Response to Casellas et al

Molecular Cell

Letters Response to Casellas et al. Pedro P. Rocha,1,5 Mariann Micsinai,1,2,3,5 Yuval Kluger,4 and Jane A. Skok1,3,* 1Department

of Pathology, New York University School of Medicine, 550 First Avenue, MSB 599, New York, NY 10016, USA York University Center for Health Informatics and Bioinformatics 3NYU Cancer Institute 550 First Avenue, New York, NY 10016, USA 4Department of Pathology and Yale Cancer Center, Yale University School of Medicine, 333 Cedar Street, New Haven, CT 06520, USA 5These authors contributed equally to this work *Correspondence: [email protected] http://dx.doi.org/10.1016/j.molcel.2013.07.019 2New

Casellas and colleagues challenge the conclusions from our recent paper and claim that AID-dependent translocations occur independent of Igh proximity. In contrast to our study (Rocha et al., 2012), they focus solely on hotspots, which they claim represent the only ‘‘true’’ AID-mediated translocations (Hakim et al., 2012). However, their contention that other sites with translocation capture (TC) reads reflect AID-independent rearrangements is not supported by any genome-wide subtraction analysis examining signal enrichment in the IghI-SceI AID-sufficient versus deficient sample. Nonetheless, this is precisely the analysis they state is missing from our study, and that without this we could not distinguish between AID-dependent and AID-independent events. By implication, as per their suggestion we should find considerable overlap and comparable signal strength in the two data sets, which would account for the 90% of sites that they claim are AID-independent translocations. Furthermore, nonoverlapping regions or sites that have signal enrichment only in the AID-sufficient data set should be restricted to AIDinduced hotspots. This is not what we observed. First, one issue confounding a genomewide analysis of AID-dependent translocations is the huge variation in the number of translocations identified in the two IghI-SceI AID-deficient biological replicate data sets generated by the Nussenzweig lab: 67,156 translocations for replicate #1 and 1,247 translocations for replicate #2 (Table S7 in Klein et al., 2011). Using 100 kb windows, we found a very low correlation between these controls (r = 0.0768), indicating that any analysis of AID-dependent translocation events could vary tremendously depending on

which data set was used (Figure S1A). For our analysis we considered sites with >2 reads, and in order not to skew our results, we excluded the 350 kb region around the I-SceI site where the highest frequency of translocations occur in both AID-sufficient and deficient samples (Klein et al., 2011). Second, reads in the IghI-SceI AID-deficient replicate #1 are distributed fairly evenly across the genome with a signal enrichment that is uniformly low, in contrast to the AID-sufficient sample, which shows more localized enrichment of translocations (Figure S1A). Indeed, using the same exclusion criteria as outlined above, even in the absence of translocation hotspots we found a mean signal enrichment of 6.49 reads versus 11.97 in the AID-deficient versus sufficient sample (t test: p < 1 3 10 5). Moreover, if we normalized for the total number of reads in the two samples, these differences would be even greater, because less weight would be attached to the events in the AID-deficient sample, which contains the most reads. Third, it is not possible to perform a subtraction analysis between AID-sufficient and deficient samples because the events in these two data sets are predominantly nonoverlapping: 732 regions with signal content in the IghI-SceI AID-sufficient sample were not represented at all in the AIDdeficient sample, while 15,127 regions with signal content in the IghI-SceI AIDdeficient sample were not represented at all in the AID-sufficient sample. In Figure S1B we show a scatterplot comparing the signal in 100 kb windows in the IghI-SceI AID-sufficient versus deficient data set to demonstrate the low correlation between the two samples (r = 0.0405). In contrast, there is a high correlation between AID-sufficient replicates (r = 0.6492), demonstrating the validity of

this approach. Additional assessment using other measures of associations (ROC analysis and a Fisher exact test) could not reject the null hypothesis that the translocations in the deficient and sufficient mice are statistically independent. Taken together these data indicate that, outside of hotspots and the region around the bait, the majority of translocations in the AID-deficient data set are not represented as translocations in the AID-sufficient data set. Concerning other issues raised in the letter: It is clear that 200 kb windows centered on TSSs is the preferred way to analyze interactions with specific genes rather than nonoverlapping windows, as the former provides a unique signal for each gene examined. Nonetheless, we did not claim that this alternative method led to global differences in our results but suggested this may point to localized variation. However, we stressed the importance of different analytical methods for specific questions and showed that, compared with the fixed window size, a domainogram approach is a more suitable representation of proximity as judged by the gold standard, DNA FISH. We did not use 20 kb windows to analyze interactions across the genome, as implied by Casellas et al., because we are aware that many genes would have no HindIII sites in this size window. Indeed, our 20 kb analysis was limited to a region containing three hotspots with sufficient 4C resolution. We treated the bait chromosome separately from the rest of the genome because interactions with the cis chromosome occur at higher frequency than interactions in trans and must be analyzed separately, as recognized by pioneers of the 4C technique (van de Werken et al.,

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Molecular Cell

Letters 2012). In their letter Casellas et al. contend that we used an arbitrary cutoff of 60 Mb from Igh for our analysis of the bait chromosome. This was not the case. We simply demonstrated that within ‘‘a linear distance of 60 Mb, the strength of interaction decreases with increasing linear separation,’’ and this matches the decline in translocation frequency. Thus, 4C-seq and TC-seq follow the same rules, and the bait chromosome should be examined separately from the rest of the genome (Rocha et al., 2012) rather than combining the data for analysis (Hakim et al., 2012). In sum, using distinct complementary approaches, 4C-seq/TC-seq, and DNA interphase/metaphase FISH analyses, we conclude that close proximity to Igh is a contributing factor to AID-mediated translocations. However, we very clearly stated in our manuscript that ‘‘our data do not imply that every gene associated with Igh will be hit by AID as we know that there are many gene intrinsic factors that influence AID targeting.’’ Furthermore, we do not undermine the contribution of break frequency in determining translocation outcome and say that it is not surprising that Hakim et al. do not find a high degree of correlation between Igh proximity and translocation frequency

in the hotspot subset, because the frequency of double-strand breaks will always be the rate-limiting factor. Nonetheless, it is worth noting that out of the 234 AID-mediated translocation hotspots associated with the IghI-SceI site identified by Klein et al., 95 (40%) were detected in cis on chromosome 12, while only 26 (11%) were identified on chromosome 15. In contrast, for AID-mediated translocations associated with the MycI-SceI site on chromosome 15, the number of translocations on chromosome 12 dropped to 10%, while translocations on chromosome 15 increased to 33% (Table S4 in Klein et al., 2011) (Figure S1C). Since the location of AID-generated breaks is not dependent on the location of the I-SceI site, these changes can only be explained by differences in the frequency of interactions in cis versus trans (Lieberman-Aiden et al., 2009; Lin et al., 2012), which give rise to a higher number of translocation hotspots in whichever chromosome contains the I-SceI site. According to Hakim et al., AID-mediated translocation hotspots occur independent of the proximity between partner loci, thus the position of the I-Sce-1 site should not interfere with their genomic distribution. We show here that this is not the case. Indeed, the analysis in Figure S1C provides clear support

278 Molecular Cell 51, August 8, 2013 ª2013 Elsevier Inc.

for the notion that proximity is a key determinant of AID-dependent translocation hotspots. SUPPLEMENTAL INFORMATION Supplemental Information includes one figure and can be found with this article online at http://dx. doi.org/10.1016/j.molcel.2013.07.019. REFERENCES Hakim, O., Resch, W., Yamane, A., Klein, I., KiefferKwon, K.R., Jankovic, M., Oliveira, T., Bothmer, A., Voss, T.C., Ansarah-Sobrinho, C., et al. (2012). Nature 484, 69–74. Klein, I.A., Resch, W., Jankovic, M., Oliveira, T., Yamane, A., Nakahashi, H., Di Virgilio, M., Bothmer, A., Nussenzweig, A., Robbiani, D.F., et al. (2011). Cell 147, 95–106. Lieberman-Aiden, E., van Berkum, N.L., Williams, L., Imakaev, M., Ragoczy, T., Telling, A., Amit, I., Lajoie, B.R., Sabo, P.J., Dorschner, M.O., et al. (2009). Science 326, 289–293. Lin, Y.C., Benner, C., Mansson, R., Heinz, S., Miyazaki, K., Miyazaki, M., Chandra, V., Bossen, C., Glass, C.K., and Murre, C. (2012). Nat. Immunol. 13, 1196–1204. Rocha, P.P., Micsinai, M., Kim, J.R., Hewitt, S.L., Souza, P.P., Trimarchi, T., Strino, F., Parisi, F., Kluger, Y., and Skok, J.A. (2012). Mol. Cell 47, 873–885. van de Werken, H.J., Landan, G., Holwerda, S.J., Hoichman, M., Klous, P., Chachik, R., Splinter, E., Valdes-Quezada, C., Oz, Y., Bouwman, B.A., et al. (2012). Nat. Methods 9, 969–972.