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PreprintCited by 1MicroRNA in disease regulationPhysiological and biochemical adaptationsAdipose Tissue and Metabolism

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Rapid Adaptation of Cellular Metabolic Rate to the MicroRNA Complements of Mammals and its Relevance to the Evolution of Endothermy

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Fromm, Bastian, Sorger, Thomas·2022·iScience
DOI 10.1101/2022.11.24.517858PMC PMC10847693

Preprint — published as 10.1016/j.isci.2023.108740

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Abstract

SUMMARY: The metabolic efficiency of mammalian cells depends on attenuation of intrinsic translation noise by microRNAs. We devised a metric of cellular metabolic rate ( cMR) , rMR / M exp optimally fit to the number of microRNA families ( miRNA.Fam ), that is robust to variation in mass and sensitive to body temperature, consistent with the Heat Dissipation Limit Theory of Speakman and Król (2010) 1 . Using miRNA.Fam as predictor, an Ornstein-Uhlenbeck process of stabilizing selection, with an adaptive shift at the divergence of Boreoeutheria, accounted for 95% of the variation in cMR across mammals. Branchwise rates of evolution of cMR , miRNA.Fam and body temperature concurrently increased 6- to 7-fold at the divergence of Boreoeutheria, independent of mass. Cellular MR variation across placental mammals was also predicted by the sum of model conserved microRNA-target interactions, revealing an unexpected degree of integration of the microRNA-target apparatus into the energy economy of the mammalian cell.

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Graphical abstract CC BY-NC-ND · PubMed Central
Figure 1 Allometric scaling of resting MRs by maximum likelihood Comparison of the allometric scaling of the rMRs of 20 vertebrates by (A) the conventional power law method, or (B) maximum likelihood scaling according to the optimal fit (M0.80: pink circles), or two suboptimal fits (M0.75, M0.85: gray circles).
Figure 1 Allometric scaling of resting MRs by maximum likelihood Comparison of the allometric scaling of the rMRs of 20 vertebrates by (A) the conventional power law method, or (B) maximum likelihood scaling according to the optimal fit (M0.80: pink circles), or two suboptimal fits (M0.75, M0.85: gray circles).CC BY-NC-ND · PubMed Central
Figure 2 “Sliding Window” comparison of the inference of sMR and cMR Staggered groups of five mammals ranked by Tb were designated by the number of Boreoeutheria. (A) The effect of group composition on the mean value of mass or body temperature (right axis). (B) The effect of group composition on the allometric exponent of sMR (the slope of log rMR versus log M), or the maximum likelihood exponent of cMR (means ±½ standard deviation).
Figure 2 “Sliding Window” comparison of the inference of sMR and cMR Staggered groups of five mammals ranked by Tb were designated by the number of Boreoeutheria. (A) The effect of group composition on the mean value of mass or body temperature (right axis). (B) The effect of group composition on the allometric exponent of sMR (the slope of log rMR versus log M), or the maximum likelihood exponent of cMR (means ±½ standard deviation).CC BY-NC-ND · PubMed Central
Figure 3 Evolution of the number of microRNA families and cellular metabolic rate (cMR) (A) Distance tree of microRNA families. Branch lengths represent the total number of families. Node values represent the number of shared microRNA families in each clade. (B) An ultrametric reference phylogeny (inset) was assembled based on divergence times drawn from Álvarez-Carretero et al. (2022)78 for mammals and Irisarri et al. (2017)79 for earlier-branching vertebrates. The reference tree was rescaled (BayesTraits) according to the rate of evolution of cMR.75, which corresponds to the optimal allometric scaling in a model of stabilizing selection of rMR with respect to the number of microRNA families in tetrapods and mammals (see Figure 5). Values of cMR.75 inferred at ancestral nodes (BayesTraits) were then mapped onto this variable rate tree (see STAR methods).
Figure 3 Evolution of the number of microRNA families and cellular metabolic rate (cMR) (A) Distance tree of microRNA families. Branch lengths represent the total number of families. Node values represent the number of shared microRNA families in each clade. (B) An ultrametric reference phylogeny (inset) was assembled based on divergence times drawn from Álvarez-Carretero et al. (2022)78 for mammals and Irisarri et al. (2017)79 for earlier-branching vertebrates. The reference tree was rescaled (BayesTraits) according to the rate of evolution of cMR.75, which corresponds to the optimal allometric scaling in a model of stabilizing selection of rMR with respect to the number of microRNA families in tetrapods and mammals (see Figure 5). Values of cMR.75 inferred at ancestral nodes (BayesTraits) were then mapped onto this variable rate tree (see STAR methods).CC BY-NC-ND · PubMed Central
Figure 4 Phylogenetic regression of cMR.68 (rMR/M0.68) on miRNAFam (A) The number of microRNA families in non-avian tetrapods accounted for 84% of the variation in cMR.68, independent of phylogenetic distance (see Table S3). (B) The same data scaled to the slope of conventional sMR (log rMR/log mass = 0.64) in 211 birds.29
Figure 4 Phylogenetic regression of cMR.68 (rMR/M0.68) on miRNAFam (A) The number of microRNA families in non-avian tetrapods accounted for 84% of the variation in cMR.68, independent of phylogenetic distance (see Table S3). (B) The same data scaled to the slope of conventional sMR (log rMR/log mass = 0.64) in 211 birds.29CC BY-NC-ND · PubMed Central
Figure 5 BM and OU models for the variation of cMR with respect to mirFam Curves represent the corrected values of the Akaike information criterion (AICc) for each model, and the rate of adaptation (% tree height) for the OU models (see STAR methods). Note the different scales of the ordinate axes. (A) 14 tetrapods (excluding birds and cows). (B) 10 mammals (excluding cows).
Figure 5 BM and OU models for the variation of cMR with respect to mirFam Curves represent the corrected values of the Akaike information criterion (AICc) for each model, and the rate of adaptation (% tree height) for the OU models (see STAR methods). Note the different scales of the ordinate axes. (A) 14 tetrapods (excluding birds and cows). (B) 10 mammals (excluding cows).CC BY-NC-ND · PubMed Central
Figure 6 Design and analysis of simulated adaptive shifts in the evolution of cMR in relation to the predictor mirFam (A) The variance/covariance matrix for the regression of cMR.75 on mirFam was used as the selection strength matrix to generate pairs of values for these traits. The average values of these traits across all mammals, or across placental mammals, were assigned to the first shift (divergence of mammals) and second shift, respectively. Phenogram colors correspond to the log of the cMR.75 values scaled to the original, 36-fold range. (B) Correlations with the actual cMR.75 distribution of the average of ten simulations of the OU or BM process in 14 tetrapods. Adaptive shifts were modeled at the divergence of Mammalia (single shift), with a second shift at the divergence of either Placentalia, Boreoeutheria, or Euarchontoglires (p = 1.23e-05 for the difference in correlation between OU and BM for 10 simulations with the Boreoeutheria shift). (C) Validation of the estimated rates of adaptation: prediction of shift positions according to the % tree height obtained from ten OU models. Shift positions are indicated by the first letter of each diverging clade (M, P, B, E; p = 5.19e-06, ordinal logistic regression).
Figure 6 Design and analysis of simulated adaptive shifts in the evolution of cMR in relation to the predictor mirFam (A) The variance/covariance matrix for the regression of cMR.75 on mirFam was used as the selection strength matrix to generate pairs of values for these traits. The average values of these traits across all mammals, or across placental mammals, were assigned to the first shift (divergence of mammals) and second shift, respectively. Phenogram colors correspond to the log of the cMR.75 values scaled to the original, 36-fold range. (B) Correlations with the actual cMR.75 distribution of the average of ten simulations of the OU or BM process in 14 tetrapods. Adaptive shifts were modeled at the divergence of Mammalia (single shift), with a second shift at the divergence of either Placentalia, Boreoeutheria, or Euarchontoglires (p = 1.23e-05 for the difference in correlation between OU and BM for 10 simulations with the Boreoeutheria shift). (C) Validation of the estimated rates of adaptation: prediction of shift positions according to the % tree height obtained from ten OU models. Shift positions are indicated by the first letter of each diverging clade (M, P, B, E; p = 5.19e-06, ordinal logistic regression).CC BY-NC-ND · PubMed Central
Figure 7 Variable rate trees and inference of ancestral node values for cMR, Tb and mirFam The ultrametric reference phylogeny of mammals (constant rate model) was rescaled according to the rate of evolution of each of six traits (A–F): cMR.70, cMR.75, cMR.80, mirFam, Tb (ºC) and log2M. The length of the branch for the divergence of Boreoeutheria was compared (%) to the distance between the divergence of Theria and the divergence of Primates. Bayes factors were computed by comparing the median log likelihoods of the 100 most likely trees to the median log likelihoods of the trees with the smallest non-zero sum of changes to branch i.e., those trees that differed the least from the constant rate model. This criterion was adopted in lieu of the marginal likelihoods of the variable and constant rate models, which reflect the overall fit of all of the branches. The range of ancestral node values inferred in a Brownian motion context were mapped onto each tree (see STAR methods). The value at each node represents the median of the ten reconstructions with the highest likelihood. In panel C (mirFam), the lengths of the branches leading to humans and to the ancestral Therian have been reduced by half.
Figure 7 Variable rate trees and inference of ancestral node values for cMR, Tb and mirFam The ultrametric reference phylogeny of mammals (constant rate model) was rescaled according to the rate of evolution of each of six traits (A–F): cMR.70, cMR.75, cMR.80, mirFam, Tb (ºC) and log2M. The length of the branch for the divergence of Boreoeutheria was compared (%) to the distance between the divergence of Theria and the divergence of Primates. Bayes factors were computed by comparing the median log likelihoods of the 100 most likely trees to the median log likelihoods of the trees with the smallest non-zero sum of changes to branch i.e., those trees that differed the least from the constant rate model. This criterion was adopted in lieu of the marginal likelihoods of the variable and constant rate models, which reflect the overall fit of all of the branches. The range of ancestral node values inferred in a Brownian motion context were mapped onto each tree (see STAR methods). The value at each node represents the median of the ten reconstructions with the highest likelihood. In panel C (mirFam), the lengths of the branches leading to humans and to the ancestral Therian have been reduced by half.CC BY-NC-ND · PubMed Central
Figure 8 Evolution of factors in a model for energy cost saving by global microRNA activity Sensitivity to Repression: the median value of the distribution of cumulative weighted context scores for all models of the conserved targets of conserved microRNAs (see STAR methods). Conserved Sites per Cell: the total number of sites was standardized with respect to the number of coding genes in each species. AA cost/site: the weighted sum of 500 amino acid-length bins for the distribution of a non-redundant set of target proteins between 500 and 5,000 amino acids (see STAR methods).
Figure 8 Evolution of factors in a model for energy cost saving by global microRNA activity Sensitivity to Repression: the median value of the distribution of cumulative weighted context scores for all models of the conserved targets of conserved microRNAs (see STAR methods). Conserved Sites per Cell: the total number of sites was standardized with respect to the number of coding genes in each species. AA cost/site: the weighted sum of 500 amino acid-length bins for the distribution of a non-redundant set of target proteins between 500 and 5,000 amino acids (see STAR methods).CC BY-NC-ND · PubMed Central
Figure 9 Contour plot of a hypothetical fitness landscape mapped on the relationship between the core-surface temperature gradient (ΔTemp) and Exp, the optimal allometric exponent for maximum likelihood fitting of cMR to the distribution of mirFam The dashed lines represent the log-based allometric exponents for field metabolic rate (fMR) and the theoretical heat dissipation limit (HDL) in mammals (Speakman and Król, 2010).1 Silhouettes represent Boreoeutheria, earlier-branching mammals, and birds. (Phylophic © 2023: Marsupial, Sarah Werning; Dog, Tracy A. Heath; Bird, T. Michael Keesey). See STAR methods for data sources.
Figure 9 Contour plot of a hypothetical fitness landscape mapped on the relationship between the core-surface temperature gradient (ΔTemp) and Exp, the optimal allometric exponent for maximum likelihood fitting of cMR to the distribution of mirFam The dashed lines represent the log-based allometric exponents for field metabolic rate (fMR) and the theoretical heat dissipation limit (HDL) in mammals (Speakman and Król, 2010).1 Silhouettes represent Boreoeutheria, earlier-branching mammals, and birds. (Phylophic © 2023: Marsupial, Sarah Werning; Dog, Tracy A. Heath; Bird, T. Michael Keesey). See STAR methods for data sources.CC BY-NC-ND · PubMed Central
Figure 10 Variation in mass-independent residuals of rMR and fMR with respect to Tb and Ta Species include one Monotreme, two Afrotheria, 20 Marsupials and 27 Boreoeutheria. Linear models of rMR and fMR variation were compared as a function of body temperature (A) or ambient temperature (B).
Figure 10 Variation in mass-independent residuals of rMR and fMR with respect to Tb and Ta Species include one Monotreme, two Afrotheria, 20 Marsupials and 27 Boreoeutheria. Linear models of rMR and fMR variation were compared as a function of body temperature (A) or ambient temperature (B).CC BY-NC-ND · PubMed Central
Figure 11 Thermoregulatory status and the distributions of residuals for log Cmin and UCT 276 mammals from the dataset of Khaliq et al. (2014)165 were divided between Chiroptera and earlier-branching mammals (low incidence of homeothermy) versus later-branching Boreoeutheria. Bovids are highlighted because of the well-known constraint that high ambient temperature imposes on the rate of lactation (for references, see Speakman and Król, 20101). (A) For any bMR, later-branching Boreoeutheria are disproportionally represented at the upper end of the range of thermal conductance. (B) The highest upper critical temperatures among later-branching Boreoeutheria are associated with the slowest cellular metabolic rates.
Figure 11 Thermoregulatory status and the distributions of residuals for log Cmin and UCT 276 mammals from the dataset of Khaliq et al. (2014)165 were divided between Chiroptera and earlier-branching mammals (low incidence of homeothermy) versus later-branching Boreoeutheria. Bovids are highlighted because of the well-known constraint that high ambient temperature imposes on the rate of lactation (for references, see Speakman and Król, 20101). (A) For any bMR, later-branching Boreoeutheria are disproportionally represented at the upper end of the range of thermal conductance. (B) The highest upper critical temperatures among later-branching Boreoeutheria are associated with the slowest cellular metabolic rates.CC BY-NC-ND · PubMed Central

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Metadata

Authors
Fromm, B., & Sorger, T.
Affiliations
  • UiT The Arctic University of Norway (NO)
  • Roger Williams University (US)
Corresponding author
Fromm, Bastian
ORCID
Venue
iScience
Publisher
openRxiv
Published
2022-11-25
Volume
27
Issue
2
Pages
108740-108740
ISSN
2589-0042 (ISSN-L)
DOI
10.1101/2022.11.24.517858
PMC
PMC10847693
OpenAlex
W4390030483
Cited by
1
References
2
Citations by year
2024: 1
FWCI
0.13
Type
journal article
Language
en
Open access
Licence
cc-by-nc-nd
Locations
  • iScience (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • Roger Williams University - Digital Commons (Roger Williams University) (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • Universitetet i Tromsø
  • Tromsø Forskningsstiftelse (20_SG_BF)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › MicroRNA in disease regulation
  • Physical Sciences › Environmental Science › Ecology › Physiological and biochemical adaptations
  • Health Sciences › Medicine › Physiology (medical) › Adipose Tissue and Metabolism
Keywords
microRNA, endothermy evolution, miRNA-target interactions, placental mammals, energy metabolism

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This page
CoChat. (2026). Citation check: Rapid Adaptation of Cellular Metabolic Rate to the MicroRNA Complements of Mammals and its Relevance to the Evolution of Endothermy. https://app.cochat.ai/papers/10.1101/2022.11.24.517858
The paper
Fromm, B., & Sorger, T. (2022). Rapid Adaptation of Cellular Metabolic Rate to the MicroRNA Complements of Mammals and its Relevance to the Evolution of Endothermy. iScience. https://doi.org/10.1101/2022.11.24.517858