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

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

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

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

Preprint — published as 10.1016/j.isci.2023.108740

Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1101/2022.11.24.517858

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Figures

Graphical abstract
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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Research works printed but not linked: 182

  1. Speakman J.R. and Król, E . Maximal heat dissipation capacity and hyperthermia risk: neglected key factors in the ecology of endotherms. J Anim Ecol 79, 726–746. · DOI
  2. Simpson GG . 1944. Tempo and Mode in Evolution. Columbia University Press. New York.
  3. Eldredge N and Gould SJ . 1972. Punctuated Equilibria: an alternative to phyletic gradualism. In Schopf, TJM . (ed), Models in Paleobiology. pp. 82–115. Freeman Cooper. · DOI
  4. Eldredge N . 1989. Time Frames: The Evolution of Punctuated Equilibria. Princeton University Press. · DOI · Paper page
  5. 10.2307/2411186 · DOI
  6. 10.1038/44766 · DOI · Paper page
  7. 10.1038/nature10516 · DOI
  8. 10.1016/j.jtbi.2012.08.005 · DOI
  9. 2013, Rates of speciation and morphological evolution are correlated across the largest vertebrate radiation, Nature Comms, 4, 1958, 10.1038/ncomms2958 · DOI
  10. 2017, Detection of adaptive shifts on phylogenies by using shifted stochastic processes on a tree, J R Stat Soc B, 79, 1067, 10.1111/rssb.12206 · DOI
  11. 10.1073/pnas.1710920114 · DOI · Paper page
  12. 2022, General statistical model shows that macroevolutionary patterns and processes are consistent with Darwinian gradualism, Nat Commun, 13, 1113, 10.1038/s41467-022-28595-z · DOI
  13. 10.1086/284325 · DOI · Paper page
  14. 10.1016/j.cbpa.2008.05.008 · DOI
  15. 2008, Torpor and hibernation in a basal placental mammal, the Lesser Hedgehog Tenrec Echinops telfairi, J Comp Phsiol B, 179, 691
  16. 2013, A global heterothermic continuum in mammals, Global Ecol Biogeogr, 22, 1029, 10.1111/geb.12077 · DOI
  17. 2013, Thermal Ecology, Environments, Communities, and Global Change: Energy Intake and Expenditure in Endotherms, . Ann Rev of Ecol Evol System, 44, 461, 10.1146/annurev-ecolsys-110512-135917 · DOI
  18. 2015, Daily torpor and hibernation in birds and mammals, Biol Reviews, 90, 891, 10.1111/brv.12137 · DOI
  19. 10.1111/brv.12280 · DOI
  20. 10.1242/jeb.205591 · DOI · Paper page
  21. 10.1093/icb/icad067 · DOI
  22. 2022, Low-cost thermoregulation of wild sloths revealed by heart rate and temperature loggers, J Therm Biol, 110, 103387, 10.1016/j.jtherbio.2022.103387 · DOI
  23. 10.1098/rspb.2000.1025 · DOI
  24. 2014, Reconciling theories for metabolic scaling, J Animal Ecol, 83, 20, 10.1111/1365-2656.12085 · DOI
  25. 10.1007/s00360-017-1096-3 · DOI
  26. 2020, Live in fluctuating environments, Phil Trans R Soc B, 375, 20190454, 10.1098/rstb.2019.0454 · DOI
  27. 2019, The decoupled nature of basal metabolic rate and body temperature in endotherm evolution, Nature, 572, 651, 10.1038/s41586-019-1476-9 · DOI
  28. 10.1098/rspb.2013.1629 · DOI
  29. 10.1073/pnas.1521662112 · DOI
  30. 10.1644/08-MAMM-A-089.1 · DOI
  31. 10.1042/bj3120163 · DOI
  32. 2018, . Cell size sensing in animal cells coordinates anabolic growth rates and cell cycle progression to maintain cell size uniformity, elife, 7, e26957, 10.7554/eLife.26957 · DOI
  33. 2019, Decoupling of Rates of Protein Synthesis from Cell Expansion Leads to Supergrowth, Cell Systems, 9, 434, 10.1016/j.cels.2019.10.001 · DOI · Paper page
  34. 10.1016/j.cell.2019.01.018 · DOI · Paper page
  35. 2019, Cell size homeostasis: Metabolic control of growth and cell division, BBA Mol Cell Res, 1866, 409
  36. 10.1016/j.tig.2020.01.011 · DOI
  37. 10.1093/femsyr/fow081 · DOI · Paper page
  38. 2018, Homeostasis of protein and mRNA concentrations in growing cells, Nature Comm, 9, 4496, 10.1038/s41467-018-06714-z · DOI
  39. 10.1073/pnas.71.10.4135 · DOI · Paper page
  40. 10.1016/S0300-9084(75)80139-8 · DOI
  41. 10.1016/0303-2647(79)90003-0 · DOI
  42. 2015, Thermodynamic uncertainty relation for biomolecular processes, Phys Rev Lett, 114, 158101, 10.1103/PhysRevLett.114.158101 · DOI
  43. 10.1073/pnas.1614838114 · DOI
  44. 2019, Trade-offs between error, speed, noise, and energy dissipation in biological processes with proofreading, J Phys Chem B, 123, 4718, 10.1021/acs.jpcb.9b03757 · DOI
  45. 2022, The energy cost and optimal design of networks for biological discrimination, J Royal Soc Interface, 19, 20210883, 10.1098/rsif.2021.0883 · DOI
  46. 10.1038/ng869 · DOI
  47. 10.1016/j.cell.2018.03.006 · DOI · Paper page
  48. 10.1038/s41576-023-00611-y · DOI · Paper page
  49. 10.1038/msb.2010.59 · DOI · Paper page
  50. 10.1126/science.aaa1738 · DOI · Paper page
  51. 10.1093/molbev/msi126 · DOI · Paper page
  52. 10.1073/pnas.1514974112 · DOI · Paper page
  53. 10.1038/ng1536 · DOI · Paper page
  54. 10.1038/nature07242 · DOI · Paper page
  55. 10.1038/nature07228 · DOI · Paper page
  56. 10.1101/gr.156919.113 · DOI
  57. 10.1126/science.aaa1738 · DOI · Paper page
  58. Lu GA , Zhang J , Zhao Y , Chen Q , Lin P , Tang Z , Wen H , Liufu Z and Wu CI . 2023. Canalization of phenotypes—when the transcriptome is constantly but weakly perturbed. Mol Biol Evol 40: msad005. · DOI
  59. 10.1074/jbc.M113.482570 · DOI
  60. 2017, The Proteasomal ATPases Use a Slow but Highly Processive Strategy to Unfold Proteins, Front Mol Biosci, 4, 18
  61. 2019, Empirical mean-noise fitness landscapes reveal the fitness impact of gene expression noise, Nature Comms, 10, 3180, 10.1038/s41467-019-11116-w · DOI
  62. 10.7554/eLife.05005 · DOI · Paper page
  63. 10.1146/annurev-genet-120213-092023 · DOI
  64. 10.1093/gbe/evv261 · DOI
  65. 10.1093/nar/gkz885 · DOI · Paper page
  66. 2021, MirGeneDB 2.1: toward a complete sampling of all major animal phyla, Nucl Acids Res, 50, D204
  67. Porter WP and Kearney M . 2009. Size, shape, and the thermal niche of endotherms. 2009. PNAS 106: 19666–19672. · DOI
  68. 2018, On the thermodynamic origin of metabolic scaling, Scientific Reports, 8, 1448, 10.1038/s41598-018-19853-6 · DOI
  69. 2022, Reconstructed evolutionary patterns for crocodile-line archosaurs demonstrate impact of failure to log-transform body size data, Communications Biology, 171, 1
  70. 10.1111/j.1420-9101.2011.02344.x · DOI
  71. 2019, The origin and maintenance of metabolic allometry in animals, Nature Ecol Evol, 3, 598, 10.1038/s41559-019-0839-9 · DOI
  72. 2016, Positive phenotypic selection inferred from phylogenies, Biol J Linnean Soc, 118, 915
  73. 10.1016/j.cbpa.2008.08.021 · DOI
  74. 10.1111/evo.12042 · DOI
  75. 10.1098/rsbl.2005.0378 · DOI
  76. 10.1098/rspb.2016.2328 · DOI
  77. 10.1016/j.cbpa.2010.10.004 · DOI
  78. 2022, A species-level timeline of mammal evolution integrating phylogenomic data, Nature, 602, 263, 10.1038/s41586-021-04341-1 · DOI
  79. 2017, Phylotranscriptomic consolidation of the jawed vertebrate timetree, Nature Ecol Evol, 1, 1370, 10.1038/s41559-017-0240-5 · DOI · Paper page
  80. 2023, A burst of genomic innovation at the origin of placental mammals mediated embryo implantation, Comm Biol, 6, 459, 10.1038/s42003-023-04809-y · DOI
  81. 10.1093/gbe/evy096 · DOI
  82. 2020, Ambient Temperature Cycles Affect Daily Torpor and Hibernation Patterns in Malagasy Tenrecs, Front Physiol, 11, 522, 10.3389/fphys.2020.00522 · DOI
  83. 10.1073/pnas.1419823112 · DOI
  84. 10.1126/science.aav1741 · DOI
  85. Choi IY , Kwon EC and Kim NS . 2020. The C- and G-value paradox with polyploidy, repeatomes, introns, phenomes and cell economy. Genes Genomics: 699–714. · DOI
  86. 10.1101/gad.1884710 · DOI · Paper page
  87. Langenberger D , Bartschat S , Hertel J , Hoffmann S , Tafer H and Stadler PF . 2011. MicroRNA or not microRNA? Advances in Bioinformatics and Computational Biology. pp: 1–9. Springer. · DOI
  88. 2011, Systematic curation of miRBase annotation using integrated small RNA high-throughput sequencing data for C. elegans and Drosophila, Front Genet, 2, 25
  89. 10.1007/s11103-011-9829-2 · DOI
  90. 10.4161/rna.19230 · DOI
  91. 10.1093/molbev/mst133 · DOI
  92. 10.1093/nar/gks1474 · DOI
  93. 10.1016/j.tplants.2013.11.008 · DOI
  94. 2017, Bias in recent miRBase annotations potentially associated with RNA quality issues, Sci Rep, 7, 5162, 10.1038/s41598-017-05070-0 · DOI · Paper page
  95. 10.1105/tpc.17.00851 · DOI
  96. 10.1073/pnas.1407207111 · DOI
  97. 10.1016/j.ijpara.2020.10.009 · DOI
  98. 2021, Large-Scale Annotation and Evolution Analysis of microRNA in Insects, Genome Biol Evol, 13, evab083, 10.1093/gbe/evab083 · DOI
  99. 2021, Digging into bivalve microRNAomes: between conservation and innovation, Philos Trans R Soc B, 376, 20200165, 10.1098/rstb.2020.0165 · DOI
  100. 2023, Direct observation of the evolution of cell-type-specific microRNA expression signatures supports the hematopoietic origin model of endothelial cells, Evol Dev, 25, 226, 10.1111/ede.12438 · DOI
  101. 10.1126/sciadv.add9938 · DOI
  102. 2022, MicroRNAs as Indicators into the Causes and Consequences of Whole-Genome Duplication Events, Mol Biol Evol, 39, msab344., 10.1093/molbev/msab344 · DOI
  103. 10.1038/s41586-020-2336-3 · DOI
  104. 2021, MapToCleave: High-throughput profiling of microRNA biogenesis in living cells, Cell Rep, 37, 110015, 10.1016/j.celrep.2021.110015 · DOI
  105. 2023, Accurate microRNA annotation of animal genomes using trained covariance models of curated microRNA complements in MirMachine, Cell Genomics, 3, 100348, 10.1016/j.xgen.2023.100348 · DOI
  106. 2010, Scaling of basal metabolic rate with body mass and temperature in mammals, J Animal Ecol, 79, 610, 10.1111/j.1365-2656.2010.01672.x · DOI
  107. 10.1126/science.abl8189 · DOI
  108. 2016, Phylogenetic effective sample size, J Theor Biol, 4017, 371
  109. 10.1093/bioinformatics/btm538 · DOI
  110. 10.3733/hilg.v06n11p315 · DOI · Paper page
  111. 1932, Relation between basal metabolism and mature body weight in different species of mammals and birds, Univ Missouri Agr Exp Stn Res Bull, 166, 89
  112. 2004, The predominance of quarter-power scaling in biology, Funct Ecol, 18, 257, 10.1111/j.0269-8463.2004.00856.x · DOI · Paper page
  113. 2023, ncOrtho: efficient and reliable identification of miRNA orthologs, Nucl Acids Res, 51, e71, 10.1093/nar/gkad467 · DOI
  114. 2018, Evolution of metazoan morphological disparity, PNAS, 29, E8909
  115. 10.1079/NRR19900013 · DOI
  116. 1989, Protein synthesis in several tissues of laying hens, Jpn Poul Sci, 26, 340, 10.2141/jpsa.26.340 · DOI
  117. 2004, Modeling energy utilization in broiler breeders, laying hens and broilers, Brazil J Poultry Sci, 6, 1, 10.1590/S1516-635X2004000100001 · DOI
  118. 10.1126/science.1251385 · DOI · Paper page
  119. 10.1073/pnas.1616702114 · DOI · Paper page
  120. 2018, Mammalian transposable elements and their impacts on genome evolution, Chromosome Res, 26, 25, 10.1007/s10577-017-9570-z · DOI
  121. 10.1038/ng.368 · DOI
  122. 10.1093/gbe/evu030 · DOI
  123. 10.1038/nature21683 · DOI · Paper page
  124. 10.1038/s41586-020-2486-3 · DOI · Paper page
  125. 10.1093/gbe/evs070 · DOI
  126. 10.1038/377391a0 · DOI
  127. 10.1098/rspb.2008.1012 · DOI
  128. 10.1098/rspb.2013.2780 · DOI
  129. 10.1007/s11692-016-9405-4 · DOI
  130. 2014, Introns and gene expression: Cellular constraints, transcriptional regulation, and evolutionary consequences, Bioessays, 37, 148
  131. 10.1073/pnas.1517131113 · DOI · Paper page
  132. 2018, Genome size and brain cell density in birds, Can J Zool, 96, 379, 10.1139/cjz-2016-0306 · DOI
  133. 2022, Systematic characterization of seed overlap microRNA cotargeting associated with lupus pathogenesis, BMC Biol, 20, 248, 10.1186/s12915-022-01447-4 · DOI
  134. 2020, Evolutionary dynamics of microRNA target sites across vertebrate evolution, PLoS Genetics, 16, e1008285, 10.1371/journal.pgen.1008285 · DOI
  135. 10.1007/s00360-007-0193-0 · DOI
  136. 10.1111/brv.12157 · DOI
  137. 10.1002/evan.21588 · DOI
  138. 2016, Primate torpor expression: ghost of the climatic past, Physiol, 31, 398, 10.1152/physiol.00050.2015 · DOI
  139. 2023, Rare and opportunistic use of orpor in mammals—an echo from the past?, Integr Comp Biol, 0, 1
  140. 2022, Functional genomics analysis reveals the evolutionary adaptation and demographic history of pygmy lorises, Proc Natl Acad Sci, 119, e2123030119, 10.1073/pnas.2123030119 · DOI
  141. 10.1073/pnas.1606868114 · DOI
  142. 2012, Organ-tissue level model of resting energy expenditure across mammals: new insights into Kleiber’s Law, ISRN Zool, 673050, 1-9
  143. 10.1016/j.cell.2009.01.058 · DOI · Paper page
  144. 10.1016/j.cell.2019.06.012 · DOI · Paper page
  145. 2022, Universality of Torpor Expression in Bats, Physiol Biochem Zool, 95, 326, 10.1086/720273 · DOI
  146. 10.1038/s41580-018-0045-7 · DOI · Paper page
  147. 10.1016/j.molcel.2019.06.018 · DOI
  148. 2020, The evolution of mechanisms involved in vertebrate endothermy, Phil Trans R Soc B, 375, 20190136, 10.1098/rstb.2019.0136 · DOI
  149. 10.1111/j.1461-0248.2008.01277.x · DOI
  150. 2010, Modelling the ecological niche from functional traits, Phil Trans R Soc B, 265, 3469
  151. 2021, NicheMapR – an R package for biophysical modelling: the endotherm model, Ecography, 44, 1595, 10.1111/ecog.05550 · DOI
  152. 10.1890/09-0817.1 · DOI
  153. 10.1111/ele.12155 · DOI
  154. 2016, Widespread correlations between climatic niche evolution and species diversification in birds, J Animal Ecol, 85, 869, 10.1111/1365-2656.12530 · DOI
  155. 2020, Higher temperatures lower rates of physiological and niche evolution, Proc R. Soc B, 287, 0823
  156. 2021, The evolution of climatic niche breadth in terrestrial vertebrates, J Zool Syst Evol Res, 59, 1155, 10.1111/jzs.12508 · DOI
  157. 2021, The evolution of critical thermal limits of life on Earth, Nature Comms, 12, 1198, 10.1038/s41467-021-21263-8 · DOI
  158. 10.1016/S0003-3472(80)80135-7 · DOI
  159. Crowley SR . 1985. Thermal sensitivity of sprint-running in the lizard Sceloporus undulatus: support for a conservative view of thermal physiology. Oecologia: 219–25. · DOI
  160. 10.1016/0169-5347(89)90211-5 · DOI
  161. 10.1098/rstb.2018.0549 · DOI
  162. 2018, Testing the heat dissipation limitation hypothesis: basal metabolic rates of endotherms decrease with increasing upper and lower critical temperatures, PeerJ, 6, e5725, 10.7717/peerj.5725 · DOI
  163. 10.3389/fphys.2017.01070 · DOI
  164. 10.1098/rspb.2014.1097 · DOI
  165. 10.1038/ng1803 · DOI · Paper page
  166. 10.1038/ng.905 · DOI
  167. 10.1016/j.cell.2012.04.005 · DOI · Paper page
  168. 2017, The evolutionary origin of plant and animal microRNAs, Nature Ecol Evol, 1, 0027, 10.1038/s41559-016-0027 · DOI
  169. 10.1126/science.1152326 · DOI
  170. 10.1126/science.1206375 · DOI · Paper page
  171. 2020, MicroRNAs: from mechanism to organism, Frontiers in Cell and Dev Biol, 8, 409, 10.3389/fcell.2020.00409 · DOI
  172. 10.1101/gr.251421.119 · DOI · Paper page
  173. 2020, Nuclear gene proximity and protein interactions shape transcript covariations in mammalian single cells, Nature Comms, 11, 5445, 10.1038/s41467-020-19011-5 · DOI
  174. 2023, The Integrator complex regulates microRNA abundance through RISC loading, Sci Adv, 9, eadf0597, 10.1126/sciadv.adf0597 · DOI
  175. 10.1073/pnas.151588598 · DOI · Paper page
  176. 10.1126/science.abn3943 · DOI · Paper page
  177. 10.1093/nar/gkx1042 · DOI · Paper page
  178. 10.1111/j.2041-210X.2011.00169.x · DOI
  179. 10.1093/molbev/msv150 · DOI
  180. 2013, CAPER: comparative analyses of phylogenetics and evolution in R, Methods Ecol and Evol, 3, 141
  181. 10.1242/jeb.01501 · DOI · Paper page
  182. 10.1080/10635150490522232 · DOI

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review