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  1. Author Response:

    Reviewer #1 (Public Review):

    This article by H. Izgi et al. describes interesting work measuring transcriptional changes through development and later aging. The authors broadly conclude that these tissue transcriptomes diverge during development, but re-converge during aging. They name this expression pattern divergence convergence, or DiCo.

    After drawing this conclusion from tissue samples drawn from 16 mice of their own, they look at published mouse and human transcriptomic data and observe similar patterns of change.

    Overall the authors emphasize that both highly mitotic and less mitotic tissues show examples of the DiCo transcriptional pattern, supporting the possibility that this may be a general phenomenon.

    In addition, the authors ask whether the tissue-specific changes they observe might depend on changes in cell composition with tissues, or cell autonomous transcriptional changes within cells, using published single-cell data. They conclude here that both play a role.

    Some of the more specific findings are not surprising and in this support the soundness of parts of the methodology, e.g. that shared developmentally down-regulated genes were enriched in functions such as cell cycle and cell division.

    My largest suggestion centers around an alternative hypothesis that may occur to readers; namely that the convergence or Co part of DiCo could be just regression to a mean due to heteroscedasticity with respect to time (age) caused by increased noise in expression. As the divergence could be imagined to be largely due to tissue differentiation during development, which has been studied extensively previously, the overall novelty of these findings relies much more on the later convergence that the authors have observed. The authors note: "Interestingly, we found no overlap between gene sets with the reversal pattern (up-down or down-up genes) across tissues, relative to random expectation". They also note "Intriguingly, we found that similar cell types (i.e. those with the highest correlations) among tissues become less similar with age (36/54 [67%] of pairwise comparisons, Figure 5-source data 1). On the contrary, the most distinct cell types (i.e. those with the lowest correlations) among tissues become more similar with age (45/54 [83%], Figure 5-source data 1).", which is at first glance consistent with this alternative hypothesis. The authors do directly address previous observations of increased noise with age in their Discussion (Bahar et al. 2006; Martinez-Jimenez et al. 2017; Angelidis et al. 2019; Somel et al. 2006), although I might also suggest perhaps PMID: 20832724 PMID: 8604994, and PMID: 28965763. Their acknowledgment refers to the disagreement of their own findings of inter-tissue correlation distributions being modest and comparable between aging and development in Figure 1c. Their CoV trajectory data in Figure 2, perhaps most relevant here in Figure 2c, may also speak to this issue. Nevertheless, in my opinion it would strengthen the manuscript greatly for many readers if this alternative hypothesis were more explicitly and clearly spelled out, and then perhaps more explicitly ruled out, in the manuscript.

    We thank the reviewer for pointing out this interesting possibility, i.e. that increased expression heterogeneity during ageing (heteroscedasticity) may cause the observed DiCo pattern. Heteroscedasticity can occur at two different levels: inter-individual (Somel et al., 2006) or inter-cellular (Bahar et al., 2006). Here we only have enough power to test whether inter-individual heterogeneity may contribute to DiCo. We used two heteroscedasticity tests. In both cases we compared i) genes with DiCo pattern and ii) genes with DiDi pattern (divergent throughout the lifetime). The hypothesis was that if heteroscedasticity has a role in DiCo, DiCo genes should show stronger heteroscedasticity than DiDi genes.

    In the first approach, we followed the method we used to measure heteroscedasticity in (Işıldak et al., 2020) and (Kedlian et al., 2019). We first fit a linear model between age (log2 scale) and the expression level of each gene. Then, we calculated Spearman’s correlation between the absolute residuals from this model and age. We found that DiCo and DiDi genes are not significantly different in terms of their effect sizes in heteroscedasticity in any of the tissues (two-sided KS test, p>0.05 in all tissues, Figure 2-figure supplement 15a).

    In the second approach, we used the ‘ncvTest’ function from the ‘car’ package which performs Breusch-Pagan test for heteroscedasticity in a linear model. We compared the test statistics of Breusch-Pagan test, i.e. measure of heteroscedasticity of each gene in each tissue, between DiCo and DiDi genes. We found that the two gene sets do not significantly differ in heteroscedasticity in the three tissues. The only exception was muscle; here, contrary to expectation under the alternate hypothesis, DiDi genes showed slightly higher heteroscedasticity (two-sided KS test, p=0.042, Figure 2-figure supplement 15b).

    We believe that the new results strengthen our results and suggest the observed DiCo pattern is not an artefact of inter-individual heteroscedasticity. We have now updated the text to include these new analysis results and figures (Figure 2-figure supplement 15).

    Meanwhile, the above analysis does not test the possible relationship between heteroskedasticity and DiCo at the cellular level. Inter-cellular expression noise, when coupled with constraints on minimum and maximum expression levels, can theoretically lead to gene expression becoming more similar to the mean levels. In other words, the most cell-type-specific genes with the highest and lowest expression may attain lower or higher expression levels during ageing, simply due to increased expression noise during ageing. Such an effect could theoretically increase correlation among cell types. This model is in essence an alternative description of our “loss of cellular identity” model and elegantly links together two observations, inter-cellular heteroskedasticity and convergence.

    We thank the reviewer also for suggesting new references for increased noise with age. We have now updated the text to add those references.

    Reviewer #2 (Public Review):

    In this manuscript, Izgi et al investigated age-dependent gene expression pattern changes in male mice by analyzing a new bulk RNA-seq data from four different tissues collected at different ages covering post-natal development and aging. Gene expression patterns observed before and after sexual maturation seem to suggest inter-tissue divergence and convergence of gene expression profiles, respectively. The authors name that phenomenon Divergence-Convergence or "DiCo". Analysis of publicly available single cell RNA-seq [scRNAseq] datasets (from the Tabula Muris Senis consortium) suggests that such gene expression pattern changes may be explained by both alterations in tissue cell type composition, as well as by cell-autonomous expression changes. These observations may suggest that aging results in at least a partial loss of tissue identity acquired developmentally.

    Although the authors report an intriguing finding, there are major issues in the manuscript as it stands, notably concerning the clarity and rigor of the data analysis and manuscript. Notably, the authors compare expression levels across samples using the FPKM normalization method, which has been shown to be a problematic metric. There are also inconsistencies in statistical and methodological choices for which there is not a clear rationale explained in the manuscript. Finally, the authors use only male animals, which may not reflect age-related trajectories in female animals, but draw broad cross-species conclusions without raising sex as a caveat to the generalization of the conclusions.

    We thank the reviewer for their careful reading and we are happy to hear that they found the results intriguing. Following the reviewer’s criticism, we carefully re-wrote the Methods section. We hope that the reviewer will now agree that the problem in our first submission was with lack of textual clarity instead of methodological. With regard to the comment about normalisation; we do not only use FPKM in our analyses (which, as the reviewer suggests, is an intra-sample normalisation), but we also apply quantile normalisation, which is an intersample normalisation method. We now clarified this aspect in the text. In addition, we repeated the main analyses using VST, which is another inter-sample normalisation that is implemented in the widely used DESeq2 pipeline. This confirmed our main conclusions. In the main text we retained the results based on quantile normalisation, but also report the VST-based analyses for confirmation.

    We also thank the reviewer for their comment on the potential sex-dimorphism of the observed phenomenon, which we had not considered before. Our samples are indeed all-male, whereas the additional dataset from Jonker et al. is composed of only female individuals, and notably, inter-tissue convergence during ageing was also observed in this dataset. Additionally, the GTEx data covers both female and male samples in humans and also suggests a trend towards inter-tissue convergence during ageing. While we observe DiCo in both sexes, it is still possible that the genes and functional pathways that show this pattern might be sexspecific and do not overlap between sexes. A comparison of male and female-specific convergent genes in mice (i.e. those identified in our data and that of Jonker et al.) is not possible at this point, as sex effects would be confounded with laboratory and platform effects.

    Although the human GTEx data contains both males and females, the age distribution of female (n=11) and male (n=36) samples are quite different (and we also lack male individuals at 20-29 and 70-79 age groups, limiting our data only to 30-69 for males). Consequently, we could only test inter-tissue convergence in each sex but could not compare those gene sets. Based on the analysis in the GTEx data, we observed that convergence during ageing was marginally significant in the female sample (⍴_female= -0.58, p_female=0.059) but not in the male sample (⍴_male= -0.052, p_male=0.77) (Figure2-figure supplement 16). The difference might be driven by missing individuals for the youngest and oldest age groups.

    We now included these results in the main text, and discuss the importance of addressing sex-specific effects in the future.

    Reviewer #3 (Public Review):

    In this manuscript Izgi et al. analyzed gene expression time-course data in four tissues during postnatal development and ageing in mice. Authors show that the expression levels of genes often reverse with ageing compared to development. Authors further show that the expression pattern diverge among the tissues during postnatal development and converge among tissues with ageing. This divergence and convergence pattern (called DiCo) is analyzes at both individual gene and genome-wide levels using multiple statistical approaches. Both cellular composition changes and cell autonomous expression changes contribute to the reversal of gene expression pattern during ageing. This study connects expression pattern during postnatal development with ageing, extending previous work on a single tissue.

    Strengths:

    -The expression convergence with age is consistently seen across multiple datasets and species indicating it can be widespread.

    -The datasets generated are unique and would be useful resource for ageing genomic community.

    -Authors go beyond bulk RNA-seq and also analyze available single cell RNA-seq datasets in mice to asses the contribution of cell composition changes and cell intrinsic expression changes to DiCo.

    Weaknesses:

    -Many aspects of expression convergence and DiCo pattern have low effect size and some are not significant. It also appears that this pattern is best seen at the genome-wide level.

    -Although there is statistical support for DiCo, there are no consistent functional associations discovered in Gene Ontology enrichment.

    -The mechanism for DiCo and the extent to which the same genes or pathways underlie this across species is unclear.

    We thank the reviewer for their careful reading of our manuscript and for pointing out the strengths and weaknesses in a clear manner. We hope that both the dataset and the insight we gained from this study will be useful for the community and open new directions of research in the future.

    We agree with the reviewer that although we study the convergence of expression at different levels, it is the most prominent at the genome-wide level and the effect size is small. We now included a discussion on this aspect in our limitations paragraph. As the reviewer points out, our analysis was focused on identifying genome-wide patterns and not on particular genes and/or specific functional processes. Still, we do find certain associations between DiCo genes and GO categories related to tissue development and differentiation. In this version, we provide a more in-depth analysis of these categories, together with their profiles of gene expression during development and ageing. Unfortunately, confirmation of the functional consequences through experimental studies is outside the scope of this paper. Thus, the results should be seen as potential links that require further experimental support. We also mention this in our limitations paragraph. Lastly, to address the reviewer’s comment on the mechanisms, we tested whether the DiCo pattern is associated with certain transcription regulators, miRNAs and TFs; however, we did not find any specific regulator. If DiCo is indeed a transcriptome-wide phenomenon caused by loss of expression regulation and cellular identity during ageing, rather than the result of a controlled program, lack of significant association with specific transcriptional regulators may be expected. This new result and its discussion are also included in the new version.

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  2. Evaluation Summary:

    This study describes work measuring transcriptional changes through development and later aging. The authors broadly conclude that the transcriptomes of several tissues diverge during development, but re-converge during aging, a pattern that they term "divergence convergence", or DiCo. The trajectories the authors have identified could provide a powerful lens through which to improve our understanding of the basic biology of aging. This paper will be of interest to the aging community, especially to researchers interested in age-dependent gene expression changes and their consequences.

    (This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. The reviewers remained anonymous to the authors.)

    Was this evaluation helpful?
  3. Reviewer #1 (Public Review):

    This article by H. Izgi et al. describes interesting work measuring transcriptional changes through development and later aging. The authors broadly conclude that these tissue transcriptomes diverge during development, but re-converge during aging. They name this expression pattern divergence convergence, or DiCo.

    After drawing this conclusion from tissue samples drawn from 16 mice of their own, they look at published mouse and human transcriptomic data and observe similar patterns of change.

    Overall the authors emphasize that both highly mitotic and less mitotic tissues show examples of the DiCo transcriptional pattern, supporting the possibility that this may be a general phenomenon.

    In addition, the authors ask whether the tissue-specific changes they observe might depend on changes in cell composition with tissues, or cell autonomous transcriptional changes within cells, using published single-cell data. They conclude here that both play a role.

    Some of the more specific findings are not surprising and in this support the soundness of parts of the methodology, e.g. that shared developmentally down-regulated genes were enriched in functions such as cell cycle and cell division.

    My largest suggestion centers around an alternative hypothesis that may occur to readers; namely that the convergence or Co part of DiCo could be just regression to a mean due to heteroscedasticity with respect to time (age) caused by increased noise in expression. As the divergence could be imagined to be largely due to tissue differentiation during development, which has been studied extensively previously, the overall novelty of these findings relies much more on the later convergence that the authors have observed. The authors note: "Interestingly, we found no overlap between gene sets with the reversal pattern (up-down or down-up genes) across tissues, relative to random expectation". They also note "Intriguingly, we found that similar cell types (i.e. those with the highest correlations) among tissues become less similar with age (36/54 [67%] of pairwise comparisons, Figure 5-source data 1). On the contrary, the most distinct cell types (i.e. those with the lowest correlations) among tissues become more similar with age (45/54 [83%], Figure 5-source data 1).", which is at first glance consistent with this alternative hypothesis. The authors do directly address previous observations of increased noise with age in their Discussion (Bahar et al. 2006; Martinez-Jimenez et al. 2017; Angelidis et al. 2019; Somel et al. 2006), although I might also suggest perhaps PMID: 20832724 PMID: 8604994, and PMID: 28965763. Their acknowledgment refers to the disagreement of their own findings of inter-tissue correlation distributions being modest and comparable between aging and development in Figure 1c. Their CoV trajectory data in Figure 2, perhaps most relevant here in Figure 2c, may also speak to this issue. Nevertheless, in my opinion it would strengthen the manuscript greatly for many readers if this alternative hypothesis were more explicitly and clearly spelled out, and then perhaps more explicitly ruled out, in the manuscript.

    Was this evaluation helpful?
  4. Reviewer #2 (Public Review):

    In this manuscript, Izgi et al investigated age-dependent gene expression pattern changes in male mice by analyzing a new bulk RNA-seq data from four different tissues collected at different ages covering post-natal development and aging. Gene expression patterns observed before and after sexual maturation seem to suggest inter-tissue divergence and convergence of gene expression profiles, respectively. The authors name that phenomenon Divergence-Convergence or "DiCo". Analysis of publicly available single cell RNA-seq [scRNAseq] datasets (from the Tabula Muris Senis consortium) suggests that such gene expression pattern changes may be explained by both alterations in tissue cell type composition, as well as by cell-autonomous expression changes. These observations may suggest that aging results in at least a partial loss of tissue identity acquired developmentally.

    Although the authors report an intriguing finding, there are major issues in the manuscript as it stands, notably concerning the clarity and rigor of the data analysis and manuscript. Notably, the authors compare expression levels across samples using the FPKM normalization method, which has been shown to be a problematic metric. There are also inconsistencies in statistical and methodological choices for which there is not a clear rationale explained in the manuscript. Finally, the authors use only male animals, which may not reflect age-related trajectories in female animals, but draw broad cross-species conclusions without raising sex as a caveat to the generalization of the conclusions.

    Was this evaluation helpful?
  5. Reviewer #3 (Public Review):

    In this manuscript Izgi et al. analyzed gene expression time-course data in four tissues during postnatal development and ageing in mice. Authors show that the expression levels of genes often reverse with ageing compared to development. Authors further show that the expression pattern diverge among the tissues during postnatal development and converge among tissues with ageing. This divergence and convergence pattern (called DiCo) is analyzes at both individual gene and genome-wide levels using multiple statistical approaches. Both cellular composition changes and cell autonomous expression changes contribute to the reversal of gene expression pattern during ageing. This study connects expression pattern during postnatal development with ageing, extending previous work on a single tissue.

    Strengths:

    - The expression convergence with age is consistently seen across multiple datasets and species indicating it can be widespread.
    - The datasets generated are unique and would be useful resource for ageing genomic community.
    - Authors go beyond bulk RNA-seq and also analyze available single cell RNA-seq datasets in mice to asses the contribution of cell composition changes and cell intrinsic expression changes to DiCo.

    Weaknesses:

    - Many aspects of expression convergence and DiCo pattern have low effect size and some are not significant. It also appears that this pattern is best seen at the genome-wide level.
    - Although there is statistical support for DiCo, there are no consistent functional associations discovered in Gene Ontology enrichment.
    - The mechanism for DiCo and the extent to which the same genes or pathways underlie this across species is unclear.

    Was this evaluation helpful?