Expression of Concern: Natural copy number variation of tandemly repeated regulatory SNORD RNAs leads to individual phenotypic differences in mice

  1. Maryam Keshavarz
  2. Yoland Savriama
  3. Peter Refki
  4. R. Guy Reeves
  5. Diethard Tautz

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Abstract

Genic copy number differences can have phenotypic consequences, but so far this has not been studied in detail in natural populations. Here, we analysed the natural variation of two families of tandemly repeated regulatory small nucleolar RNAs (SNORD115 and SNORD116) in the house mouse ( Mus musculus ). They are encoded within the Prader‐Willi Syndrome gene region, known to be involved in behavioural, metabolic, and osteogenic functions in mammals. We determined that the copy numbers of these SNORD RNAs show substantial natural variation, both in wild‐derived mice as well as in an inbred mouse strain (C57BL/6J). We show that copy number differences are subject to change across generations, making them highly variable and resulting in individual differences. In transcriptome data from brain samples, we found SNORD copy‐number correlated regulation of possible target genes, including Htr2c , a predicted target gene of SNORD115, as well as Ankrd11 , a predicted target gene of SNORD116. Ankrd11 is a chromatin regulator, which has previously been implicated in regulating the development of the skull. Based on morphometric shape analysis of the skulls of individual mice of the inbred strain, we show that shape measures correlate with SNORD116 copy numbers in the respective individuals. Our results suggest that the variable dosage of regulatory RNAs can lead to phenotypic variation between individuals that would typically have been ascribed to environmentally induced variation, while it is actually encoded in individual differences of copy numbers of regulatory molecules.

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  1. Version published to 10.1111/mec.16076
  2. Review Commons

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    Reviewer #1 (Evidence, reproducibility and clarity (Required)):

    **Summary:**

    The work reports finding a molecular genetic basis for individual differences in behavior in different strains of outbred mice, even including individual behavioral differences between mice of the same inbred genetically isogenic strain. The authors were able to measure copy numbers for the tandemly repeated intronic snoRNA clusters SNORD115 and SNORD116 and found correlation with measures of anxiety in open-field test and elevated plus maze. Expression data for previously proposed targets of these snoRNAs are also provided.

    We note that this description represents only part of the achievements in our paper. The key of our paper is that we have not only used "different strains of outbred mice", but in addition one very different species of mouse (Apodemus) and a Guinea pig species. We believe that the test in very different species with very different genetic backgrounds is the crucial proof for the specificity of the effect.

    **Major comments:**

    1.The techniques to measure copy numbers are challenging and the authors' conclusion that ddPCR was their method of choice is convincing. They were able to obtain limited optical mapping (Bionano zephyr) data, only for SNORD116 and only in mouse, but these data are useful to corroborate those obtained with ddPCR.

    2.Figure 3 reports single copy numbers for individuals that are presumably heterozygous. Do we have to assume that the numbers reported represent the larger alleles since the ddPCR method does not allow to distinguish two different size alleles, as was shown for optical mapping?

    The numbers are derived from the whole genome DNA, i.e. represent the cumulative copy number of both alleles. We have updated the text to make this clear.

    3.The analyses reported do not take into account the specific parental origin of the alleles used in the regression analyses. Since PWSCR-specific SNORDs are only expressed from the paternal chromosomes, this generates some uncertainty about the whole dataset.

    We had explained why it is not possible to distinguish the two alleles with the current technology. Hence, it is evidently also not possible to determine which allele comes from the paternal side. In fact, given that we showed that copy numbers can change every generation, even the knowledge of which chromosome is the paternal one would not predict its copy number. Accordingly, it lies in the nature of the whole phenomenon that this uncertainty is given. It is therefore just the more surprising that we still can observe correlations that are much stronger than has been shown for natural alleles of any genetic locus implicated in behavioral traits so far.

    4.Lines 353-365: The ankrd11 exon-specific RNAseq data are confusing and too preliminary. More work needs to be done to resolve the splice variants in this region and their relationship to SNORD116 copy numbers. Alternatively lines 356-361 should be deleted.

    We have included the data to show that the mechanism must be different from the one that is seen for Htr2c. This difference is clearly documented and we should therefore like to retain it. What is missing is to show the actual mechanism by which SNORD116 causes the alternative splicing. This will require more biochemical approaches that go beyond the current study.

    5.In all tested rodents, higher SNORD copy number was correlated with higher relative anxiety score. In the human samples, however, higher anxiety scores were associated with lower copy numbers. These apparently contradictory results are not mentioned in the abstract, and are not satisfactory explained in the text.

    We have decided to leave the human data out from the current manuscript. First, the behavioral tests for the rodents are indeed not directly comparable with the questionnaire scores in humans. Second, in human genetics one usually asks the results to be confirmed in an independent study. hence, we plan to extend the human data, but to present them eventually in a follow-up paper.

    6.Extension to other species would be desirable but was limited by availability of genomic data: Results are presented for wood mouse only for SNORD115 and for the guinea pig for SNORD116.

    Given that we show a strong correlation between SNORD115 and SNORD116 copy numbers for those species where the information is available for both loci, we do not think that this is a major limitation of our study.

    **Minor comments:**

    1.The authors present skull shape data related to SNORD116 copy numbers, but fail to consider how these data are relevant to the craniofacial abnormalities reported in an ankrd11 mutation. Barbaric et al (2008) reported a dominant ENU- induced mutation caused shortened snouts, wider skull, deformed nasal bones, reduced BMD, reduced osteoblast activity and reduced leptin levels. This phenotype was traced to a heterozygous missense mutation (conserved glutamate to lysine change) in an HDAC binding site. They postulated that the mutation fails to recruit HDACs to a transcription complex and to inhibit hormone-receptor activated gene transcription. What is the postulated link between this mechanism and the here reported skull shape data correlated with SNORD copy number variation?

    The described missense mutation is located in the differentially spliced exon, i.e. a direct functional link is given. We have added this information to the text and compared the direct phenotypic effects from their study and our study.

    2.The observed co-variation of copy numbers between the two SNORD clusters could indicate a duplication involving the entire region, This could be tested by determining the dosage of IPW, UBE3a and Snrpn genes.

    While this is a theoretical possibility, it was not described in the literature before. Also, in our systematic survey of copy number variation in mouse populations (Pezer et al. 2015) we did not find a deviation of these genes from expected diploid copy number in any of the populations analysed.

    3.Line 129 "the RNA coding region" and Line 148 "snoRNA coding parts" (and elsewhere) does seems correct, as by definition, this is non-coding RNA. The region they are referring to could be called the "processed C/D box snoRNA". The mechanism that generates these C/D box snoRNAs is well established: the "genes" are located in introns of host genes - and after transcription - the spliced out introns are exonucleolytically trimmed to the functional sizes. Both SNORD115 and 116 clusters are within a large transcript that originates from the SNRPN promoter of the paternal allele.

    We adjusted the wording to make clear that we refer to the mature RNAs.

    4.Figure 2 does not show data on skull shape as claimed in the legend.

    We apologize - this was a carry-over from an older version of this figure. The skull shape analysis had been moved to a new figure in the current version of the manuscript.

    5.S1 Figure: Snprn should be Snrpn

    Thank you for spotting the error - we have corrected this

    Reviewer #1 (Significance (Required)):

    This provocative work proposes the regulation of behavioral variance by dosage changes of a regulatory RNA. The dosage changes are apparently caused by dynamic and frequent alteration in copy number. This is a novel concept and worthy of publicizing. Extensive data documentation is provided for others to analyze and possibly replicate. The data potentially throw light on the function of the tandemly repeated imprinted snoRNA clusters in the PWS critical region.

    Novel aspects of this work include the discovery of copy number variation of these snoRNAs; and validation of a target of SNORD116: Ankrd11 is one of many potential targets of SNORD116 that was previously computationally predicted, this paper reports experimental evidence for this interaction.

    The work would be of interest to researchers in behavioral evolution, non-coding RNA function, epigenetics and overall genome evolution.

    Define your field of expertise with a few keyword: Molecular genetic disorders, Prader-Willi syndrome, mouse models

    Reviewer #2 (Evidence, reproducibility and clarity (Required)):

    **Summary**

    Maryam Keshavarz et al. aimed at seeking the molecular basis underlying individual behavioral variance within populations. Focusing on the Prader-Willi Syndrome (PWS) gene complex, which has been well recognized being associated with neurodevelopmental disorders, anxiety and metabolic issues, the authors found that the levels of PWS region's small nucleolar RNAs SNORD 115/116 of individual animals correlated with these individuals' behavioral test scores. The variations in transcript processing of some anxiety-associated target genes also revealed correlation with SNORD 115/116 copy numbers. Authors implicated that the copy numbers of SNORD 115/116 within PWS plausibly influenced behavioral variances among individuals.

    • Authors first validated that the droplet digital PCR (ddPCR) was suitable for quantifying variations in copy numbers of genomic clusters. Their ddPCR data showed confident correspondence with reads calculation of whole-genome-seq dataset. Also, ddPCR showed good replicability and congruent tissue-to-tissue similarity.

    • Authors found the ddPCR-measured SNORD copy numbers from several mice populations showed significant regression with SNORD RNA levels, respectively. Also, the anxiety profiling using Open Field Test and Elevated Plus Maze test indicated a significant regression between SNORD copy numbers and anxiety profiling scores, namely individual mouse with higher copy numbers received higher relative anxiety scores. Some other representative genes outside PWS, such as Sfi1 and Cwc22, failed to show such copy number-anxiety score regression.

    • Authors applied RNA-seq of individual mice with different SNORD 115/116 copy numbers and analyzed potential target gene regions. They found the level of alternative splice-resulted exon Vb of gene Htr2c, a serotonin receptor, was positively correlated with SNORD 115 copy number. Also, an alternative splicing product of gene Ankrd11, a chromatin receptor regulating GABA receptor, was found to positively correlated with SNORD 116 copy number. Positive correlation to SNORD copy numbers also occurred to some Htr2c and Ankrd downstream genes.

    • Authors used a landmark-based analysis to score mice craniofacial features and found the scores were in relationship with SNORD 116 copy numbers.

    • Authors also found significant regression between SNORD copy numbers and behavioral evaluations in other rodents. In humans, the Tridimensional Personality Questionnaire (TPQ) based evaluation also showed correlation with SNORD 115 and 116 copy numbers.

    **Major comments**

    The study mainly revealed important correlations between copy numbers of 2 small nucleolar RNAs and cognitive behavioral variance of different individual animal. Although very useful and important findings, the study did not provide any evidence about the causality between SNORD 115/116 and the observed behavioral phenotypes. For instance,

    • #1: the behavioral observations (i.e. anxiety) may not be merely regulated by the PWS gene complex.

    It is already well understood that the respective behavioral observations have a polygenic basis. But our data show that the SNORD copy numbers act as major modulators of the behavior.

    • #2: the paper did not show if manipulations on mouse SNORD 115/116 could affect target genes as well as the consequential behavioral phenotypes.

    A direct interaction between SNORD115 and its target gene HTr2c has previously been shown in cell culture experiments. Further, we show that the commonly used inbred mouse strain C57Bl6 carries already different copy number alleles that would not be different from artificial manipulation of the copy number. There is a long tradition in mouse genetics to accept also spontaneous alleles as genetic proof, not only the alleles that were created by artificial intervention.

    Further, as also pointed out in response to reviewer 1, in the absence of the possibility to do a direct genetic manipulation in a given genetic background, we use the comparative analysis between different genetic backgrounds to prove causality.

    Reviewer #2 (Significance (Required)):

    Authors provided a potential molecular basis regulating the PWS region, which is a genomic imprinted gene complex and related to many neurodevelopmental diseases in mammals, including humans.

    Considerably cost-saving than whole-genome deep-seq, the application of droplet digital PCR on copy number (esp. in stretching regions) measurement can overcome some technical difficulties, for example, qPCR has limit in resolution when differentiating subtle variance in copy numbers; the Nanopore seq and current mapping algorithm show difficulties when placing the internal repeats also.

    Authors proposed SNORD copy number as a potential explanation to the individual-to-individual variance within the same species or even the same population.

  3. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #2

    Evidence, reproducibility and clarity

    Summary

    Maryam Keshavarz et al. aimed at seeking the molecular basis underlying individual behavioral variance within populations. Focusing on the Prader-Willi Syndrome (PWS) gene complex, which has been well recognized being associated with neurodevelopmental disorders, anxiety and metabolic issues, the authors found that the levels of PWS region's small nucleolar RNAs SNORD 115/116 of individual animals correlated with these individuals' behavioral test scores. The variations in transcript processing of some anxiety-associated target genes also revealed correlation with SNORD 115/116 copy numbers. Authors implicated that the copy numbers of SNORD 115/116 within PWS plausibly influenced behavioral variances among individuals.

    • Authors first validated that the droplet digital PCR (ddPCR) was suitable for quantifying variations in copy numbers of genomic clusters. Their ddPCR data showed confident correspondence with reads calculation of whole-genome-seq dataset. Also, ddPCR showed good replicability and congruent tissue-to-tissue similarity.

    • Authors found the ddPCR-measured SNORD copy numbers from several mice populations showed significant regression with SNORD RNA levels, respectively. Also, the anxiety profiling using Open Field Test and Elevated Plus Maze test indicated a significant regression between SNORD copy numbers and anxiety profiling scores, namely individual mouse with higher copy numbers received higher relative anxiety scores. Some other representative genes outside PWS, such as Sfi1 and Cwc22, failed to show such copy number-anxiety score regression.

    • Authors applied RNA-seq of individual mice with different SNORD 115/116 copy numbers and analyzed potential target gene regions. They found the level of alternative splice-resulted exon Vb of gene Htr2c, a serotonin receptor, was positively correlated with SNORD 115 copy number. Also, an alternative splicing product of gene Ankrd11, a chromatin receptor regulating GABA receptor, was found to positively correlated with SNORD 116 copy number. Positive correlation to SNORD copy numbers also occurred to some Htr2c and Ankrd downstream genes.

    • Authors used a landmark-based analysis to score mice craniofacial features and found the scores were in relationship with SNORD 116 copy numbers.

    • Authors also found significant regression between SNORD copy numbers and behavioral evaluations in other rodents. In humans, the Tridimensional Personality Questionnaire (TPQ) based evaluation also showed correlation with SNORD 115 and 116 copy numbers.

    Major comments

    The study mainly revealed important correlations between copy numbers of 2 small nucleolar RNAs and cognitive behavioral variance of different individual animal. Although very useful and important findings, the study did not provide any evidence about the causality between SNORD 115/116 and the observed behavioral phenotypes. For instance,

    • #1: the behavioral observations (i.e. anxiety) may not be merely regulated by the PWS gene complex.

    • #2: the paper did not show if manipulations on mouse SNORD 115/116 could affect target genes as well as the consequential behavioral phenotypes.

    Significance

    Authors provided a potential molecular basis regulating the PWS region, which is a genomic imprinted gene complex and related to many neurodevelopmental diseases in mammals, including humans.

    Considerably cost-saving than whole-genome deep-seq, the application of droplet digital PCR on copy number (esp. in stretching regions) measurement can overcome some technical difficulties, for example, qPCR has limit in resolution when differentiating subtle variance in copy numbers; the Nanopore seq and current mapping algorithm show difficulties when placing the internal repeats also.

    Authors proposed SNORD copy number as a potential explanation to the individual-to-individual variance within the same species or even the same population.

  4. Review Commons

    Note: This preprint has been reviewed by subject experts for Review Commons. Content has not been altered except for formatting.

    Learn more at Review Commons

    Referee #1

    Evidence, reproducibility and clarity

    Summary:

    The work reports finding a molecular genetic basis for individual differences in behavior in different strains of outbred mice, even including individual behavioral differences between mice of the same inbred genetically isogenic strain. The authors were able to measure copy numbers for the tandemly repeated intronic snoRNA clusters SNORD115 and SNORD116 and found correlation with measures of anxiety in open-field test and elevated plus maze. Expression data for previously proposed targets of these snoRNAs are also provided.

    Major comments:

    1.The techniques to measure copy numbers are challenging and the authors' conclusion that ddPCR was their method of choice is convincing. They were able to obtain limited optical mapping (Bionano zephyr) data, only for SNORD116 and only in mouse, but these data are useful to corroborate those obtained with ddPCR.

    2.Figure 3 reports single copy numbers for individuals that are presumably heterozygous. Do we have to assume that the numbers reported represent the larger alleles since the ddPCR method does not allow to distinguish two different size alleles, as was shown for optical mapping?

    3.The analyses reported do not take into account the specific parental origin of the alleles used in the regression analyses. Since PWSCR-specific SNORDs are only expressed from the paternal chromosomes, this generates some uncertainty about the whole dataset.

    4.Lines 353-365: The ankrd11 exon-specific RNAseq data are confusing and too preliminary. More work needs to be done to resolve the splice variants in this region and their relationship to SNORD116 copy numbers. Alternatively lines 356-361 should be deleted.

    5.In all tested rodents, higher SNORD copy number was correlated with higher relative anxiety score. In the human samples, however, higher anxiety scores were associated with lower copy numbers. These apparently contradictory results are not mentioned in the abstract, and are not satisfactory explained in the text.

    6.Extension to other species would be desirable but was limited by availability of genomic data: Results are presented for wood mouse only for SNORD115 and for the guinea pig for SNORD116.

    Minor comments:

    1.The authors present skull shape data related to SNORD116 copy numbers, but fail to consider how these data are relevant to the craniofacial abnormalities reported in an ankrd11 mutation. Barbaric et al (2008) reported a dominant ENU- induced mutation caused shortened snouts, wider skull, deformed nasal bones, reduced BMD, reduced osteoblast activity and reduced leptin levels. This phenotype was traced to a heterozygous missense mutation (conserved glutamate to lysine change) in an HDAC binding site. They postulated that the mutation fails to recruit HDACs to a transcription complex and to inhibit hormone-receptor activated gene transcription. What is the postulated link between this mechanism and the here reported skull shape data correlated with SNORD copy number variation?

    2.The observed co-variation of copy numbers between the two SNORD clusters could indicate a duplication involving the entire region, This could be tested by determining the dosage of IPW, UBE3a and Snrpn genes.

    3.Line 129 "the RNA coding region" and Line 148 "snoRNA coding parts" (and elsewhere) does seems correct, as by definition, this is non-coding RNA. The region they are referring to could be called the "processed C/D box snoRNA". The mechanism that generates these C/D box snoRNAs is well established: the "genes" are located in introns of host genes - and after transcription - the spliced out introns are exonucleolytically trimmed to the functional sizes. Both SNORD115 and 116 clusters are within a large transcript that originates from the SNRPN promoter of the paternal allele.

    4.Figure 2 does not show data on skull shape as claimed in the legend.

    5.S1 Figure: Snprn should be Snrpn

    Significance

    This provocative work proposes the regulation of behavioral variance by dosage changes of a regulatory RNA. The dosage changes are apparently caused by dynamic and frequent alteration in copy number. This is a novel concept and worthy of publicizing. Extensive data documentation is provided for others to analyze and possibly replicate. The data potentially throw light on the function of the tandemly repeated imprinted snoRNA clusters in the PWS critical region.

    Novel aspects of this work include the discovery of copy number variation of these snoRNAs; and validation of a target of SNORD116: Ankrd11 is one of many potential targets of SNORD116 that was previously computationally predicted, this paper reports experimental evidence for this interaction.

    The work would be of interest to researchers in behavioral evolution, non-coding RNA function, epigenetics and overall genome evolution.

    Define your field of expertise with a few keyword: Molecular genetic disorders, Prader-Willi syndrome, mouse models

  5. Version published to 10.1101/476010v2 on bioRxiv
  6. Version published to 10.1101/476010v1 on bioRxiv