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http://journals.tubitak.gov.tr/biology/

Turkish Journal of Biology Turk J Biol(2014) 38: 880-897© TÜBİTAKdoi:10.3906/biy-1404-1

Identification of new genes required for the maintenance of chromosome integrity in Drosophila melanogaster

Francesca CEPRANI1, Franco SPIRITO1, Roberto PIERGENTILI2,*1Department of Biology and Biotechnologies, Sapienza University of Rome, Rome, Italy

2Institute of Biology, Molecular Medicine, and Nanobiotechnologies at the National Research Council (CNR), Sapienza University of Rome, Rome, Italy

* Correspondence: roberto.piergentili@uniroma1.it

1. IntroductionRNA-interference (RNAi) is experimentally used for gene silencing via a double-stranded RNA (dsRNA) targeting a complementary messenger RNA (mRNA) and promoting its degradation by a nuclease-dependent cut. Although this acronym was first used by Fire et al. (1998) in studying the model organism Caenorhabditis elegans, this biological phenomenon had already been observed before, though not completely understood, in several organisms such as transgenic plants (Ecker and Davis, 1986; Napoli et al., 1990), Neurospora crassa (Romano and Macino, 1992), Caenorhabditis elegans (Guo and Kemphues, 1995), and Drosophila melanogaster (Pal-Bhadra et al., 1997), indicating that RNAi is widely present in most eukaryotes. Notably, some eukaryotes lack all or most of the RNAi machinery; among others, these include some protozoa (Robinson and Beverley, 2003; DaRocha et al., 2004), and several fungi including Saccharomyces cerevisiae (Aravind et al., 2000; Nakayashiki et al., 2006; Drinnenberg et al., 2009). Some researchers suggest that this mechanism – probably a defense against exogenous, potentially harmful dsRNA such as that of viruses or transposable elements

– evolved very early and was already present in the first, primitive eukaryotes; as a result, organisms not showing RNAi probably lost it during evolution (Cerutti and Casas-Mollano, 2006). The use of RNAi has been largely and successfully used for the analysis of single gene silencing as well as for the screening of genome-wide collections of genes, showing that the resulting phenotype is usually highly specific and penetrant (Mohr et al., 2010). This approach allowed achieving extremely important results, especially in the investigation of basic cell life phenomena such as metabolism, mitosis and cytokinesis, chromosome structure and behavior, and mitotic spindle functions. Remarkably, in most screenings RNAi is not used to identify genes required to maintain mitotic chromosome integrity, principally because this type of analysis cannot be automated (Conrad and Gerlich, 2010). Thus, genome-wide screenings for this phenotype are largely missing from the scientific literature.

The DNA of living cells is subject to many types of molecular lesions, including base modifications, single- and double-strand breaks, and intra- and interstrand cross-links between bases. Double-stranded DNA breaks (DSBs)

Abstract: Although genome-wide RNA interference (RNAi) screens for mitotic genes are not new in the literature, most of them lack the cytological characterization of the cell karyotype as for chromosome integrity. Here, the effects of RNAi on chromosome structure in S2 cultured cells of Drosophila melanogaster were analyzed. The cytological phenotype of 1132 genes selected by coexpression with known mitotic genes was scored. Cytological and statistical analysis of the treated cells allowed the identifying of 81 loci whose inactivation brings a level of chromosome breakage significantly higher than in the control. Many of the genes characterized in the present work had never been associated with a cellular function; in other cases, their putative role is apparently unrelated to the chromosome breakage phenotype. These results suggest novel biological roles for the proteins encoded by the identified genes and indicate that the number of loci required for chromosome integrity is much larger than expected. Moreover, these results strongly suggest that the compilation of a list of coexpressed genes for any given function would result in a largely incomplete set of data, and that quite surprisingly a more complete collection of loci may be obtained using screening criteria different from selection.

Key words: Cancer, functional coexpression, genome instability, karyotype, S2 cultured cells, segmental aneuploidy, RNAi

Received: 01.04.2014 Accepted: 21.07.2014 Published Online: 24.11.2014 Printed: 22.12.2014

Research Article

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are probably the most deleterious lesions, as they can result in chromosome aberrations (CAs), cell death, and neoplastic transformation (Khanna and Jackson, 2001; van Gent et al., 2001; Mills et al., 2003). To counteract the effects of DSBs, living organisms have evolved 2 main mechanisms for repairing their DNA: homologous recombination (HR) (San Filippo et al., 2008) and nonhomologous end joining (NHEJ) (Lieber, 2010). In the HR pathway the broken ends undergo a recombinational process with the undamaged sequence of either the sister chromatid or the homologous chromosome, which is used as a template for accurate DSB repairs. In the NHEJ pathway, after a limited degradation (Huertas, 2010), broken ends are ligated irrespective of homology, thus resulting in a small sequence deletion at the joining site. The latter mechanism is intrinsically error-prone, and in addition to the aforesaid deletion it may also lead to various types of chromosomal rearrangements, such as transpositions and reciprocal translocations (Lieber et al., 2006; Weinstock et al., 2006). Experimental analyses using ionizing radiation and restriction enzymes have shown that DSBs are the principal lesions leading to the formation of CAs (Natarajan and Obe, 1978; Obe et al., 1992; Vamvakas et al., 1997; Richardson and Jasin, 2000; Obe et al., 2002; Tsai and Lieber, 2010). Interestingly, after a first burst, a second round of radiation-derived CAs may appear several cell generations after the first genomic insult, indicating that its effects on genome stability might become evident even after a long time (Streffer, 2010). Because of the lack of homology, the erroneous ligation of 2 centromere-containing broken DNA ends by NHEJ leads to the formation of a dicentric chromosome able to start the break-fusion-bridge cycle (McClintock, 1951), which in turn makes CAs more complex. CAs were associated with neoplastic transformation in man a long time ago (Nowell and Hungerford, 1960; Levan, 1967; Rowley, 1973; Zech et al., 1976; Fukuhara et al., 1979; Hatano et al., 1981; Tsujimoto et al., 1984; Finger et al., 1986; Rabbitts et al., 1988; Le Beau et al., 1993), frequently because of up-, down- or misregulation of genes important for DNA replication, DNA repair, checkpoint control, and mature ribonucleoprotein (mRNP) biogenesis (Aguilera and Gomez-Gonzalez, 2008; Clémenson and Marsolier-Kergoat, 2009; Kerzendorfer and O’Driscoll, 2009; Mitelman et al., 2010). Acentric fragments, lacking a centromeric region, are not able to correctly segregate during the cell division and can either be inherited by any of the daughter cells – irrespective of their gene content – or be lost. In most cases, the final output is the formation of a cell having a quantity of DNA that is larger or smaller than the normal complement; this situation is usually called segmental aneuploidy because only a fraction of one or a few chromosome(s) is genetically unbalanced. Interestingly, cells cope better

with polyploidy than aneuploidy; although chromosome number abnormalities are frequently associated with the neoplastic transformation (Torres et al., 2008; Williams and Amon, 2009), aneuploidy is a potential tool to target cancer cells, since even transformed cells are sensitive to genomic unbalances (Bannon and Mc Gee, 2009; Williams and Amon, 2009). As a general mechanism, aneuploidy – including segmental aneuploidy – leads to alterations of the gene copy number, and these alterations may influence not only the gene itself (and/or the protein it encodes) but also its molecular or functional interactors (Torres et al., 2008; Veitia et al., 2008; Henrichsen et al., 2009), which leads to the potential deregulation of tens of genes. In this perspective, it becomes crucial to identify those genes that determine the genome stability by controlling the chromosome integrity, since the effects of even a single un- or misrepaired DNA break may have harmful consequences. Of great help is the fact that these genes are widely conserved in most eukaryotes, so it is conceivable that studying them in a model system, such as cultured Drosophila S2 cells interfered with by RNAi, might provide important suggestions about the role of their human orthologs, with the advantage that in Drosophila the interference is easily achieved and the karyotype is simpler than in humans due to a reduced chromosome number.

In 2008 Somma et al. identified a number of genes having a role in mitosis. To achieve this result, they created a list of genes coexpressed with other, known mitotic genes. In order to evaluate the coexpression, they used the Pearson correlation coefficient and, since this variable goes between –1.00 and + 1.00 and its highest values express increasing levels of positive correlation, they focused on the range of [0.85, 1.00]. Specifically, Somma et al. evaluated the frequency of contemporary expression of a reference gene (first variable) against any other gene (second variable) of the D. melanogaster genome using microarray data from 89 different experiments. They repeated this analysis 6 times, using 6 reference genes connected to mitosis, and created a merged table of coexpression using the average value of each gene against the 6 reference genes. Finally, they analyzed the first approximately 1000 genes that had a Pearson correlation coefficient in the above mentioned range. The screening was performed by studying the cytological phenotype of cultured S2 cells after RNAi targeting of these genes. In the present work we applied RNAi against the same genes, but specifically focused on the presence of chromosome instability as a consequence of gene silencing. Our approach allowed the identification of a group of at least 81 genes whose silencing results in a chromosome breakage phenotype. Surprisingly, only less than one-third of them can be directly related to DNA metabolism, i.e. DNA replication and/or repair, nucleotide biosynthesis, and chromosome structure. This indicates

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that the majority of the loci we identified could not be predicted to produce such a phenotype upon silencing. We were able to group some of them into discrete classes, according to their function, and to identify a new phenotype, which we called multifragmented cells, showing extensive chromosome breakage upon RNAi-mediated silencing, and that is particularly strong for genes that have DNA replication-related functions. Unexpectedly, another class of genes was also highlighted by our experiments; they are apparently involved in the mitosis/apoptosis cell fate, but also destabilize the chromosome structure after silencing. Taken together, our results suggest that the genes involved in genome integrity are much more numerous than expected.

2. Materials and methods2.1. Double-stranded RNA synthesis and cell treatmentGenes were amplified by polymerase chain reaction (PCR) either using a mixed embryonic cDNA library (Brown and Kafatos, 1988) or genomic DNA. Each gene-specific primer used contained the 35-nt sequence for the T7 RNA polymerase binding site (5’-TAATACGACTCACTATAGGGAGG-3’) at the 5’ end. dsRNA was synthesized with an average length of 750 bp (minimum length: 600 bp) and analyzed as previously described (Somma et al., 2002, 2008). S2 cells were cultured at 25 °C in Shields and Sang M3 medium (Sigma) supplemented with 10% heat-inactivated fetal bovine serum (FBS, Invitrogen). Cells reared at 25 °C in complete medium were centrifuged, then resuspended at a concentration of 106 cells/mL with serum-free medium and finally plated in a 6-well culture dish (Sarstedt). Each culture was subsequently inoculated with 15 µg of dsRNA. Control cultures were prepared in the same way, in parallel, but no dsRNA was added. After 1 h of incubation at 25 °C, 2 mL of medium supplemented with 15% FBS was added to each culture. Both dsRNA-treated and control cells were grown for 72 h at 25 °C.2.2. Cytological preparations and image collectionCells from 3-mL cultures were interfered with for 3 days and then resuspended in the medium, and 1 mL of cell suspension was treated for 2 h with colchicine (colcemid at a final concentration of 10–5 M). Cells were then centrifuged at 1000 rpm for 5 min. Pelleted cells were washed in 10 mL of phosphate-buffered saline, spun down by centrifugation, and resuspended in 5 mL of hypotonic solution (0.5 M sodium-citrate) for 7 min. After further centrifugation at the same speed, pelleted cells were fixed in 5 mL of methanol and acetic acid (3:1), spun down again, and resuspended in the small volume of fixative left after the gentle removal of the supernatant. Ten microliters of this suspension was dropped onto a microscope slide and air-dried. All slides were mounted in Vectashield

with DAPI (Vector) to stain DNA. Images were captured using a CoolSnap CCD camera (Photometrics, Tucson, AZ, USA) connected to a Nikon Eclipse E600 fluorescence microscope equipped with an HBO 50-W mercury lamp.2.3. Statistical methodsFor the proportion of cells with aberrations, the comparison between each line with interference and the control entails the analysis of a 2 × 2 contingency table. The chi-square value with Yates’ correction for continuity and its associated probability were calculated. When the expected number in 1 or more of the table cells was less than 10, Fisher’s exact test was carried out and the 2-sided probability values were calculated using the method of small P-values, as described (Agresti, 1992).

The expected number of aberrations per cell in every pair of samples compared (the treated line considered and the control) was calculated assuming that the 2 samples were extracted from the same population with a Poisson distribution. The chi-square test (which is based on the difference between observed and expected values) was performed using Yates’ correction. When the expected number of aberrations was less than 10 in at least 1 of the cells of the table, the exact test based on the binomial distribution was performed. Additionally, in this case the 2-sided probability was calculated with the method of small P-values. For genes whose interference caused a significant effect, the possible nonrandom distribution inside the coexpression list was checked by the Mann–Whitney test, while the possible nonrandom location within the chromosome bands was established evaluating the significance of the deviations from a Poisson distribution (variance test).

Finally, whenever multiple tests were involved, in order to achieve a protection against errors of the first type, Holm’s sequential Bonferroni procedure was applied to check the statistical significance at the level of a P ≤ 0.05, as described (Holm, 1979).

3. Results3.1. Preliminary screeningOur initial analysis of S2 cells revealed that there is a considerable number of spontaneous CAs, including both chromatid/chromosome breaks and exchanges. This phenotype is not influenced by the use of non-Drosophila random dsRNA. In order to evaluate the absolute number of CAs and their variation, we performed a series of 45 different control experiments, in parallel with RNAi treatments, using cells grown for the same time and with the same medium, but without adding dsRNA. The results of these experiments are listed in Table 1. As shown, controls averaged 21 aberrant cells (ACs) and 23.7 CAs per 100 scored metaphases – the latter number being higher since occasionally 2 CAs are present in the same

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Table 1. Control S2 cells without dsRNA treatment (total: 45 independent experiments). Each row summarizes the results of a single experiment, performed in parallel with the RNAi; the last row (bold font) shows the total values (columns 1, 2, and 4) and the average values (columns 3 and 5) of the experiments. ACs: Aberrant cells; CAs: chromosome aberrations.

Number of metaphases scored per experiment Number of ACs ACs per 100 scored

metaphases Total CAs CAs per 100 scored metaphases

80 17 21.3 22 27.555 13 23.6 14 25.5100 23 23.0 26 26.0100 23 23.0 29 29.065 13 20.0 15 23.156 12 21.4 14 25.030 5 16.7 6 20.050 11 22.0 12 24.050 12 24.0 12 24.050 11 22.0 12 24.055 13 23.6 14 25.540 8 20.0 8 20.0100 23 23.0 23 23.080 17 21.3 22 27.556 12 21.4 14 25.030 5 16.7 6 20.050 12 24.0 12 24.050 11 22.0 12 24.050 11 22.0 12 24.050 9 18.0 9 18.040 9 22.5 10 25.050 11 22.0 13 26.040 9 22.5 10 25.020 6 30.0 6 30.022 3 13.6 4 18.231 7 22.6 7 22.620 3 15.0 3 15.030 4 13.3 6 20.025 5 20.0 6 24.030 6 20.0 7 23.320 4 20.0 4 20.065 13 20.0 15 23.1100 23 23.0 23 23.056 5 8.9 6 10.7100 22 22.0 26 26.020 4 20.0 5 25.070 17 24.3 20 28.611 2 18.2 2 18.220 4 20.0 4 20.030 5 16.7 6 20.058 12 20.7 14 24.140 4 10.0 6 15.020 5 25.0 5 25.030 6 20.0 7 23.320 4 20.0 4 20.02165 454 21.0 513 23.7

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metaphase. Analysis of these data using the chi-square test demonstrated that these numbers are very similar among different experiments (P > 0.9999 for both ACs and CAs), so the variability of this parameter is very low. In conclusion, it can be assumed that the basal level of ACs and CAs is a characteristic feature of this cell lineage and may be considered as a constant when evaluating the possible clastogenic effects of RNAi on a particular gene.

As a starting point for the search for genes that are important for chromosome integrity, the experiments described by Somma et al. (2008) were replicated to reproduce the same working conditions, i.e. the same interfering RNA obtained with the same primers was used,

and a corresponding amount of dsRNA was added to the cells, which were treated for an equivalent time. In total, the chromosome complement of 1132 interfered genes was cytologically analyzed. The quantitative data were then studied using the chi-square test with Yates’ correction. We found that none of the tested dsRNA was able to lower the number of either ACs or CAs compared to our controls. Instead, genes increasing the number of CAs and/or ACs upon interference, with a P-value of ≤0.05 in the single comparison with the control, are reported in Table 2. For each cell lineage, at least 50 metaphases in at least 2 different experiments were examined. The experiments characterized by less than 50 metaphases are those for

Table 2. List of the genes that impair chromosome integrity upon RNAi-mediated silencing, with 137 genes identified by RNAi and validated using the chi-square test with Yates’ correction. ACs: Aberrant cells; MCs: multifragmented cells; CAs: chromosome aberrations. Columns – 1: Rank of the gene according to the list by Somma et al. (2008); 2: locus identifier according to FlyBase; 3: other names of the gene/locus, according to the available literature; 4: human orthologous gene; 5: function of the gene as reported in FlyBase, Release FB2013_04 (see below for the meaning of each code); 6: total number of scored metaphases; 7: cells having at least one chromosome break/rearrangement; 8: number of cells with multiple, unscorable breaks and already counted in Column 7; 9: the asterisk marks loci positive for ACs after applying the Holm–Bonferroni correction; 10: total number of CAs, excluding those in MCs; 11: the asterisk marks loci positive for CAs after applying the Holm–Bonferroni correction. Rows in bold text highlight genes/loci that are positive only for ACs or for CAs according to chi-square test with Yates’ correction. Codes for the genetic functions in alphabetical order – A: General RNA metabolism; C: chromatin/chromosome structure; D: DNA damage repair, DNA recombination; E: import-export from nucleus; K: protein phosphorylation and dephosphorylation; M: mitotic spindle assembly; N: nucleotide/nucleoside metabolism; O: other functions; P: phagocytosis, engulfment; R: DNA replication; S: splicing, mRNA maturation/modification; T: transcription; U: ubiquitin and sumo metabolism, protein degradation; X: unknown function; Y: apoptosis; Z: translation. Each functional class was arbitrarily created when at least 3 genes fell inside it; in all other cases, the genes were assigned to the O (other functions) class.

RankLocus identifier (CG)

AliasesHuman orthologous gene

FunctionScored metaphases

ACs MCs

Holm–Bonferroni correction for ACs

Total CAs

Holm–Bonferroni correction for CAs

0 control --- 2165 454 0 --- 513 ---

4 9193 mus209 PCNA M R 257 111 17 * 163 *

5 15220 RPA3 --- R 100 38 8 47 *

11 8975 rnrS RRM2 N Y 165 90 7 * 168 *

12 10159 BEAF-32 --- C T 120 47 0 * 65 *

18 4978 mcm7 MCM7 R 174 121 24 * 164 *

20 9273 rpA2 RPA2 R 103 31 1 35

41 3178 rrp1 APEX1 D 142 47 2 62 *

46 8142 --- RFC4 O 100 23 0 35

84 7055 dalao SMARCE1 C T 113 35 0 35

104 1966 acf1 BAZ1A C 181 54 1 72 *

112 3642 clipper CPSF4L A 20 9 0 10

120 11979 rpb5 POLR2E T 83 31 0 45 *

126 2925 noisette SF3A3 E M 183 84 3 * 126 *

135 14999 rfC4 RFC4 C D R 142 51 0 79 *

142 9241 mcm10 MCM10 C R 104 35 0 40

151 12359 ulp1 SENP1 U 170 88 0 * 101 *

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152 4206 mcm3 MCM3 R 71 24 2 30

159 5553 DNApolα60 --- R 110 36 0 47

161 4654 dp TFDP1 T Y 322 110 2 * 128 *

164 8749snRNP-U1-70K

SNRNP70 M S 100 28 0 35

168 12050 --- WDR75 X 213 116 2 * 141 *

183 12113 intS4 INTS4 A 36 15 0 20

202 5452 dnk ENS400000260851 N 103 49 0 * 80 *

219 8068 su(var)2-10 PIAS1 C 250 84 2 * 143 *

231 13427 --- --- X 126 55 0 * 80 *

245 2848 trn-SR TNPO3 E S 156 54 1 68 *

264 7769 piccolo DDB1 D 201 72 5 * 99 *

271 11920 --- NDOR1 X 80 26 1 31

276 18528 --- GTPBP3 A 150 39 0 54

277 9633 rpA-70 RPA1 M R 70 63 47 * 54 *

294 10354 rat1 XRN1 O 274 99 3 * 133 *

300 31671 tho2 THOC2 A E 91 31 0 44 *

306 5198 holn1 CD2BP2 P 44 16 0 21

307 7833 orc5 ORC5L C M R 150 45 0 70 *

312 6349 DNApolα180 POLA1 R 159 76 1 * 106 *

337 17938 midway DGAT1 Y 125 54 0 * 58 *

353 14749 GLE1 GLE1 A E 151 64 1 * 78 *

386 17383 jigr1 --- T 237 80 0 * 101 *

387 6011 prp18 PRPF18 S 43 16 0 20

391 10667 orc1 ORC1 R 91 33 1 36

395 3736 okra RAD54B D 152 68 2 * 104 *

406 11906 --- --- X 151 63 0 * 76 *

407 7989 wicked UTP18 A 191 81 0 * 94 *

414 6249 csl4 EXOSC1 A 100 29 0 38

419 2260 --- WDR46 X 130 52 1 * 63 *

420 6121 tip60 KAT5 C D 100 36 0 45 *

422 2199 --- --- X 200 54 1 79 *

426 33095 --- --- X 105 28 0 36

427 8274 megator TPR M 86 43 0 * 79 *

429 5965 woc --- C O T 156 61 0 * 80 *

430 7993 --- RPF2 X 100 31 0 55 *

445 8711 cullin-4 CUL4A E U 200 108 8 * 204 *

454 9348 taf6 TAF6 T 198 79 0 * 106 *

456 8962 paf-AHα PAFAH1B3 O 150 49 0 57

462 9677 int6 EIF3E P Z 280 149 4 * 175 *

474 5370 dcp-1 CASP2 U Y 117 51 1 * 67 *

490 8989 his3.3b HIST1H3B C 188 90 2 * 125 *

Table 2. (Continued).

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493 1078 fip1 FIP1L1 A 150 58 0 * 67 *

495 2161 regena CNOT2 T 121 60 0 * 86 *

502 7014 rpS5b RPS5 Z 80 28 1 39 *

516 8878 --- --- K 171 71 1 * 91 *

517 4281 --- --- X 188 63 0 79 *

519 4788 --- --- X 150 43 0 54

520 13329 cid --- M 50 20 1 22

523 6340 --- RSRC2 X 85 26 0 32

529 9900 mit(1)15 ZW10 O 10 6 0 11 *

539 10754 --- 3F3A2 M 102 45 1 * 66 *

555 6197 --- XAB2 P S 70 25 1 40 *

561 2213 msd5 --- M 139 40 0 48

562 8426 l(2)NC136 CNOT3 T 106 35 6 40

585 5649 kin17 KIN S 150 39 0 56

595 17161 grapes CHEK1 D K M 61 24 1 28

596 3181 ts TYMS N 135 62 1 * 98 *

605 31111 --- TBRG1 X 233 69 0 78

607 11266 caper RBM39 S 251 72 0 100 *

611 5193 TfIIB GTF2B T 140 51 1 * 63 *

613 6693 --- DNAJC9 X 90 27 0 35

622 8950 --- GTF3C3 X 151 45 0 48

627 1017 Mfap1 MFAP1 O S 35 10 0 15

632 6453 --- PRKCSH X 225 65 1 84

652 8980 niPp1 PPP1R8 K 50 21 0 27 *

656 10689 l(2)37Cb DHX8 A 50 25 1 * 39 *

661 9473 MED6 MED6 T 75 24 0 32

672 8243 --- SMAP1 O 268 110 0 * 139 *

674 32708 --- ABT1 X 85 20 0 32

677 13849 nop56 NOP58 X 50 20 0 24

686 1939 dpck DCAKD O 160 67 0 * 85 *

689 18041 dgt1 KANSL2 X 50 24 0 * 38 *

693 17446 cfp1 CXXC1 C 99 39 0 * 50 *

708 5786 peter pan PPAN X 180 89 0 * 107 *

715 10542 bre1 RNF20 C P 100 31 0 40

725 10903 --- WBSCR22 X 118 37 0 51

729 5933 ime4 METTL3 A 210 81 0 * 91 *

734 5149 --- TLDC1 X 80 25 0 32

743 12135 c12.1 CWC15 M 146 80 1 * 123 *

758 9601 --- PNKP X 200 62 0 73

760 6946 glorund --- C Z 252 113 0 * 129 *

763 6480 frg1 FRG1 X 200 103 0 * 110 *

Table 2. (Continued).

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768 6937 --- NIFK X 245 91 0 * 129 *

772 7757 prp3 PRPF3 S 180 80 0 * 133 *

788 10042 MBD-R2 --- T 201 95 6 * 205 *

807 3351 mRpL11 MRPL11 Z 138 39 0 51

808 2685 --- WBP11 S 150 59 0 * 73 *

810 15019 --- --- X 121 36 0 45

811 12249 miranda --- O 93 32 1 36

823 4916 me31B DDX6 A 175 60 0 66

824 1676 cactin CACTIN X 301 107 0 * 121 *

829 4326 mRpS17 --- Z 86 24 0 36

843 6686 --- SART1 X 90 28 0 28

850 5408 tribbles TRIB1 K 189 59 0 68

853 6057 SMC1 SMC1A C 180 63 1 * 77 *

860 13625 --- BUD13 S 82 29 1 44 *

862 2177 zip103B SLC39A9 O 150 48 0 62 *

867 10139 --- --- X 160 46 0 50

878 7003 msh6 MSH6 D 138 50 0 * 62 *

882 32066 CG6487 FAM49A X 159 73 0 * 107 *

883 5102 daughterless --- D T 265 82 0 99 *

885 5923 DNApolα73 POLA2 R 164 50 0 56

893 4449 --- --- X 300 107 4 * 139 *

900 4785 intS14 VWA9 X 80 35 1 * 58 *

927 17528 --- DCX K 110 41 0 50 *

930 3735 --- DIEXF X 110 29 0 48

932 4173 septin-2 SEPT11 O 109 34 0 43

949 1710 hcf HCFC1 C T 130 46 0 51

967 17068 --- --- X 126 41 0 59 *

978 6854 CTP synthase CTPS1 N 120 54 0 * 91 *

983 6977 cadherin 87a --- O 140 40 0 53

986 7656 --- UBE2R2 U 76 20 2 28

994 4866 --- IMP3 Z 102 36 1 42

995 7081 pex2 PEX2 O 170 42 0 59

998 8169 pms2 PMS2 D 125 40 0 58 *

1002 18005 beag IK O 122 49 1 * 56 *

1021 6744 --- EXD2 D 157 42 0 52

1023 2050 modulo --- X 234 86 0 * 110 *

1057 18190 --- MAPRE1 O 152 48 0 61

1061 5454 snRNP-U1-C SNRPC M S 218 65 1 79

1073 9680 dbp73D DDX51 A 176 52 0 62

Table 2. (Continued).

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which the interfered cells show a mitotic index much lower than that of the control; in this case, RNAi was performed at least 3 times (and up to 4 times) and, if metaphases were still not enough, data were also analyzed using Fisher’s exact test (for ACs) and binomial distribution (for CAs) to confirm the results of the chi-square test. This allowed the identification of 137 genes (i.e. more than 12% of the 1132 scored) in which the number of ACs, the number of CAs, or both are significantly higher than in the control (P ≤ 0.05 in the single test). It is noteworthy that more than 80% of these genes (112 / 137) have a human counterpart (Table 2). Of great interest is the discovery that 34 of them (~25%), to date, have a completely unknown function, and 66 (~48%) have a function not directly related to DNA metabolism, since their annotation in FlyBase, the fruit fly database, does not report (as to Release 2013_04) for them a role in any of the following groups: i) chromatin/chromosome structure, ii) DNA damage repair, iii) DNA recombination, iv) DNA replication, v) nucleotide/nucleoside metabolism (Table 2). Consequently, only 27% (37/137) of the genes in the list could have been predicted, on the basis of their established function or of their homology with other known genes, as capable of altering

the karyotype structure. In conclusion, the number of genes important to maintain the stability of the genome is much greater than expected.

For the majority of the identified genes (117/137, 85.4%), both the number of ACs and the number of CAs per 100 metaphases is significantly increased, compared to control, using the chi-square test. However, for 20 genes (14.6%), this is not true (Table 2, see the genes highlighted in bold font). Of them, 16 (11.7%) showed a significant increase only in CA absolute number, and 4 (2.9%) only in the number of ACs. An analysis of the function of these 20 genes indicates that also in this case, many (8/20, 40%) are of unknown function, and only a few of them (2/20, 10%) are directly related to DNA metabolism. Thus, also in these cases it would have been impossible to guess their role in genome stability without some types of experimental evidence.

Finally, we also identified a phenotype that is not present in control cells and that we called multifragmented cells (MCs), i.e. cells showing such a high number of CAs that it does not allow the discerning of single chromosomes. This phenotype is easily recognizable: an average karyotype of S2 cells contains 12 chromosomes (Figure 1a), while

Figure 1. Cytology of S2 cells. A) Untreated control cell; note the chromosome complement, composed of 12 elements. B) An aberrant cell (AC) showing 1 chromosome involved in 2 rearrangements (CAs) at the same time, an X-type symmetric exchange (arrow) and a U-type asymmetric exchange (arrowhead). C) A multifragmented cell (MC); each asterisk marks a chromosome aberration (CA), either a single or a double chromatid break. Metaphases in panels B and C come from mus209-interfered cells. D, E, F) MC cells from rpA70-interfered cells; note the increasing level of chromosome fragmentation.

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in these cells the number of chromosomal structures, including acentric fragments, is far higher that 20 (our arbitrary threshold to separate ACs and MCs). Notably, in control cells this phenotype was never scored in more than 2000 metaphases. Instead, during the screening it was possible to identify 49/137 genes (35.8%) positive for this phenotype, up to a MC/AC ratio value of ~75% (47/63) as in the case of the RpA-70 coding gene (Tables 2 and 3; Figure 1). Notably, in previous reports this phenotype was identified only in RNAi experiments targeting RpA-70.

3.2. Identification of new genes involved in chromosome integrityIn the previous paragraph, the results of single comparisons between each interfered line and the control were shown. However, the analysis of hundreds of tests may cause the emergence of several false positives by chance. In order to obtain protection against an inflated error of the first type, and to maintain a P-value of ≤0.05 as our level of statistical significance, Holm’s sequential Bonferroni procedure for multiple testing was used. Genes significant at the 0.05

Table 3. Genes showing the MC phenotype after RNAi-mediated silencing. Genes are ranked according to their descending MC/AC ratio. Column 1: Gene locus; Column 2: gene aliases (FlyBase, Release 2013_04); Column 3: functions (they are the same as in Table 2); Column 4: MC/AC ratio; Column 5: Holm–Bonferroni correction positivity.

Locus (CG) Aliases Function MC/AC × 1000 Positive after Holm–Bonferroni correction

9633 rpA-70 M R 746 yes

15220 --- R 211 yes

4978 mcm7 R 198 yes

8426 l(2)NC136 T 171 no

9193 mus209 M R 153 yes

7656 --- U 100 no

4206 mcm3 R 83 no

8975 rnrS N Y 77 yes

8711 cullin-4 E U 74 yes

7769 piccolo D 69 yes

10042 MBD-R2 T 63 yes

13329 cid M 50 no

3178 rrp1 D 43 yes

17161 grapes D K M 42 no

6197 --- P S 40 yes

10689 l(2)37Cb A 40 yes

11920 --- X 38 no

4449 --- X 37 yes

2925 noisette E M 36 yes

7014 rpS5b Z 36 yes

13625 --- S 34 yes

9273 rpA2 R 32 no

12249 miranda O 31 no

10354 rat1 O 30 yes

10667 orc1 R 30 no

3736 okra D 29 yes

4785 --- X 29 yes

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level after this correction are indicated by an asterisk in Table 2. This allowed the identification of at least 81 genes with the same characteristics, i.e. either CAs only or both ACs and CAs are significantly higher than in the control after RNAi. Their relative proportions as for the functions resulted similarly to those previously described, and also in this shorter list more than 80% of identified genes has a human ortholog. Interestingly, and differently from what was found with the single comparisons described before, with this statistical method none of the identified loci show a significant increase of ACs only. Consequently, at the moment it is not possible to assess whether the increase of ACs only is a true phenotype for some silenced genes, or if it is just a byproduct of randomness.3.3. Genes causing AC-CA are randomly distributed as for ranking and map positionTo find out whether there is any correlation among the 81 genes identified in the present screening, 3 different variables were analyzed separately using statistical approaches.

The first variable is their ranking position inside the original list created by Somma et al. (2008) (reported in Table 2, Column 1). Their original work was based on the coexpression of genes having a role in mitosis, with the rationale that genes involved in the same biological process tend to be transcriptionally coexpressed. The ranking position of any given gene (Somma et al., 2008) consequently reflects its average coexpression value with respect to 6 reference genes. To get a general picture of the present data, the 1132 selected genes were arbitrarily split into 11 blocks of 103 genes (with the exception of the last block, which contains 102 genes) and the number of genes positive for ACs and/or CAs after RNAi treatment was evaluated. As shown in Figure 2, the genes seem to be almost uniformly distributed and there is no evidence of a tendency to concentrate in any portion of the coexpression list. A comparison between the distribution of the 81 loci and that of the remaining 1051 genes using the Mann–Whitney test confirmed the above intuitive conclusion (P = 0.1168). Therefore, genes causing genome instability

4866 --- Z 28 no

9677 int6 P Z 27 yes

8068 su(var)2-10 C 24 yes

8989 his3.3b C 22 yes

10754 --- M 22 yes

5370 dcp-1 U Y 20 yes

5193 TfIIB T 20 yes

18005 beag O 20 yes

1966 acf1 C 19 yes

2848 trn-SR E S 19 yes

2260 --- X 19 yes

2199 --- X 19 yes

4654 dp T Y 18 yes

12050 --- X 17 yes

14749 GLE1 A E 16 yes

3181 Ts N 16 yes

6057 SMC1 C 16 yes

6453 --- X 15 no

5454 snRNP-U1-C M S 15 no

8878 --- K 14 yes

6349 DNApola180 R 13 yes

12135 c12.1 M 13 yes

Table 3. (Continued).

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upon silencing are randomly and uniformly distributed throughout the original list.

The second variable considered is the mapping position of the identified genes inside the D. melanogaster genome, to investigate if they share their chromosomal location, creating syntenic groups (Figure 3). For this purpose the available data from FlyBase about the 102 chromosomal bands that characterize the polytene chromosomes of the fruit fly were collected. In particular, the focus was on the number of protein-coding genes, which are the target of RNAi experiments. In order to check whether the 81 significant genes tend to concentrate on particular bands, an analysis was performed on the possible presence of departures from a Poisson distribution. The variance test

did not show any significant deviation (the 1-sided p-value equals 0.4648). Similarly, the number of positive genes on any given chromosome arm was proportional to the overall number of protein-coding genes mapped on that arm (Table 4), the single gene on the fourth chromosome being excluded in computing the p-value; the chi-square test did not show any significant deviation from the expected numbers (P = 0.5487). In conclusion, the 81 genes causing genome instability upon silencing are not clustered inside the genome of D. melanogaster.3.4. Identification of gene clusters inside the list according to their cellular functionThe third variable evaluated is the cellular function of the 81 genes identified in the present screening, to check

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Figure 2. Distribution of the genes causing chromosome breakage upon RNAi. The genes analyzed in the present screening were split into 11 classes of 103 genes each (with the exception of the last class, which has 102 genes) according to their rank position. For each class, the numbers of genes positive after applying the Holm–Bonferroni correction are reported. Note that the identified genes are almost uniformly distributed as for their rank position (see text for the statistical analysis).

Figure 3. Genes required for the chromosome integrity are not clustered inside the D. melanogaster genome. Asterisks mark the approximate cytogenetic position of the 81 genes positive after applying the Holm–Bonferroni correction for multiple tests; note the almost uniform distribution of the genes along the polytene chromosomes of the fruit fly.

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whether it is somehow related to their rank position. For this aim, the function reported in FlyBase (Release FB2013_04) was used as a reference. Sixteen arbitrary functional classes were created, after setting the rule that a class must be represented by at least 3 genes/loci; in all other cases, the genes/loci were assigned to the class “other functions” (Table 2). These functional classes were then further grouped into 5 macroclasses, which can be related to more general metabolic and/or cellular mechanisms. The 5 macroclasses are: i) DNA metabolism-related functions; ii) RNA metabolism-related functions; iii) subcellular functions; iv) cell cycle progression-related functions; v) unknown functions (Figure 4). Interestingly, the Mann–Whitney test revealed a significant deviation from randomness for the distribution of the positive genes belonging to 2 of the macroclasses: the DNA metabolism-related genes (P = 0.0070) and the cell-cycle progression-related genes (P = 0.0088). This means that these genes tend to be clustered toward the higher portion of the list. Notably, data of these 2 classes of genes are significant at the 0.05 level also after the Holm–Bonferroni correction for multiple tests.3.5. Genes causing high frequencies of MCs upon silencing mainly have functions related to DNA replication and tend to have high rankingsAs described before, genes causing extensive chromosome fragmentation (MCs) upon RNAi-mediated silencing are well represented among those causing CAs and ACs – indeed, almost half of the genes (37/81, 45.7%) show this peculiar phenotype. Genes positive for the MC phenotype are reported in Table 3, which includes also those genes that are not positive after applying the Holm–Bonferroni method (in the present section, we will refer to the entire content of Table 3 as an “extended list”). Notably, 4 of the 8 genes positive after applying this statistical correction and with a stronger phenotype (i.e. with a ratio of MC/

AC of ≥5%), namely rpA-70 (whose ratio MC/AC equals 75%) CG15220 (MC/AC = 21%), mcm7 (MC/AC = 20%), and mus209 (MC/AC = 15%), are involved in the DNA replication. Additionally, these 4 genes are also the top 4 as for the strength of their MC phenotype. Looking at the extended list, 3 more loci fall inside the MC/AC ≥5% group, i.e. l(2)NC136 (MC/AC = 17%), CG7656 (MC/AC = 10%), and mcm3 (MC/AC = 8%). Of these, mcm3 (position 7 in the extended list) is involved in DNA replication as well. Taken together, these data suggest that an extreme MC/AC ratio phenotype could be typical of genes involved in DNA replication-related processes. Interestingly, among the genes inducing the appearance of MCs after silencing, the number of genes involved in the mitotic spindle assembly is also relatively high (5 loci positive with the Holm–Bonferroni method out of 8 total loci), although for them there is no particular accumulation in any part of the extended list.

To characterize further this class of genes, their rank position inside the list reported in Table 2 was considered, in order to check if their distribution is random (Figure 5). An analysis with the Mann–Whitney test revealed that these genes are not randomly distributed (P = 0.0243). These data indicate that genes causing extensive chromosome damage tend to be coexpressed with the mitotic genes originally described by Somma et al. in 2008.

4. Discussion4.1. Overview of the screeningIn the present work, we analyzed the karyotype of S2 cells treated with dsRNA against a chosen pool of genes. These genes had previously been selected based on their coexpression with other genes involved in various aspects of mitosis (Somma et al., 2008). The cited study and the data shown here demonstrate that this methodology allows the identification of genes that play a role in chromosome

Table 4. Map distribution – chromosome arms (euchromatin only). Data according to FlyBase, Release FB2010_06.

Chromosome arm

Length (kb)

Number ofgenes (all)

Protein-coding genes

Genes positive for AC-CA

X 22775 2341 2188 12

2L 22366 2899 2626 14

2R 20973 3299 2758 19

3L 23550 2960 2719 11

3R 28842 3701 3395 24

4 1339 84 84 1

Total 119,845 15,284 13,770 81

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Figure 4. Function distribution. The functions identified by the 81 genes selected with the Holm–Bonferroni method (Table 2) were grouped into 5 macroclasses. Genes/loci were split into the same 11 blocks reported in Figure 2. There is not a one-to-one relationship between genes and functions: a gene having 2–3 functions in the same macroclass was scored once, while a gene having 2–3 functions in different macroclasses was scored once for each macroclass; thus, the number of functions analyzed here (98) does not correspond to the number of genes selected with the Holm–Bonferroni method (81). CDNR: DNA metabolism-related functions (for the letter meaning, see Table 2); AST: RNA metabolism-related functions; EKOPUZ: other cellular functions; MY: cell cycle progression-related functions; X: unknown functions. Note that the CDNR and MY functions are not evenly distributed along the list of coexpression (see text for the statistical analysis).

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integrity. S2 cells show a relatively high, yet constant, level of spontaneous CAs (Table 1); this fact, and the relatively simple chromosome complement of this cell lineage, makes S2 cultured cells a suitable model for the karyotype analysis and for the identification of genes whose function is related to the maintenance of chromosome integrity. In their original analysis, Somma et al. found a lower number of genes causing chromosomal damage upon RNAi treatment. This difference may be explained by the different statistical approach we used. In their report, positive genes were identified by applying a high threshold to the total AC/CA values, to compensate for lower cell scores. Our increased number of observations, obtained by repeating the interference up to 4 times per gene, allowed us to perform a double, sequential statistical analysis, which permitted the identification of at least 81 genes positive for this phenotype.

We are aware that, apparently, there is a main limitation in this screening, due to the method used to shrink the number of examined genes. The reference list (Somma et al., 2008) relies on the average coexpression value (evaluated with Pearson’s correlation coefficient) of D. melanogaster genes with other 6 mitosis-related genes. The choice of 6 reference genes is arbitrary; any other list derived averaging 6 (or even more) different lists could have been used, and at the moment it is not known if this would have created a significantly different record of coexpressed genes. Moreover, our screening was performed for a phenotype (chromosome integrity) that was not straightly related to their gene selection method. For these reasons, it is not surprising that genes directly involved in the DNA damage recognition/repair, or in general DNA metabolism (nucleotide biosynthesis, DNA replication, chromatin/chromosome structure), are poorly represented among those identified – they are approximately 32% of the total after the Holm–Bonferroni correction. Instead, a lot of found genes either have unknown function (~21%) or are assigned to functions not apparently related to chromosome integrity (~47%). Given that segmental aneuploidy is potentially harmful for cell survival and is a recurrent step during tumorigenesis, all genes causing such a phenotype are potentially “indirect” oncogenes, since their clastogenic action is able to alter the genetic equilibrium of the cell. Consequently, the possibility to have a list of such genes is crucial for the comprehension of the neoplastic transformation and for the study of the possible role of these genes in this phenomenon. Moreover, these genes may be used for patient screening and as potential targets of antitumor therapy. In addition, the fact that 2 of the reference genes encode proteins having important roles also outside of mitosis (Cid/CENPA deposition occurs during the S phase and EB1 plays important roles during the interphase) further broadens the record of interesting genes found.

Since the coexpression-based selection worked fine for the isolation of new genes involved in mitosis (Somma et al., 2008), and since the cytogenetic methodology works fine as well for the study of genes involved in chromosome structure, merging these 2 approaches (i.e. creating coexpression lists using genes known to be related to chromosome integrity) should allow the identification of novel genes whose cellular role is still unknown. At this point, the goal is not trying to understand why a given gene causes a certain phenotype, but rather to identify how many and which genes are responsible, directly or indirectly, for this phenotype. For example, there are no clues about why 7/81 (8.6%) genes have a described function connected to mitotic spindle assembly. However, knowing that these 7 genes may also cause chromosome instability is certainly an important discovery and indicates that, potentially, other genes with the same function might induce a comparable phenotype on chromosomes; this in turn indicates that genes responsible for mitotic spindle assembly are potentially able to cause neoplastic transformation by induction of segmental aneuploidy. Similar conclusions may be drawn for the other classes of genes described.

The reason why these genes cause the CA phenotype is, in many cases, unknown. Several scenarios may be depicted to explain these data, and probably more than one should be applied to the genes here described. The easiest explanation is that these genes encode proteins that play multiple roles inside the cell, one of which is maintaining the chromosome integrity – a function not described before and that partly addresses the so-called “g-value paradox” (Hahn and Wray, 2002). This is reasonable, since available analyses of mutant flies did not necessarily cover all aspects of cell biology. Another possibility is that the reported function is simply wrong. In some cases no mutants are available for a given locus, and the gene role is inferred only on the basis of sequence similarity with other genes of known function. Finally, it may be also assumed that the gene function is not “directly” related to DNA damage, and that the chromosome breakage occurs only after one or more intermediate metabolic steps that, for some reasons, are not visible in mutants (if available). Indeed, cell cultures are not complete organisms, and RNAi is a very powerful technique: it is possible that these phenotypes are just not visible in mutant flies, either because of the presence of redundant functions, or because cells undergo cell cycle arrest that does not allow the analysis of the karyotype, or even because some genes are not active in certain tissues (the fruit fly karyotype is usually analyzed in larval neuroblasts, and S2 is an undifferentiated, embryo-derived cell lineage). In any case, the conclusion is that the list of genes that potentially may alter the chromosome structure is surely much longer

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than anticipated, and one way to compile such a list is, unexpectedly, to search for genes not related to genome integrity.4.2. Identification of gene clustersA great effort of the present screening was the search for relationships among the identified genes, especially for their relative position inside the coexpression list, for their physical position inside the genome, and for their role inside the cell. The result is that the 81 loci found are almost evenly distributed for both their relative position in the coexpression list, and for their map position inside the D. melanogaster genome. This roughly uniform scattering suggests a functional and evolutionary meaning. As described in Section 1, the loss of a genomic portion is frequently associated with the neoplastic transformation. Consequently, an even localization of the genes causing chromosomal breaks upon malfunction determines the fact that any deletion of any genomic region will impair the entire genome stability. Thus, it is possible to postulate some kind of “positional information” related to these genes that allows the cell to indirectly control the overall stability of the genome. In other words, any random genomic damage – independently of its cause – will frequently induce the associated loss of at least one gene controlling the overall genomic stability; this would further destabilize the genome, inducing cell death in most cases. Further studies are needed to verify if this almost even distribution is typical of Drosophila or is evolutionary conserved; if it is conserved, this would partly explain, among other things, why some genomic rearrangements in cancer cells are more frequent than others.

When the positive genes were grouped into functional macroclasses, other interesting results arose. At least for 2 macroclasses, the positive genes tend to be clustered toward the top of the list: those connected to DNA metabolism, and those connected to cell cycle progression. The discovery of the first class of genes was not surprising, but the second class of loci, comprising genes involved in apoptosis and mitotic spindle assembly, was unpredictable. We would expect that impairing mitotic spindle assembly would result in an activation of the spindle checkpoint able to stop the cell cycle; alternatively (if this checkpoint is the interfered function), we would expect the cell to go on dividing, but would also expect that a subsequent interphase checkpoint would induce its cycle arrest. Similar hypotheses could be made for the genes involved in apoptosis. Instead, RNAi on these genes produces a potent effect on the chromosome integrity, and the high level of CAs that we found suggests that their formation is probably irrespective of other cellular control mechanisms. These results may be explained in several ways, and we suggest here 3: i) the silencing of these genes causes an inactivation cascade targeting also other checkpoint

proteins, so the entire cellular control machinery is impaired; ii) these genes play a role in the activation of one or more still-working checkpoints that, if not activated at the proper time, are not able to function anymore; iii) the silencing of these genes destabilizes the chromosome structure immediately before the mitotic chromosome condensation, so checkpoints do not have enough time to be activated prior to the metaphase plate establishment. Further analysis of these genes is required to discriminate among these possibilities, or to postulate others.

The present study also allowed the identification of a class of genes causing extensive chromosome breakage after RNAi and recognizable by the presence of MCs, a phenotype absent in controls. Analysis of these loci revealed that many of them, and particularly those showing the stronger phenotype (as measured by the MC/AC ratio), belong to the DNA replication functional class. DNA replication, strictly speaking, is not a mitosis-related function, but probably these genes were selected because, as described before, 2 of the 6 reference genes also act during interphase. This fact allowed description of what the effect of RNAi is on this type of genes and the drawing of some conclusions. First, many genes related to DNA replication, after silencing, may show a similar phenotype. This may be easily verified by performing RNAi against genes belonging to this functional group. Second, a given gene showing, upon silencing, a high MC/AC ratio, will likely take part in DNA replication. This of course will be useful for genes either with an unrelated or without a known function, and in this case the verification should be based on the cytogenetic analysis of mutants (if available) and/or on biochemical evidences (for example, interaction with known DNA replication proteins or in vitro functional analysis). Ideally, this should provide a role to at least some genes with unknown function but similar phenotype. Third, according to the present data, these genes tend to be coexpressed with each other (accumulation in the top half of the list), so in principle the creation of appropriate coexpression lists using known genes of this functional class should give good results in the search of new DNA replication-related loci.4.3. Final remarks and future perspectivesThe aim of the present screening was to find genes causing chromosome integrity failure upon silencing. The results were obtained using, as a selection criterion, coexpression with genes not related to this phenotype. Our results indicate that, at least in some cases, these genes encode proteins with multiple, different roles. Indeed, this is a relatively new and not completely surprising finding: searching for new gene functions implies that the “previous” function should not be considered during the screening. These data in part fill the so-called “g-value paradox” (Hahn and Wray, 2002), according to which

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there are too many functions compared to the number of protein-coding genes. Our data show that the genes whose function is preventing chromosome breakage are far more numerous than expected, and potentially they might be involved in tumorigenesis through the induction of segmental aneuploidy. Since many genes are apparently unrelated with this phenotype, a screen of the entire D. melanogaster genome would be invaluable for the identification of such loci; in fact, our data strongly suggest that any selection criterion would result in an incomplete record of genes.

AcknowledgmentsThis work was achieved thanks to the generosity and kindness of Emeritus Professor M Gatti (Sapienza University of Rome), who permitted us to use his laboratory facilities and reagents to perform the experiments. We are thus deeply grateful to him and his staff. We also gratefully acknowledge Dr Alessandro Porrello (Lineberger Comprehensive Cancer Center, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA) for providing us with important comments and suggestions and for the careful review of this manuscript.

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