The maximum lifespan of mammalian species differs by more than 100-fold, ranging from ~2 years in shrews to >200 years in bowhead whales (Tacutu et al., 2013). While it has long been observed that maximum lifespan tends to correlate positively with body mass and time to maturity, but negatively with growth rate, mass-specific metabolic rate, and number of offspring (Peters, 1986; Sacher, 1959; Western, 1979), the underlying molecular basis is only starting to be understood.

One way to study the control of longevity is to identify the genes, pathways, and interventions capable of extending lifespan or delaying aging phenotypes in experimental animals. Studies using model organisms have uncovered several important conditions, such as knockout of insulin-like growth factor 1 (IGF-1) receptor (Friedman and Johnson, 1988; Holzenberger et al., 2003; Tatar et al., 2001), inhibition of mechanistic target of rapamycin (mTOR) (Harrison et al., 2009; Kenyon, 2010; Miller et al., 2014), mutation in growth hormone (GH) receptor (Coschigano et al., 2000), ablation of anterior pituitary (e.g. Snell dwarf mice) (Flurkey et al., 2002), augmentation of proteins of the sirtuin family (Chang and Guarente, 2013; Gomes et al., 2013; Mouchiroud et al., 2013; Wood et al., 2004), and restriction of dietary intake (Guarente and Kenyon, 2000; Heilbronn and Ravussin, 2003; McCay et al., 1935; Weindruch et al., 1986). While many of these genes and pathways have been verified in yeast, flies, worms, and mice, the comparisons largely involve treatment and control groups of the same species, and the extent to which they explain the longevity variations across different species is unclear. For example, do the long-lived species have metabolic profiles resembling calorie restriction? Do they suppress IGF-1 or growth hormone signaling compared with the shorter-lived species? More generally, how do the evolutionary strategies of longevity relate to the experimental strategies that extend lifespan in model organisms?

To address these questions, a popular approach has been to compare exceptionally long-lived species with closely related species of common lifespan and identify the features associated with exceptional longevity. Examples include the amino acid changes in Uncoupling Protein 1 (UCP1) and production of high-molecular-mass hyaluronan in the naked mole rat (Kim et al., 2011; Tian et al., 2013); unique sequence changes in IGF1 and GH receptors in Brandt’s bat (Seim et al., 2013); gene gain and loss associated with DNA repair, cell-cycle regulation, and cancer, as well as alteration in insulin signaling in the bowhead whale (Keane et al., 2015; Seim et al., 2014); and duplication of the p53 gene in elephants (Abegglen et al., 2015). Again, it is important to ascertain whether these mechanisms are unique characteristics of specific exceptionally long-lived species, or whether they can also help account for the general lifespan variation (Martin, 1988; Partridge and Gems, 2002).

An extension of this approach has been cross-species analyses in a larger scale. For example, several biochemical studies across multiple mammalian and bird species identified some features correlating with species lifespan. Longevity of fibroblasts and erythrocytes in vitro (Röhme, 1981), poly (ADP-ribose) polymerase activity (Grube and Bürkle, 1992), and rate of DNA repair (Cortopassi and Wang, 1996) were found to be positively correlated with longevity, whereas mitochondrial membrane and liver fatty acid peroxidizability index (Pamplona et al., 1998, 2000), rate of telomere shortening (Haussmann et al., 2003), and oxidative damage to DNA and mitochondrial DNA (Adelman et al., 1988; Barja and Herrero, 2000) showed negative correlation. The advent of high-throughput RNA sequencing (RNAseq) and mass spectrometry technologies has enabled the quantification of whole transcriptomes (Fushan et al., 2015), metabolomes (Ma et al., 2015b), and ionomes (Ma et al., 2015a), across multiple species and organs. These studies revealed the complex transcriptomic and metabolic landscape across different organs and species, as well as some overlaps with the changes observed in the long-lived mutants created in laboratory (Ma et al., 2015b).

While molecular profiling of mammals at the level of tissues may better represent the underlying biology, profiling in cell culture represents more defined experimental conditions and allows further manipulation to alter the identified molecular phenotypes. In this study, we examined the transcriptomes and metabolomes of primary skin fibroblasts across 16 species of mammals, to identify the molecular patterns associated with species longevity. We report that the genes involved in DNA repair and glucose metabolism were up-regulated in the longer lived species, whereas proteolysis and protein translocation activities were suppressed. The longer lived species also had lower levels of lysophosphatidylcholine and lysophosphatidylethanolamine and higher levels of amino acids; and the latter finding was validated in an independent dataset of bird and primate fibroblasts. Thus, molecular insights into longevity may indeed come from defined cell culture systems in mammals.

All lines of mammals descended from the same common ancestor over the previous 230 million years and have since undergone remarkable diversification in body size, metabolic rate, fertility, and longevity, with corresponding changes in the gene expression and metabolite landscape (Fushan et al., 2015; Ma et al., 2015b). As fibroblasts can be obtained without sacrificing animals and can be cultured under standardized conditions, it is of great interest to determine if their gene expression and metabolite patterns represent lifespan variation across mammals. Fibroblasts are also amenable to experimental manipulation. On the other hand, cross-species gene expression analyses are often hampered by the lack of publicly available genomes and gene orthology information, especially for those species not commonly studied. Using primary fibroblasts from 16 species of rodents, bats, and shrew, we developed a pipeline for generating species-specific ortholog sets, profiled gene expression by RNAseq and metabolites by mass spectrometry, and identified the molecular features associated with longevity traits.

Our pipeline can be easily extended for a larger number of species. We defined gene orthology based on reciprocal best hit in BLAST (Tatusov et al., 1997) and ignored the issues of gene duplication and gene loss. Sequence fragments and missing sequences were filled up using consensus data from the other species. While these steps unavoidably introduced inaccuracy within our species-specific ortholog sequences, they did not affect the overall read alignment results (Figure 2B–C, Figure 2—figure supplement 1). Furthermore, we observed no significant differences in sequence divergence between those ortholog sets showing correlation to longevity and those that did not (Wilcoxon Rank Sum Test p value = 0.32; Figure 2—figure supplement 1D), so the degree of longevity correlation was not biased by the degree of sequence conservation.

The gene expression findings revealed a clear segregation based on phylogeny (Figure 3A–B), suggesting that evolutionary relationships significantly influenced the expression patterns. On the other hand, the metabolite patterns were less clear-cut (Figure 5A–B), which might be attributed to the smaller number of species and metabolites. Using phylogenetic regression and a two-step verification procedure, we identified a list of genes and metabolites with significant correlations to multiple longevity traits. The pathways of ‘nucleotide binding', 'DNA repair’, 'chromosome organization’, and ‘glucose metabolic process’ were enriched among the genes with positive correlation with longevity, whereas ‘proteolysis’, ‘protein transportation/localization’ and ‘regulation of transcription’ were enriched for genes showing a negative correlation. Furthermore, a significant number of these longevity-correlating genes are involved in ‘housekeeping’ functions, implying that lifespan variation across species is often accompanied by coordinated shifts in the gene expression landscape and modulation of fundamental biological processes.

The link between proteolysis/autophagy and aging has been proposed by a number of authors. In general, proteolytic functions decline and oxidized proteins increase with age, and autophagy genes are required for the lifespan extension effects of Insulin/IGF-1 signaling and dietary restriction (Chondrogianni and Gonos, 2008; Hansen et al., 2008; Kenyon, 2010; Kevei and Hoppe, 2014; Löw, 2011; Meléndez et al., 2003; Rubinsztein et al., 2011; Starke-Reed and Oliver, 1989; Vernace et al., 2007). Activation of proteasome or autophagy has been shown to extend lifespan in C. elegans (Chondrogianni et al., 2015; Ghazi et al., 2007), yeast (Kruegel et al., 2011), and flies (Simonsen et al., 2008). Immunoproteasome and proteasome activity was also elevated in the livers of long-lived Snell dwarf mice and in mice exposed to drugs known to extend lifespan (Pickering et al., 2015). On the other hand, our results suggest that fibroblasts from the longer lived animals actually have lower expression levels of genes involved in proteolysis, autophagy, and apoptosis but higher expression of genes related to DNA repair and maintenance. In particular, the genes coding for the tumor suppressor TP53, apoptosis regulator BAX, and several growth and proliferation signaling pathways were all down-regulated in the fibroblasts of the longer lived species (Figure 4C–F). One possible interpretation may be that the longer lived species generate less damage and/or have better repair mechanisms, so that the cells rely less on proteolysis, autophagy and apoptosis. Previous studies reported enhanced DNA repair capacity and reduced oxidative damage in longer lived species (Adelman et al., 1988; Cortopassi and Wang, 1996; Grube and Bürkle, 1992; Perez-Campo et al., 1998; Sohal et al., 1995). Down-regulation of the ubiquitin ligase complex was also reported in the liver of longer lived mammalian species (Fushan et al., 2015). In agreement, our toxicology experiments confirmed that the fibroblasts of longer lived species were more resistant to oxidative stress induced by cadmium and paraquat treatments. In terms of metabolites, the pattern of low LPC and LPE among long-lived species was consistent with previous reports, and the positive correlation between amino acids and longevity was independently validated using fibroblasts from multiple species of primates and birds, suggesting that these changes in cell biology are likely to have evolved, independently, in each of these separate lineages in association with slower aging and longer lifespan.

On the other hand, several possible caveats in our data warrant additional attention. The levels of gene expression could be influenced by confounding factors such as gene length and proximity to other genes (Chiaromonte et al., 2003), and our definition of orthology might have missed out those genes with less conserved sequences. Our analyses were limited to those genes expressed in fibroblasts, and the effects of different spliced isoforms were not captured by our data. Furthermore, although our RNA sequencing and metabolic measurements were performed on cells of second or third passage, it was nevertheless possible that in vitro culture conditions might have introduced changes in chromatin architecture (Zhu et al., 2013), or that differences in stress-related pathways might reflect differential responses to the culture conditions. In addition, the use of ML as an aging research metric can be problematic for the less well documented animals due to the small sample size, high variance, and reliance on a single individual per species (Moorad et al., 2012). Although FTM is less prone to reporting bias and shows strong correlation with ML (Spearman correlation coefficient 0.87), it may be influenced by seasonality factors. Other parameters with better statistical properties, such as mean adult lifespan and 90th quantile of longevity (Moorad et al., 2012), should be used in place of ML in the long run, although at the moment such records are available for only a limited number of species.

Overall, our study supports the idea that gene expression, and to some degree metabolite levels, in fibroblast cultures can uncover differences in cell biology and metabolism that correspond to longer life. Apparently, these expression patterns are preserved when the intraorganismal environment is removed and cells instead are subjected to standardized cell culture conditions in the lab setting. This makes fibroblasts a particularly attractive experimental system to examine and manipulate molecular patterns, with gene expression (or metabolite patterns) as a readout. While our study represents an initial study, this approach can be extended to a larger group of species and samples, refining the molecular signatures and then manipulating them via genetic and environmental manipulations. Ultimately, this should reveal the genetic basis for differences in species longevity and lead to new strategies for targeting them, thereby shifting cells, and ultimately organisms, to the state of cells from related longer-lived species.

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Summary:

The manuscript by Gladyshev and colleagues seeks to correlate gene expression and metabolite signatures in cultured fibroblasts from 16 different mammals with longevity phenotypes of the respective mammals, including maximal lifespan, body size, female time to maturity, and stress resistance. Using RNAseq and mass spec, the authors show that the constructed gene expression profiles reflect phylogenetic relationships, and identify subsets of genes and metabolites that correlate, positively or negatively, with multiple longevity traits (but not necessarily body size). The study presents a massive amount of work, particularly in compiling the various datasets and developing the informatics pipelines to specify species-specific ortholog sets and assess the robustness of the results, which will certainly be of interest to the community of researchers studying the biology of aging. All three reviewers agreed that the study provides interesting insights into aging and longevity, and should be of broad interest. However, as detailed below, a number of concerns were also raised, relating in large part to insufficient clarity in the current version with respect to the authors' methods and the limitations of their approach and data.

Essential revisions:

1) As this fibroblast culture system is the cornerstone of the authors' approach, it is essential that they provide clear and complete details regarding the experimental strategies used to isolate and culture these cells. In particular, detailed answers to the following key questions must be available in the present manuscript:

A) What age and sex were the animals from which the fibroblasts were isolated (e.g., was% max lifespan of donor animals matched for the different species? Were they sex-matched?)?

B) From what region of the body were fibroblasts isolated? This is important since other studies have shown that fibroblasts retain a regional gene expression program (e.g., hox patterning) after isolation.

C) What is the media system used, and strategy for cell passaging? How were these culture conditions chosen, and how can it be determined if they are optimal for all species (e.g., the authors recently published on a specific sensitivity to oxygen tension for fibroblasts from lab mice, but not other rodent species). This information should be very clearly articulated and discussed in the manuscript text itself, since it forms the basis for the experimental system used.

D) At what passage number were gene expression and metabolite profiles generated?

E) What is the in vitro proliferation rate of the different fibroblast isolates, and does this correlate with any of the gene expression profiles or longevity traits analyzed?

2) It should also be discussed that in vitro cell culture conditions dramatically change chromatin architecture (Zhu et al., Cell 2013), and so gene expression patterns. The gene expression signature detected may simply reflect the ability of cells to differentially adjust to the in vitro conditions, and may contribute to the multiplex stress resistance. This caveat should be discussed in the text.

3) It is not clear whether the common 9,389 gene orthologs that were reliably detected across the 15 species are comparable to the number of expressed genes detected by RNA-seq in fibroblasts in the 5 species with annotated genomes. The base line information on how many transcripts are detected in the 5 species should be available as they may provide a way to estimate the number of potentially missing orthologs, due to sequence divergence, some of which may be critical contributors to longevity through drastic functional alterations of gene products. The text should be clarified to address this point.

4) It is not clear what the baseline gene set was for the enrichment analysis of the 827 top hits (Figure 3D). Was it 9,389 genes or whole gene sets? And if the latter, which species? The data should be presented to show the enriched pathways in the top hits using the whole gene sets vs. 9,389 genes as baseline. This is because if DNA repair pathways are already enriched among the 9,389 genes, the conclusion that it is a longevity-associated molecular signature can be misleading. It is formerly possible that genes in these ancient pathways (such as DNA repair or glucose metabolism) may be more conserved sequence-wise and therefore more enriched among the orthologs, and thus among top hits. The text and data presentation should be clarified to address this point.

5) Subsection “Longevity trait variation across mammal”. The authors need to provide more information on how they calculated residuals from body mass. The reference to Ma et al. 2015b gives allometric equations, but no information is provided regarding where those equations come from. The description in Ma et al. (residual LS = LS/(a x Mass^b)) implies that residuals were taken from a least-squares regression of log(LS) vs. log(Mass) with an intercept of log(a) and a slope of b. But this is not mentioned in the present manuscript, and must be inferred from the Ma et al. manuscript. If a least-squares regression approach was used, one important assumption is that the residuals are normally distributed. The data for Figure 1 (MLres and FTMres) do not look normally distributed. If they are not, a general linear model with an appropriate link function must be applied.

6) The authors use maximum lifespan as a metric of aging, and thus should add to the text of their manuscript a discussion of the concerns that have been raised regarding this statistic (e.g. Moorad et al. 2012 Aging Cell). In particular, it does not provide a measure of aging per se, is quite sensitive to small sample size, and is not in itself under direct selection.

7) Analyses of primate and bird species (subsection “Validation of amino acid patterns in primate and bird fibroblasts”) does not appear to be size corrected. Numerous studies have shown strong size effects of life span not only in primates, but also in birds. Therefore, size effects should be addressed here as well.

8) Subsection “Evaluation of amino acid levels in bird and primate fibroblast cell lines”. For the bird/primate analysis, the authors state, "when two or more […] features were annotated as corresponding to the same amino acid, we tabulated the degree of association from the feature most strongly correlated with lifespan among the species studied." This approach will necessarily bias the result in favor of suggesting a stronger relationship between lifespan and metabolite levels than might be true, and this caveat must be mentioned.

Essential revisions:

1) As this fibroblast culture system is the cornerstone of the authors' approach, it is essential that they provide clear and complete details regarding the experimental strategies used to isolate and culture these cells. In particular, detailed answers to the following key questions must be available in the present manuscript:

We agree that these are very important points. We have updated the Methods section to incorporate these reviewers’ comments and suggestions. Additional comments are provided below.

A) What age and sex were the animals from which the fibroblasts were isolated (e.g., was% max lifespan of donor animals matched for the different species? Were they sex-matched?)?

Fibroblasts were isolated from post-pubertal adults. Although ages of the wild-captured donors could not be reliably determined, it is a good assumption that most were young, i.e. their age was substantially less than the maximal lifespan that could be achieved by protected or captured members of the species. For mice, for example, maximum lifespan in the laboratory is approximately 3.5 to 4 years, but mice in their natural environment typically live about six months. Thus, most wild-captured adults were likely to be well below half of the species-specific maximal recorded longevity. The sex of the donors was not recorded. Variations in donor age or sex are unlikely to have generated false positive findings in our design. If such variations (age or sex, for example) did have any effects on our endpoints, such effects would have increased Type II error (false negatives), but not have led us to false positive claims (Type I error), unless these demographic variables were confounded with species life history variables. For example, if most of the animals captured from short-lived species were males, and most of those captured from longer-lived species were females, this could in principle create a false positive error by confounding species lifespan with gender effects, but such a scenario seems unlikely.

B) From what region of the body were fibroblasts isolated? This is important since other studies have shown that fibroblasts retain a regional gene expression program (e.g., hox patterning) after isolation.

We used an identical procedure to prepare cells. Fibroblasts were taken from sun-protected abdominal skin, as previously reported (Harper et al., Aging Cell 2007). They were therefore consistent across all our species.

C) What is the media system used, and strategy for cell passaging? How were these culture conditions chosen, and how can it be determined if they are optimal for all species (e.g., the authors recently published on a specific sensitivity to oxygen tension for fibroblasts from lab mice, but not other rodent species). This information should be very clearly articulated and discussed in the manuscript text itself, since it forms the basis for the experimental system used.

The detailed culture conditions are now incorporated under “Cell culture” in the Materials and methods section. In particular, these cultures were kept under low-oxygen conditions (3% O₂) after thawing to minimize selection for resistance to O₂ toxicity. All of the preparations used for RNA and metabolite testing were similar in degree of expansion, all were at the earliest possible passage number from the original skin biopsy, and were expanded under low O₂ conditions.

The cell lines were all exposed to the same culture conditions. We do not know if the conditions were "optimal" for rapid growth of cells from each species, but clearly the use of identical culture conditions is needed to avoid confounding effects due to species origin with possible effects of culture variations. We recently showed that cells from wild mice and other wild rodents are much less sensitive to O₂ toxicity than cells of laboratory mice (Patrick et al., Aging 2016), but in any case we tried to minimize any such effects by use of 3% O₂ and by doing all analyses on cells at early passage numbers.

D) At what passage number were gene expression and metabolite profiles generated?

See answer to point C) above and the revised Materials and methods section. Cryopreserved cells were prepared at passage 2 (counting the first transfer of confluent cells as passage 1); thawed aliquots of 10⁶ cells were then allowed to expand to 30 x 10⁶ cells, an increase of approximately 4 or 5 doublings.

E) What is the in vitro proliferation rate of the different fibroblast isolates, and does this correlate with any of the gene expression profiles or longevity traits analyzed?

We do not know the "in vitro proliferation" rate of our cell lines. This would vary in complex ways with seeding density, glucose and serum levels, degree of confluence at subculture, and days after seeding at which the proliferation was to be measured (lag phase, log expansion phase, or near-confluence, for example). It is possible that cell lines from different species might differ in some of these parameters, and that these differences could contribute to species differences in gene expression in a pattern that reflects longevity of the species tested, but evaluating this idea would be a complex undertaking. Nevertheless, the fibroblasts used for gene expression were collected at similar degree of confluency (with ~ 30 x 10⁶cells), so any variations in growth rate will be less likely to influence the gene expression pattern.

2) It should also be discussed that in vitro cell culture conditions dramatically change chromatin architecture (Zhu et al., Cell 2013), and so gene expression patterns. The gene expression signature detected may simply reflect the ability of cells to differentially adjust to the in vitro conditions, and may contribute to the multiplex stress resistance. This caveat should be discussed in the text.

We agree with the reviewers and have included this point in the Discussion. We used cells from the second/third passage for the RNA-seq and metabolite measurements in order to minimize in vitro culture artifacts, but it is still possible that some of the features reflect the differential responses to in vitro culture stress.

3) It is not clear whether the common 9,389 gene orthologs that were reliably detected across the 15 species are comparable to the number of expressed genes detected by RNA-seq in fibroblasts in the 5 species with annotated genomes. The base line information on how many transcripts are detected in the 5 species should be available as they may provide a way to estimate the number of potentially missing orthologs, due to sequence divergence, some of which may be critical contributors to longevity through drastic functional alterations of gene products. The text should be clarified to address this point.

We appreciate these comments and have provided the additional information in the main text and in Figure 1—source data 1C “Mapping to Genomes”.

Figure 1—source data 1C compares the results of mapping to the publicly available genomes and to our ortholog sets. 5 species (big brown bat, little brown bat, guinea pig, chinchilla, and deer mouse) have publicly available genomes in ENSEMBL and NCBI. For these species, we mapped the reads to the full genomes and the associated annotations, and counted the number of expressed genes (>10 counts).

The number of annotated genes in these species ranged between ~14,000 to ~16,000. Such variations are likely due to the different pipelines used for gene annotation. Among these annotated genes, ~10,000-11,000 had >10 counts (the same criteria for the expressed orthologs). This is similar to the 9389 orthologs we identified, indicating that our list of expressed orthologs is likely to have captured at least 80% of the expressed genes.

4) It is not clear what the baseline gene set was for the enrichment analysis of the 827 top hits (Figure 3D). Was it 9,389 genes or whole gene sets? And if the latter, which species? The data should be presented to show the enriched pathways in the top hits using the whole gene sets vs. 9,389 genes as baseline. This is because if DNA repair pathways are already enriched among the 9,389 genes, the conclusion that it is a longevity-associated molecular signature can be misleading. It is formerly possible that genes in these ancient pathways (such as DNA repair or glucose metabolism) may be more conserved sequence-wise and therefore more enriched among the orthologs, and thus among top hits. The text and data presentation should be clarified to address this point.

We have clarified the pathway enrichment procedure in the Materials and methods section.

The background for pathway enrichment was based on the 16,816 unique protein-coding genes identified as Mouse Reference (i.e. Step 1 in Figure 2A, after removing highly repetitive and highly similar sequences). The pathways were based on Mus musculus. We have now also included pathway enrichment statistics performed using only the 9389 expressed orthologs as background (Table 1—source data 1G,H). In both cases, the top enriched pathways remained statistically significant.

Regarding the comment “It is formerly possible that genes in these ancient pathways (such as DNA repair or glucose metabolism) may be more conserved sequence-wise and therefore more enriched among the orthologs, and thus among top hits”, we would like to discuss it in terms of the following aspects:

1) We agree with the reviewers that the genes in the DNA repair and glucose metabolism pathways are likely well conserved across the species. In fact, our additional analysis suggested that many of our ML-associated genes are “housekeeping genes” (Table 1—source data 1I). This is consistent with the notion that lifespan regulation across the species likely requires the co-ordination of a number of basic biological and cellular pathways. Since the longevity variation falls along a continuum across the different mammalian species, it is logical that the underlying evolution forces act on a set of pathways that are common (and conserved) across these species (i.e. the “public” pathways).

2) To the extent that our results are biased by sequence conservation (i.e. that we identified the DNA repair and glucose metabolism pathways only because of their high sequence conservation, and that we miss out many other important genes due to our ortholog identification strategy), we do not believe this is the case for the following reasons:

A) First, for those species with publicly available genomes, when we mapped the reads to the public genomes and used the public annotations, only ~10,000 to ~11,000 genes were expressed (based on a criterion of > 10 counts; same criterion in our ortholog filtering step), even though ~14,000 to ~16,000 genes were annotated (Figure 1—source data 1C). This is mainly because the expression of many genes are tissue-specific, so they are simply not expressed in fibroblasts. In comparison, we identified 9389 expressed ortholog sets across all 15 species. Our 9389 orthologs should therefore represent at least 80% of all the expressed genes in fibroblasts of each species, and the correlation was between 0.93 to 0.98 for the read counts.

B) To address the issue of sequence conservation and divergence, we calculated the median DNA distance (using Kimura 2-parameters distance) for each ortholog set (Figure 2—figure supplement 1C and D). In particular, among our 9389 expressed orthologs, there was no statistical difference in DNA distance between those orthologs that showed correlation with longevity and those orthologs that did not (Wilcoxon Rank Sum Test p value = 0.32; see Author response image 1). In other words, the degree of sequence conservation or divergence is not linked to the degree of correlation with longevity. There are many other genes that are either more conserved or less conserved in sequence than the genes in DNA repair or glucose metabolism; but these other genes did not show up in the top hits because of their lack of expression correlation to longevity, not because of their sequence conservation or divergence per se.

Author response image 1

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5) Subsection “Longevity trait variation across mammal”. The authors need to provide more information on how they calculated residuals from body mass. The reference to Ma et al. 2015b gives allometric equations, but no information is provided regarding where those equations come from. The description in Ma et al. (residual LS = LS/(a x Mass^b)) implies that residuals were taken from a least-squares regression of log(LS) vs. log(Mass) with an intercept of log(a) and a slope of b. But this is not mentioned in the present manuscript, and must be inferred from the Ma et al. manuscript.

We have now included an additional paragraph (“Life history data of the species”) under the Methods section to provide more information on the allometric equation. The ML equation, MLres = ML/(4.88×AW^0.153), was based directly on the documentation of the AnAge database (http://genomics.senescence.info/help.html#anage). The relevant paragraph was quoted here:

“For mammals, also included is the maximum longevity (tmax) residual, expressed as a percentage of the expected maximum longevity calculated from the adult body size (M) and derived from the mammalian allometric equation: tmax = M^0.153. This is useful to identify species that live longer than expected for their body size. Cetaceans were excluded because we have less confidence in their longevity records, obtained from studies in the wild often using indirect methods, than in those from other mammalian taxa.”

The FTM equation, FTMres = FTM/(78.1×AW^0.217), was calculated by us, by linear regression using the FTM and body mass records of 1330 mammalian species in the AnAge database.

If we had used the same procedure to calculate the MLres equation, we would obtain MLres = ML/(4.36×AW^0.158), which is comparable to the AnAge documentation.

If a least-squares regression approach was used, one important assumption is that the residuals are normally distributed. The data for Figure 1 (MLres and FTMres) do not look normally distributed. If they are not, a general linear model with an appropriate link function must be applied.

We had tested the normalcy of the data using Shapiro test. The results were as follows (if p-value < 0.05, then the data is not normally distributed):

Therefore, none of the longevity traits violated normalcy assumption.

Furthermore, we also tested the normalcy of the gene expression data and metabolite data. Using Shapiro test, 89% of the genes and 90% of the metabolites did not violate normalcy assumption on log₁₀ scale.

6) The authors use maximum lifespan as a metric of aging, and thus should add to the text of their manuscript a discussion of the concerns that have been raised regarding this statistic (e.g. Moorad et al. 2012 Aging Cell). In particular, it does not provide a measure of aging per se, is quite sensitive to small sample size, and is not in itself under direct selection.

Thank you. We have included this point in the Discussion section. Additional parameters such as mean adult lifespan and 90^th percentile of longevity (Moorad et al., 2012) should be used in place of ML in the long run, although at the moment such records are available for only a limited number of species.

7) Analyses of primate and bird species (subsection “Validation of amino acid patterns in primate and bird fibroblasts”) does not appear to be size corrected. Numerous studies have shown strong size effects of life span not only in primates, but also in birds. Therefore, size effects should be addressed here as well.

We have now expanded Table 2 to include the body-mass adjusted lifespan correlation. We would like to point out that:

A) Removal of body mass weakened the observed correlation (Table 2). This is likely due to the inherent strong correlation between maximum lifespan and body mass. Indeed, the same weakening is also observed among the rodent fibroblasts (Table 2—source data 1), indicating the two datasets are in fact consistent.

B) The same weakening of correlation is also observed in our previous studies (Ma et al., 2015 Cell Metabolism and Ma et al., 2015 Cell Reports). This is in fact expected, given the strong correlation between maximum lifespan and body mass. It is highly debatable whether one could completely disentangle these 2 traits, since they are likely under the same natural selection force.

C) Whether the body-mass should be removed in cross-species longevity studies is an area where experts in comparative biology hold widely divergent views. Some (e.g. John Speakman) think that "uncorrected" data are not interpretable; another set, equally experienced (e.g. Tony Hulbert), thinks that an attempt to "correct" for mass will create a major risk for missing key associations, since mass and lifespan are so strongly confounded across species. Others suggest that one should report both unadjusted and adjusted values so that readers can see whichever set of results they feel is most valid.

D) Such associations, whether or not corrected for mass, are only a first step towards more definite, causal, hypotheses. Some remain significant under both ML and MLres, while others do not. Nevertheless, they can all be potential candidates for further experimental verification.

8) Subsection “Evaluation of amino acid levels in bird and primate fibroblast cell lines”. For the bird/primate analysis, the authors state, "when two or more […] features were annotated as corresponding to the same amino acid, we tabulated the degree of association from the feature most strongly correlated with lifespan among the species studied." This approach will necessarily bias the result in favor of suggesting a stronger relationship between lifespan and metabolite levels than might be true, and this caveat must be mentioned.

This selection reflects, in part, uncertainties in the annotation of specific features in the untargeted metabolomics protocol used for the bird and primate species. In most cases where two or more of the annotated features were thought to represent the same amino acid, there was very good agreement. The table below gives the key information on this point. It lists each amino acid for which there was more than one annotated feature, and gives the p-values for the relationship with species lifespan for each such feature.

In four cases (tryptophan, phenylalanine, proline, valine), all such features show a significant association with lifespan. For the other three, the features that do not meet the traditional p = 0.05 criterion have two-sided p-values between 0.05 and 0.2, with the same sign of the slope coefficient.

We agree with the reviewer that our decision to present the feature with the best fit to the MLS regression could in principle create bias and thus increase Type I error, but in this case there are several reasons for confidence: the close fit between each of the nominal "replicate" features within the bird/primate data set, the good agreement across each of the 10 amino acids evaluated, and the excellent agreement to the relationship seen for the rodent cell lines evaluated with an entirely different method.

Author details

1. Siming Ma

   Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States

   Contribution

   SM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

2. Akhil Upneja

   Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States

   Contribution

   AU, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Andrzej Galecki

   1. Department of Pathology, University of Michigan Medical School, Ann Arbor, United States
   2. Geriatrics Center, University of Michigan Medical School, Ann Arbor, United States
   3. Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, United States

   Contribution

   AG, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Yi-Miau Tsai

   1. Department of Pathology, University of Michigan Medical School, Ann Arbor, United States
   2. Geriatrics Center, University of Michigan Medical School, Ann Arbor, United States

   Contribution

   Y-MT, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Charles F Burant

   Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, United States

   Contribution

   CFB, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Sasha Raskind

   Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, United States

   Contribution

   SR, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Quanwei Zhang

   Department of Genetics, Albert Einstein College of Medicine, Bronx, United States

   Contribution

   QZ, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Zhengdong D Zhang

   Department of Genetics, Albert Einstein College of Medicine, Bronx, United States

   Contribution

   ZDZ, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Andrei Seluanov

   Department of Biology, University of Rochester, Rochester, United States

   Contribution

   AS, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Vera Gorbunova

    Department of Biology, University of Rochester, Rochester, United States

    Contribution

    VG, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Clary B Clish

    Broad Institute, Cambridge, United States

    Contribution

    CBC, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Richard A Miller

    1. Department of Pathology, University of Michigan Medical School, Ann Arbor, United States
    2. Geriatrics Center, University of Michigan Medical School, Ann Arbor, United States

    Contribution

    RAM, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

13. Vadim N Gladyshev

    Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, United States

    Contribution

    VNG, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    For correspondence

    vgladyshev@rics.bwh.harvard.edu

    Competing interests

    The authors declare that no competing interests exist.

    "This ORCID iD identifies the author of this article:" 0000-0002-0372-7016

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