sign in sign up
<<
Home , Birth, Euarchonta, History of life, Mammalian reproduction, Nocturnal bottleneck, Primatomorpha

G C A T T A C G G C A T genes

Article of Reproductive History in and the Associated Change of Functional Constraints

Jiaqi Wu 1,2,*,† , Takahiro Yonezawa 3,† and Hirohisa Kishino 4,5

1 School of Life and Technology, Tokyo Institute of Technology, Meguro Ward, Tokyo 152-8550, Japan 2 Department of Molecular Life Science, Tokai University School of Medicine, Isehara, Kanagawa 259-1193, Japan 3 Faculty of Agriculture, Tokyo University of Agriculture, Atsugi City, Kanagawa 243-0034, Japan; [email protected] 4 Graduate School of Agricultural and Life Sciences, The University of Tokyo, Bunkyo Ward, Tokyo 113-8657, Japan; [email protected] 5 The Research Institute of Evolutionary , Setagaya Ward, Tokyo, 138-0098, Japan * Correspondence: [email protected] † These authors contributed equally to this work.

Abstract: Phylogenetic based on multiple genomic loci enable us to estimate the evolution of functional constraints that operate on genes based on lineage-specific fluctuation of the evolutionary rate at particular gene loci, “gene–branch interactions”. Using this information as predictors, our previous work inferred that the common ancestor of placental mammals was nocturnal, insectivorous, solitary, and bred seasonally. Here, we added seven new continuous traits including lifespan, bodyweight, and five -related traits and inferred the network of 14 core life  history traits for 89 mammals. In this network, bodyweight and lifespan are not directly connected to  each other; instead, their correlation is due to both of them coevolving with period. Diurnal Citation: Wu, J.; Yonezawa, T.; mammals are more likely to be monogamous than nocturnal mammals, while arboreal mammals Kishino, H. Evolution of tend to have a smaller litter size than terrestrial mammals. Coevolution between diet and the seasonal Reproductive Life History in breeding behavior test shows that -round breeding preceded the dietary change to omnivory, Mammals and the Associated Change while seasonal breeding preceded the dietary change to carnivory. We also discuss the evolution of of Functional Constraints. Genes 2021, reproductive strategy of mammals. Genes selected as predictors were identified as well; for example, 12, 740. https://doi.org/10.3390/ genes function as tumor suppressor were selected as predictors of age. genes12050740

Keywords: Academic Editor: Sergio Minucci rate-based prediction approach; evolutionary history of reproduction-related traits; gestation period; weaning period; to independence; litter size; female sexual maturity age;

Received: 31 March 2021 reproductive strategy; genes identified for reproduction-related traits Accepted: 11 May 2021 Published: 14 May 2021

Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in As fitness—how well an adapts to its environment—is described by the published maps and institutional affil- reproductive success of the individual, reproduction strategy is the most important life iations. history trait for living . For gametogony species, reproduction needs cooperation between the two sexes. The contains much more nutrition than the cell; thus, from the beginning, the female’s contribution to the next generation is much greater than the male’s contribution. Mammalian species in particular are characterized by , Copyright: © 2021 by the authors. and the female’s mammary glands produce milk for nursing her offspring. Therefore, Licensee MDPI, Basel, Switzerland. parental investment in mammals covers a broad range including gestating and weaning This article is an open access article the young, providing food, protecting the young from predators, huddling, grooming distributed under the terms and and carrying the offspring, and teaching the basic skills to survive. Such investments conditions of the Creative Commons are mainly conducted by the female. Accordingly, they place physiological constraints Attribution (CC BY) license (https:// on female mammals [1,2]. The heavy investment causes a smaller litter size, namely the creativecommons.org/licenses/by/ number of offspring per litter, in mammals compared with and [3–5]. In 4.0/).

Genes 2021, 12, 740. https://doi.org/10.3390/genes12050740 https://www.mdpi.com/journal/genes Genes 2021, 12, 740 2 of 10

some mammalian species, the male may also help to raise the young. Male provisioning in mammals may increase the litter size, indicating an evolutionary benefit in terms of reproductive success [6]. A trade-off between quantity and quality of offspring is a key reproduction strategy. As mentioned above, litter sizes of mammals are generally smaller than those of other [5], suggesting that mammals are generally K-strategists, comprising species in which the populations are suitable for stability at or near the carrying capacity (K) of stable environments. The reproduction of K-strategists is usually slower and produces fewer offspring. Nevertheless, the numbers of offspring vary among mammalian species. For example, litter size of the common tenrec (Tenrec ecaudatus) ranges up to 32 (18 offspring per litter on average). Litter sizes are usually smaller in larger mammals, while the variance in litter sizes is larger in smaller mammals [5], suggesting that smaller mammals have various reproduction strategies. To clarify the coevolution pattern among reproduction- related life history traits is very important for understanding strategies. However, little is known about the transformations of reproduction strategies during mammalian evolution because the record of reproduction-related traits is generally poorly preserved. In our previous study [7], we built a new framework of statistical genomic analysis to reconstruct ancestral states. Selection pressure comes mainly from the environment. Biological traits including behavior are the result of to the environment. There- fore, fluctuations in the selective constraint on each gene may well record the history of biological traits. Based on this idea, we modelled the differences in the evolutionary rates of individual genes in the . In this model, genomic evolution is the context behind the evolution of each single gene. Each single gene has its own unique evolutionary rate (“gene effect”) due to its functional constraints. On the other hand, each species has its own genome-wide rate (“genomic rate”, the background molecular evolutionary rates that are shared by all genes, see Wu et al., 2017 [7]), which depends on generation length and exposure to mutagens. Furthermore, each gene may have a species-specific acceleration or deceleration of the evolutionary rate, called “gene–branch interaction”. Evolutionary rates of individual genes in each species can be expressed by the product of these three rates, and the last one captures species-specific fluctuations of selective constraints from the environment. We performed a regression analysis of the gene–branch interactions and traits of extant species and then used the fitted model to predict ancestral states. By this approach, we could reconstruct the evolutionary history of traits and obtained the genes that may be related to the trait. In our previous work, we mainly focused on discrete traits. By a simple modification, the approach can also be applied to continuous traits. Elucidating the evolutionary history, coevolution network, and genes related to these traits should provide a new view for understanding mammalian reproduction.

2. Method and Materials 2.1. Sequence Data and Species We downloaded alignments of 1204 single-copy genes of 89 mammals and the species tree in Wu et al., 2017 [7], and used them in this study.

2.2. Life History Traits Related to Reproduction Besides 10 life history traits published in Wu et al., 2017 [7], we collected seven additional traits that are related to reproduction of these 89 mammals, namely average gestation period, average weaning age, average time to independence, average number of offspring, female average age at sexual or reproductive maturity, lifespan, and bodyweight from the Animal Diversity Web (Table S1). Genes 2021, 12, 740 3 of 10

2.3. Rate-Based Ancestral State Reconstruction on Continuous Traits In Wu et al., 2017 [7], we modelled the rate of as the product of branch effect, gene effect, and gene–branch interactions. Branch effect depends on genera- tion length and exposure to mutagens, and contains information on genomic evolution; gene effect is unique for each gene and is the level of functional constraints conducts on each gene; gene–branch interaction is the fluctuation of molecular evolutionary rate across each gene and branch and contains information on historical . By regressing gene–branch interactions to the trait value at terminal branches, we could obtain a model to predict trait values using gene–branch interactions. Applying the fitted model to gene– branch interactions of ancestral branches, we could then also predict ancestral states. The underlying assumption of this method is that traits with similar values also have a similar pattern of changes in molecular evolutionary rates of related genes. In Wu et al., 2017 [7], we mainly focused on discrete traits and used LASSO penalized logistic regression to fit the model; for continuous traits, we directly regressed gene–branch interactions to trait values. We used log-transferred trait values for continuous traits to fit the regression model. Ancestral states of all seven traits collected in this work were calculated and summarized in Supplementary Table S2. LASSO penalized logistic regression shrinks the coefficients of nonsignificant variables to zero, leaves only a small proportion of variables as signif- icant predictors. A similar approach was used by Chikina’s group and associates the gene-specific rates with the change in trait states [8]. When we fit the model, we excluded terminal branches that have a missing value or uncertain trait states; for example, mammals that do not have any records on its weaning age were excluded when we fit the regression model of weaning age. The terminal branches that were removed during model-fitting also have a full set of gene–branch interactions. We could apply the fitted model to terminal branches and impute its trait values.

2.4. Trait Coevolution Network Besides the seven traits newly collected in this work, we also included ten discrete traits that were published in Wu et al., 2017 [7]. We interpreted missing values using the regression model. We calculated partial correlations for ten discrete traits, five reproductive traits, lifespan and bodyweight using the R package ppcor [9]. To visualize the pattern of correlations among all traits, a minimum- Bayesian information criterion (BIC) graph were generated using the R package gRapHD [10]. Ancestral states obtained as mentioned above were used to create the trait coevolution network. We used igraph in R to draw the network [11]. We analyzed the coevolution between diet and seasonal breeding using the reversible- jump Markov Chain Monte Carlo model implanted in BayesTraits (V3.0.2) [12].

3. Results and Discussion 3.1. Correlations among Traits In this work, besides lifespan and bodyweight, we collected five new reproduction- related traits including gestation period, weaning period, time to independence, litter size, and female sexual maturity age, all of which are continuous traits. We extended our rate-based ancestral prediction approach to continuous traits and reconstructed ancestral states of all five reproduction-related traits as well as lifespan and bodyweight. How life history traits are correlated with each other is an area under active investiga- tion and is one of the key questions for understanding mammalian evolution. Besides the seven continuous traits mentioned above, we analyzed 10 discrete traits including activity pattern (diurnal vs. nocturnal), sociality (social vs. solitary), diet, etc., in our previous work [7]. We calculated correlations among all 17 traits, and the correlations among re- productive life history traits are summarized in Table S3. As the ancestral states of each trait are also used to calculate the correlations, the result is less biased due to phylogenetic inertia. Average gestation period is correlated with both lifespan and bodyweight. For lifespan, the correlation is as high as 0.726, and the correlation is 0.603 for bodyweight. Genes 2021, 12, 740 4 of 10

As well as average gestation period, time to independence, weaning period, and female sexual maturity age are also correlated with lifespan, with values of 0.418, 0.518, and 0.485, respectively. The correlations of these reproduction-related traits with bodyweight show the same tendency (Table S3). Since bigger are usually longer-lived, it is to be expected that they also show longer gestation and weaning periods. Litter size is negatively correlated with lifespan and bodyweight, with correlation values of −0.446 and −0.227, respectively. Notably, the correlations of these traits with lifespan are all larger than they are with bodyweight. Compared with bodyweight, lifespan is thus a more important factor for the respective traits.

3.2. Trait Coevolution Network Reproductive life history traits influence each other. The pairwise correlation shown in Table S4 is possible due to an indirect correlation of traits with the third factors. Considering this, we further calculated partial correlation networks among traits. The edges shown in the figure are selected on minimum Bayesian information criterion scores (BIC), which indicate the coevolution relationships among traits. We find that gestation period is most strongly connected to lifespan, with a positive partial correlation of 0.514. Time to independence is positively correlated with female sexual maturity age and weaning period, with partial correlation values of 0.411 and 0.325, respectively. A later time to independence of the young may increase the age of sexual maturity. Litter size is negatively correlated with gestation period and arboreality, with partial correlation values of −0.251 and −0.210, respectively. Longer gestation period and weaning period indicate heavier female investment in the offspring. When becomes denser, litter size may become smaller, due to the trade-off in which reproductive investment constrains litter size [3]. Arboreal mammals generally have smaller litter sizes because it is difficult to carry many new babies after [13]. Diurnal mammals often have a longer weaning period than nocturnal mammals (correlation = 0.351), while monogamous mammals often have a shorter one (correlation = −0.172). There is also no direct relation between lifespan and bodyweight; their correlation is due to both factors being impacted by gestation period (correlation = 0.514 and 0.377, respectively). Male- biased (male-larger in Figure1) is positively correlated with lifespan and weaning age, with correlation coefficients of 0.163 and 0.199, respectively. We did not find a direct correlation between the male-larger and monogamous traits. Seasonal breeding is positively correlated to omnivory (correlation = 0.169). The relation between diet change and reproductive seasonality is discussed in a later section as well. These finding provide new insights into how life history traits coevolve with each other.

3.3. Evolutionary History of Parental Investment in Mammals Except for , viviparity is an important characteristic of mammals. After giving birth, female mammals spend time feeding milk to and rearing their offspring. For most species, females mainly contribute to the care of offspring, although male care also occurs in some cases. Life history traits that are related to parental investment in mammals are gestation period, weaning period, time to independence, and litter size. The length of the gestation period and weaning period mainly indicates how much the female contributes to the care of offspring, while time to independence and litter size are also impacted by male care [6]. As mentioned above, all of these traits are impacted to some extent by other life history traits, such as lifespan, bodyweight, and social behavior. Genes 2021, 12, x FOR PEER REVIEW 4 of 5

Genes 2021, 12, 740 5 of 10

Figure 1. Coevolution network of life history traits. Based on the partial correlations of traits, we Figure 1. Coevolution network of life history traits. Based on the partial correlations of traits, we inferredinferred a trait–coevolution a trait–coevolution network. network. The edges The shown edges in shown the figure in the are figure selected are on selected minimum on minimum BayesianBayesian information information criterion criterion scores scores (BIC). (BIC). Ancestral Ancestral states statesof the oftraits the are traits also are used also to used infer to the infer the correlationcorrelation network. network. Numbers Numbers along along edges edges are part areial partial correlation correlation coefficients coefficients among among traits, traits, with with the the relativerelative strength strength of of the the correlation correlation indicated indicated by the thickness ofof thethe edgeedge andand whetherwhether thethe correlationcor- relationis positive is positive or negative or negative indicated indicated by red–greenby red–green color color gradients. gradients.

3.3. EvolutionaryFigure2 History shows of the Parental evolutionary Investment history in Mammals of mother care (gestation period + weaning period),Except for female monotremes, sexual maturity viviparity age, is andan important litter size. characteristic The common of ancestormammals. of After placental givingmammals birth, female had amammals relatively spend short time maternal feeding care milk period, to and perhaps rearing due their to offspring. its short lifespan. For mostHowever, species, females before the mainly –Paleogene contribute to the (K–Pg)care of boundary,offspring, thealthough average male time care for maternalalso care in ancestral mammals became longer. These ancestors, including the common ances- occurs in some cases. Life history traits that are related to parental investment in mammals tors of , , Euungulata, Ferae, etc., show a prolonged duration of are gestation period, weaning period, time to independence, and litter size. The length of maternal care compared with the common ancestor of mammals. The results of time for the gestation period and weaning period mainly indicates how much the female contrib- female sexual maturity show a similar pattern, with some lineages displaying a prolonged utes to the care of offspring, while time to independence and litter size are also impacted time for female sexual maturity just before the K–Pg boundary. On the other hand, litter by male care [6]. As mentioned above, all of these traits are impacted to some extent by size shows a different pattern, namely that the ancestors of mammals generally show a other life history traits, such as lifespan, bodyweight, and social behavior. small litter size compared with modern mammals. Collectively, these results indicate that Figure 2 shows the evolutionary history of mother care (gestation period + weaning mammals mainly use the K-strategy, which means a smaller litter size and more intensive period), female sexual maturity age, and litter size. The common ancestor of placental care of offspring, to reproduce the next generation. mammals had a relatively short maternal care period, perhaps due to its short lifespan. However,3.4. Coevolution before the between Cretaceous–Paleogene Diet and Seasonal (K–Pg) Breeding boundary, the average time for mater- nal care in ancestral mammals became longer. These ancestors, including the common Reproduction of the next generation needs tremendous effort, and the requirement ancestorsfor food of Euarchonta, is the most importantPrimatomorpha, factor. Euun For mostgulata, of Ferae, the species, etc., show the likelihood a prolonged of success-du- rationfully of maternal raising the care young compared is highest with during the common the season ancestor when of foodmammals. is most The abundant. results of Food timeavailability for female sexual is considered maturity one show of the a simi mostlar important pattern, with factors some for lineages seasonal displaying breeding behav- a prolongedior [14 ].time Compared for female with sexual omnivorous maturity mammals,just before constraintsthe K–Pg boundary. on the availability On the other of food hand,are litter more size severe shows for a different carnivorous pattern, and herbivorousnamely that mammals.the ancestors Since of mammals omnivorous generally mammals showhave a small a wider litter selectionsize compared of food with that modern is less likelymammals. to be Collectively, constrained these by season, results we indi- expect cate thatthat omnivorousmammals mainly mammals use the are K-strategy more likely, which to reproduce means a year-roundsmaller litter than size and more and intensiveherbivores. care of The offspring, correlation to reproduce betweenreproductive the next generation. seasonality and omnivory is 0.237, which is much higher than that between reproductive seasonality and both carnivory (−0.181) and herbivory (−0.008).

Genes 2021, 12, x FORGenes PEER2021 ,REVIEW12, 740 4 of 5 6 of 10

Figure 2. EvolutionaryFigure 2. Evolutionary histories of histories mother careof mother time, femalecare time, age fe atmale sexual age maturity, at sexual andmaturity, litter sizeand reconstructedlitter size by rate- based prediction.reconstructed Nodalcircle by rate-based sizes are pred proportionaliction. Nodal to the circle magnitude sizes are of proportional each quantitative to the trait.magnitude Absolute of values of the ancestral traitseach are quantitative shown in Supplementary trait. Absolute Tablevalues S2. of Branchthe ancestral lengths traits are proportionalare shown in to Supplementary divergence , Table as estimated in our previousS2. study Branch (Wu lengths et al., 2017),are proportional and abbreviations to divergence of the geological times, as estima epochsted are in shown our previous at the top study of tree (Wu (Pa: et , E: Eocene, O:al. Oligocene, 2017), and M: abbreviations Miocene, Pli: of Pliocene, the geological Ple: Pleistocene). epochs are shown Gray vertical at the top lines of indicate tree (Pa: the Paleocene, Cretaceous–Paleogene E: Eocene, O: Oligocene, M: Miocene, Pli: Pliocene, Ple: Pleistocene). Gray vertical lines indicate the boundary (66 Ma) and the Eocene–Oligocene boundary (33 Ma). Cretaceous–Paleogene boundary (66 Ma) and the Eocene–Oligocene boundary (33 Ma). We further tested the relationships between changing diet and the change between 3.4. Coevolutionyear-round between Diet (1) and and Seasonal seasonal Breeding (0) breeding of 89 mammals by the reversible-jump Markov ReproductionChain of Montethe next Carlo generation model implantedneeds tremendous in BayesTraits effort, (V3.0.2)and the (Figurerequirement3)[ 12 ]. The results for food is the mostare summarized important factor. in Figure For 3most. The of change the species, in diet the and likelihood year-round of successfully breeding (1) to seasonal raising the youngbreeding is highest (0) occur during at thethe sameseason rate when for most food pairs,is most especially abundant. for Food ; availa- however, there bility is consideredare two one cases of the that most show importan a differentt factors pattern. for seasonal If the ancestors breeding are behavior year-round [14]. breeders, it is Compared withdifficult omnivorous to change mammals, their diet constraints to a carnivorous on the availability one; similarly, of food if the are ancestors more are seasonal severe for carnivorousbreeders, and it is alsoherbivorous difficult toma changemmals. their Since diet omnivorous to an omnivorous mammals one. have This a result indicates wider selectionthat of food the availability that is less oflikely food to is be a prerequisiteconstrained forby seasonalseason, we breeding expect behaviorthat om- but that, after nivorous mammalsthe species are more has becomelikely to fixedreproduce into a year-round certain breeding than carnivores type, either and seasonal herbi- or year-round, vores. The correlationseasonal between breeding reproductive behavior may seasonality constrain theirand omnivory diet changes is 0.237, as well. which is much higher than that between reproductive seasonality and both carnivory (−0.181) and herbivory (−0.008). We further tested the relationships between changing diet and the change between year-round (1) and seasonal (0) breeding of 89 mammals by the reversible-jump Markov

Genes 2021, 12, x FOR PEER REVIEW 4 of 5

Chain Monte Carlo model implanted in BayesTraits (V3.0.2) (Figure 3) [12]. The results are summarized in Figure 3. The change in diet and year-round breeding (1) to seasonal breeding (0) occur at the same rate for most pairs, especially for herbivores; however, there are two cases that show a different pattern. If the ancestors are year-round breeders, it is difficult to change their diet to a carnivorous one; similarly, if the ancestors are seasonal breeders, it is also difficult to change their diet to an omnivorous one. This result indicates Genes 2021, 12, 740 that the availability of food is a prerequisite for seasonal breeding behavior but that, after 7 of 10 the species has become fixed into a certain breeding type, either seasonal or year-round, seasonal breeding behavior may constrain their diet changes as well.

Figure 3. Coevolution between seasonal/yearly breeding and diet in mammals. Transitions Figure 3. Coevolution between seasonal/yearly breeding and diet in mammals. Transitions can can occur between four states: yearly reproduction and carnivory (left upper)/herbivory (left occur between four states: yearly reproduction and carnivory (left upper)/herbivory (left lower)/omnivory (right), seasonal reproduction and carnivory/herbivory/omnivory, yearly re- lower)/omnivory (right), seasonal reproduction and carnivory/herbivory/omnivory, yearly repro- ductionproduction and non-carnivory/non-herbivory/non-om and non-carnivory/non-herbivory/non-omnivory,nivory, and seasonal and reproduction seasonal reproduction and non- and non- carnivory/non-herbivory/non-omnivory.carnivory/non-herbivory/non-omnivory. Arrows Arrows indicating indicating transitions transitions between between states are states scaled are scaled to to representrepresent the theprobability probability of a of transition: a transition: line line thicknesses thicknesses are are pr proportionaloportional to to transition transition rates. rates. Absolute Absolutevalues values of transitionof transition rates rates (per (per million million ) years) are are indicated indicated next next to each to each arrow. arrow.

3.5. Genes3.5. GenesRelated Related to Reproductive to Reproductive Traits Traits The rate-basedThe rate-based ancestral ancestral reconstruction reconstruction approach approach regresses regresses transformations transformations of traits of traits to fluctuations in the molecular evolutionary rates of genes. We used LASSO penalized to fluctuations in the molecular evolutionary rates of genes. We used LASSO penalized regression to conduct the analysis; hence, genes that are not significant in predicting a regression to conduct the analysis; hence, genes that are not significant in predicting a trait trait have zero coefficients, leaving only genes that are significant in the prediction of trait have zero coefficients, leaving only genes that are significant in the prediction of trait val- values. Genes selected as predictors of the trait are likely related to the trait (Figure4 and ues. Genes selected as predictors of the trait are likely related to the trait (Figure 4 and Table S4). We found 23 genes as predictors of average weaning age. At least 11 tumor Table S4). We found 23 genes as predictors of average weaning age. At least 11 tumor suppressor genes were selected, and some are related to, or function directly as, tumor suppressor genes were selected, and some are related to, or function directly as, tumor suppressor genes in breast cancer (VCAN, CYLD, KLF10, CASS4, NUF2, DAAM2, PIP4K2A, suppressor genes in breast cancer (VCAN, CYLD, KLF10, CASS4, NUF2, DAAM2, PRKCD, etc.), mainly with a negative coefficient. VCAN, with the strongest coefficient of PIP4K2A, PRKCD, etc.), mainly with a negative coefficient. VCAN, with the strongest co- −0.484, is highly expressed in breast cancer progenitor cells [15]. Downregulation of CYLD efficient of −0.484, is highly expressed in breast cancer progenitor cells [15]. Downregula- (coefficient = −0.156) may promote breast cancer metastasis [16]; KLF10 (−0.151) is an tion ofanti-metastasis CYLD (coefficient gene that= −0.156) significantly may promote reduces breastbreast cancer cancer cell metastasis invasion [[16];17]; lowKLF10 PIP4K2B (−0.151)(− 0.047)is an anti-metastasis expression in gene that breast significantly tumors correlates reduces with breast reduced cancer patient cell invasion survival [18]. [17]; lowNKTR, PIP4K2B the natural (−0.047) killer expression cell-triggering in human receptor, breast istumors also selected. correlates Longer with reduced weaning age patientis survival highly likely [18]. toNKTR, be related the natural to strengthened killer cell-triggering functional constraints receptor, is on also tumor selected. suppressor Longergenes. weaning Genes age related is highly to averagelikely to time be related to independence to strengthened have multiplefunctional functions, constraints such as on tumorimmune suppressor system genes. genes Genes (NKTR, related coefficient to average = − 0.564),time to genesindependence related tohave autism multiple spectrum functions,disorders such (ERMN,as immune−0.226) system [19 ],genes and tumor (NKTR, suppressors coefficient (PIP4K2A, = −0.564),− genes0.059). related The gene to with autismthe spectrum highest coefficientdisorders (−(ERMN,0.714) is−0.226) IRAK3, [19], which and is associatedtumor suppressors with asthma (PIP4K2A, [20]. Twelve −0.059).genes The weregene selectedwith the tohighest be predictors coefficient of ( litter−0.714) size. is IRAK3, They function which is as associated a tumor suppressor with (PIP4K2A, 0.039) [18], interfere with neuronal development and are related to autism (CSDE1, −0.039) [21], or function as a transcriptional repressor (ZBTB1, 0.126) [22]. Thirty- nine genes were selected to be related to average gestation period, and 76 were related to female sexual or reproductive maturity age. For many of these genes, genome-wide association studies indicate a relationship with body weight, body height, or BMI-adjusted waist–hip ratio (Table S4). Notably, several genes were detected as predictors of several different traits, indicating the complex correlation structure and mechanisms of traits. Genes 2021, 12, x FOR PEER REVIEW 4 of 5

asthma [20]. Twelve genes were selected to be predictors of litter size. They function as a tumor suppressor (PIP4K2A, 0.039) [18], interfere with neuronal development and are re- lated to autism (CSDE1, −0.039) [21], or function as a transcriptional repressor (ZBTB1, 0.126) [22]. Thirty-nine genes were selected to be related to average gestation period, and 76 were related to female sexual or reproductive maturity age. For many of these genes, genome-wide association studies indicate a relationship with body weight, body height, or BMI-adjusted waist–hip ratio (Table S4). Notably, several genes were detected as pre- Genes 2021, 12, 740 8 of 10 dictors of several different traits, indicating the complex correlation structure and mech- anisms of traits.

FigureFigure 4. 4. CoefficientsCoefficients of genes selected asas predictorspredictors of of traits. traits. Genes Genes selected selected as as predictors predictors of of weaning weaningage, time age, to independence,time to independence, litter size, litter gestation size, ge period,station period, and female and female sexual maturitysexual maturity age are age listed, arewith listed, their with coefficients their coefficients shown as barshown plots. as bar plots.

3.6.3.6. Evolution Evolution of of the the Mammalian Mammalian Reproduction Reproduction Strategy Strategy AlthoughAlthough litter litter size size information information is is seldom seldom preserved preserved in in fossil fossil records records in in mammalian mammalian evolution,evolution, Hoffman andand RoweRowe (2018)(2018) [ 11[11]] reported reported a a stem stem mammalian mammalian fossil fossil (Kayentatherium (Kayentather-: iumMammaliaformes): ) from from the Early the Early Jurassic period period in which in which the clutch the comprisesclutch comprises at least 38at leastperinates, 38 perinates, which is which outside is theoutside range the of rang extante of mammalian extant mammalian species. On species. the other On hand,the other our hand,ancestral our stateancestral reconstruction state reconstruction of litter size of litter (Figure size2) (Figure indicated 2) indicated that litter that size litter in the size com- in themon common ancestor ancestor of of Theria ( (Eutheria + ) + Marsupial) in the in Early the CretaceousEarly Cretaceous period period was small was small(2.7 individuals (2.7 individuals per litter per). litter). This finding This findin suggestsg suggests that the that reproduction the reproduction strategy strategy of mam- of mammalsmals shifted shifted from from r-selection r-selection to K-selection to K-selectio beforen before the emergencethe emergence of Theria. of Theria. Interestingly, Interest- ingly,litter sizeslitter ofsizes Theria of Theria had generallyhad generally been b smalleen small through through the Cretaceousthe Cretaceous to the to Paleogenethe Paleo- geneperiods, periods, and and litter litter sizes sizes independently independently increased increased in in the the Neogene Neogene inin multiplemultiple lineages (Figure(Figure 22).). The The transformation transformation pattern ofof litterlitter sizesize asas inferredinferred from from our our rate-based rate-based analysis analy- sissuggests suggests that that K-strategists K-strategists served served as as the the source source of of the the diverse diverse reproduction reproduction strategies strategies in inmammalian mammalian evolution. evolution. Stockley Stockley and and Hobson Hobson (2016) (2016) [6 ][6] demonstrated demonstrated the the coevolution coevolution of ofpaternal care and and litter litter size. size. They They suggested suggested that that the the root root of mammals of mammals had had a small a small litter litter size sizewithout without male male provisioning provisioning and and that that the transitionthe transition probability probability from from “small “small litter litter size withsize male provisioning” to “large litter size with male provisioning” was higher than any other with male provisioning” to “large litter size with male provisioning” was higher than any state changes. Their proposal is largely harmonious with our ancestral state reconstruction. other state changes. Their proposal is largely harmonious with our ancestral state recon- The r-strategists are species that maximize reproductive capacity (r), generally reproduce struction. The r-strategists are species that maximize reproductive capacity (r), generally faster, and produce larger numbers of offspring. The r-strategists typically prosper in reproduce faster, and produce larger numbers of offspring. The r-strategists typically unstable environments, and conversely the K-strategists generally occupy more stable environments. However, the fitness of r/K-strategists through mass has not been well documented. Extant mammals experienced at least two mass events, at the Cretaceous–Paleogene boundary (66 Ma) and at the Eocene–Oligocene boundary (33 Ma)[7]. Considering that the K-strategists survived these two mass extinction events in our ancestral state reconstruction, it is possible that the fitness of mammalian K-strategists is higher in such periods. Botha-Brink et al. (2015) [23] analyzed growth mark data of stem mammals around the – boundary, known as the greatest mass extinction in ’s history, and demonstrated that late-breeding strategists generally became extinct whereas lineages that shifted to an early breeding strategy survived this mass . Genes 2021, 12, 740 9 of 10

Late-breeding strategists re-emerged several million years later, in the . Our ancestral state reconstruction on the sexual maturity age (Figure2) also shows a compatible tendency. The mammalian species were generally late-breeding strategists, and they shifted to the early breeding strategy after the Cretaceous–Paleogene boundary. From these findings, it is plausible that K-selected species with the early breeding strategy have the highest fitness to survive mass extinction events during mammalian evolution. The modern families of extant mammals mostly emerged after the event in the Oligocene period (33–23 Ma) [24,25]. Although it is possible that r-selected mammalian species repeatedly emerged in the Paleogene period (66–23 Ma), their lineages appear to have gone extinct or failed to shift their reproduction strategy. Mammalian r-strategists as seen in the Rodentia and might have evolved new lineages that were adapted to the blank after the mass extinction caused by the global cooling event in the Oligocene period.

Supplementary Materials: The following are available online at https://www.mdpi.com/article/10 .3390/genes12050740/s1, Table S1. The reproductive traits, lifespan, and bodyweight of 89 . Table S2. Ancestral states of seven traits by the rate-based prediction approach. Table S3. The pairwise correlations of traits. Table S4. The pairwise partial correlations of traits. Table S5. Coefficients of genes that are selected as predictors of each trait. Author Contributions: J.W., T.Y. and H.K. designed the research. J.W. and H.K. considered the method and performed the data analysis. J.W., T.Y. and H.K. wrote the paper. All authors have read and agreed to the published version of the manuscript. Funding: This study was supported by a Grant-in-Aid for Scientific Research (B) (19H04070) and a Grant-in-Aid for JSPS Research Fellows (18F18385) from the Japan Society for the Promotion of Science. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Correspondences and requests for materials should be addressed to [email protected]. Acknowledgments: We sincerely thank Ian Smith for improving this manuscript. Conflicts of Interest: The authors declare no competing interests.

References 1. Bronson, F.H. Mammalian Reproductive Biology; University of Chicago Press: London, UK, 1989. 2. Speakman, J.R. The physiological costs of reproduction in small mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2008, 363, 375–398. [CrossRef] 3. Williams, G.C. , the costs of reproduction and a refinement of Lack’s principle. Am. Nat. 1966, 100, 687–690. [CrossRef] 4. Lack, D. The significance of clutch size. IBIS 1947, 89, 302–352. [CrossRef] 5. Werner, J.; Griebeler, E.M. Reproductive biology and its impact on body size: Comparative analysis of mammalian, avian and dinosaurian reproduction. PLoS ONE 2011, 6, e28442. [CrossRef] 6. Stockley, P.; Hobson, L. Paternal care and litter size coevolution in mammals. Proc. Biol. Sci. 2016, 283, 20160140. [CrossRef] [PubMed] 7. Wu, J.; Yonezawa, T.; Kishino, H. Rates of Molecular Evolution Suggest of Life History Traits and a Post-K-Pg Nocturnal Bottleneck of Placentals. Curr. Biol. 2017, 27, 3025–3033.e3025. [CrossRef] 8. Kowalczyk, A.; Meyer, W.K.; Partha, R.; Mao, W.; Clark, N.L.; Chikina, M. RERconverge: An R package for associating evolutionary rates with convergent traits. 2019, 35, 4815–4817. [CrossRef][PubMed] 9. Kim, S. ppcor: An R Package for a Fast Calculation to Semi-partial Correlation Coefficients. Commun. Stat. Appl. Methods 2015, 22, 665–674. [CrossRef][PubMed] 10. Abreu, G.C.G.; Edwards, D.; Labouriau, R. High-Dimensional Graphical Model Search with the gRapHD R Package. J. Stat. Softw. 2010, 37, 1–18. [CrossRef] 11. Ju, W.; Li, J.; Yu, W.; Zhang, R. iGraph: An incremental data processing system for dynamic graph. Front. Comput. Sci. 2016, 10, 462–476. [CrossRef] Genes 2021, 12, 740 10 of 10

12. Pagel, M.; Meade, A. Bayesian analysis of correlated evolution of discrete characters by reversible-jump Markov chain Monte Carlo. Am. Nat. 2006, 167, 808–825. [CrossRef] 13. Sikes, R.S.; Ylönen, H. Considerations of optimal litter size in mammals. Oikos 1998, 83, 452–465. [CrossRef] 14. Bronson, F.H. change and seasonal reproduction in mammals. Philos. Trans. R. Soc. Lond. B Biol. Sci. 2009, 364, 3331–3340. [CrossRef][PubMed] 15. Du, W.W.; Fang, L.; Yang, X.; Sheng, W.; Yang, B.L.; Seth, A.; Zhang, Y.; Yang, B.B.; Yee, A.J. The role of versican in modulating breast cancer cell self-renewal. Mol. Cancer Res. 2013, 11, 443–455. [CrossRef][PubMed] 16. Hayashi, M.; Jono, H.; Shinriki, S.; Nakamura, T.; Guo, J.; Sueta, A.; Tomiguchi, M.; Fujiwara, S.; Yamamoto-Ibusuki, M.; Murakami, K.; et al. Clinical significance of CYLD downregulation in breast cancer. Breast Cancer Res. Treat. 2014, 143, 447–457. [CrossRef][PubMed] 17. Memon, A.; Lee, W.K. KLF10 as a Tumor Suppressor Gene and Its TGF-beta Signaling. Cancers 2018, 10, 161. [CrossRef] 18. Keune, W.J.; Sims, A.H.; Jones, D.R.; Bultsma, Y.; Lynch, J.T.; Jirstrom, K.; Landberg, G.; Divecha, N. Low PIP4K2B expression in human breast tumors correlates with reduced patient survival: A role for PIP4K2B in the regulation of E-cadherin expression. Cancer Res. 2013, 73, 6913–6925. [CrossRef][PubMed] 19. Homs, A.; Codina-Sola, M.; Rodriguez-Santiago, B.; Villanueva, C.M.; Monk, D.; Cusco, I.; Perez-Jurado, L.A. Genetic and epigenetic methylation defects and implication of the ERMN gene in autism spectrum disorders. Transl. Psychiatry 2016, 6, e855. [CrossRef][PubMed] 20. Pino-Yanes, M.; Sanchez-Machin, I.; Cumplido, J.; Figueroa, J.; Torres-Galvan, M.J.; Gonzalez, R.; Corrales, A.; Acosta-Fernandez, O.; Garcia-Robaina, J.C.; Carrillo, T.; et al. IL-1 receptor-associated kinase 3 gene (IRAK3) variants associate with asthma in a replication study in the Spanish population. J. Allergy Clin. Immunol. 2012, 129, 573–575.e1-10. [CrossRef] 21. Guo, H.; Li, Y.; Shen, L.; Wang, T.; Jia, X.; Liu, L.; Xu, T.; Ou, M.; Hoekzema, K.; Wu, H.; et al. Disruptive variants of CSDE1 associate with autism and interfere with neuronal development and synaptic transmission. Sci. Adv. 2019, 5, eaax2166. [CrossRef] [PubMed] 22. Matic, I.; Schimmel, J.; Hendriks, I.A.; van Santen, M.A.; van de Rijke, F.; van Dam, H.; Gnad, F.; Mann, M.; Vertegaal, A.C. Site- specific identification of SUMO-2 targets in cells reveals an inverted SUMOylation motif and a hydrophobic cluster SUMOylation motif. Mol. Cell 2010, 39, 641–652. [CrossRef][PubMed] 23. Botha-Brink, J.; Codron, D.; Huttenlocker, A.K.; Angielczyk, K.D.; Ruta, M. Breeding Young as a Survival Strategy during Earth’s Greatest Mass Extinction. Sci. Rep. 2016, 6, 24053. [CrossRef][PubMed] 24. Benton, M.J. The Fossil Record 2; Springer: Berlin/Heidelberg, Germany; Chapman & Hall: London, UK, 1993. 25. Zachos, J.; Pagani, M.; Sloan, L.; Thomas, E.; Billups, K. Trends, rhythms, and aberrations in global climate 65 Ma to present. Science 2001, 292, 686–693. [CrossRef]