to the reader even if only in figure supplements.
7) The descriptions of the female cell lines are a bit confusing. The general statement or conclusion is that for the female lines with additional X chromosomes, the active (Xa) chromosome is gained. This is important because gains of the inactive X chromosome (silenced) should not confer much selection. However, in Figure 1F, XIST expression (associated with X-chromosome inactivation) is observed in most (N=103) cell lines. Also, in Figure 1E, only 20% of the female cell lines are illustrated – what is the status of the other 80% of the cell lines? More clarity is needed for evaluation.
8) The HeLa cell line studies are a bit confusing. Figure 2D is used to state that "within-cell heterogeneity does not seem to undermine our conclusions". Then 6 single HeLa cells were isolated and grown to a million cells (subsection “The concerted evolution of autosomes as a set”, second paragraph). WGS was used to evaluate ploidy, with the conclusion that larger (chr 1-13) evolve "cohesively" as a set (see the aforementioned subsection, last paragraph). This is a very interesting observation and conclusion, but the numbers (N=6) are small. However, if true, it should be possible to karyotype single cells to see if the expected 1:2, 2:3 X:A ratios are observed, as predicted. This data is already available (Figure 2B) and could just be organized and presented. This data might be already presented in Figure 4—figure supplements 1 and 2, but there are male and female peaks in it Figure 4—figure supplement 2, but Figure 4—figure supplement 1 is about the HeLa (female) cell line, so the sources of the male peaks are uncertain. Clarification is needed.
9) The reasoning for Figure 4D should also account for a potential bias in the sense that for the X;A ratios, typically X = 1 or 2 chromosomes and A = 22 or more chromosomes. That X can change when a single chromosome changes ploidy, but A requires 22 chromosomes to change uniformly, a less likely scenario which should be discussed or clarified.
10) Along these lines, the modeling in Figure 5 illustrates "A" as a single entity with integer moves. Given the large number of autosomal chromosomes, the feasibility of this model should be further clarified.
Overall, the X:A ratios are complicated because although one can calculate an average "A" value, the complex rearranged nature of many cancer cell chromosomes (where chromosomes may be hybrids of many different autosomes) can render the meaning of "A" a bit uncertain. It would be useful to present exactly how many cell lines are actually X:A1:2 versus 2:3 versus neither.https://doi.org/10.7554/eLife.28070.026
- Chung-I Wu
- Xuemei Lu
- Chung-I Wu
- Chung-I Wu
- Xuemei Lu
- Chung-I Wu
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
We thank Jian Lu and Xionglei He for comments on an earlier version of the manuscript. Additionally, we thank Yang Shen, Yong E Zhang, Rui Zhang and Wenfeng Qian for numerous constructive discussions.
- Diethard Tautz, Reviewing Editor, Max-Planck Institute for Evolutionary Biology, Germany
- Received: June 20, 2017
- Accepted: October 28, 2017
- Version of Record published: December 18, 2017 (version 1)
© 2017, Xu et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.