Transcriptome, genetic editing, and microRNAdivergence substantiate sympatric speciationof blind mole rat, SpalaxKexin Lia,b,1,2, Liuyang Wangc,1, Binyamin A. Knisbacher1, Qinqin Xue,1, Erez Y. Levanond, Huihua Wanga,Milana Frenkel-Morgensternf, Satabdi Tagoref, Xiaodong Fangg, Lily Bazakd, Ilana Buchumenskid, Yang Zhaob,Mat�ej Lövyh, Xiangfeng Lii, Lijuan Hang, Zeev Frenkelb, Avigdor Beilesb, Yi Bin Caoj,2, Zhen Long Wangk,2,and Eviatar Nevob,2

aInstitute of Apicultural Research, Chinese Academy of Agricultural Sciences, Beijing 100093, China; bInstitute of Evolution, University of Haifa, Haifa3498838, Israel; cDepartment of Molecular Genetics and Microbiology, School of Medicine, Duke University, Durham, NC 27708; dThe Mina and EverardGoodman Faculty of Life Sciences, Bar-Ilan University, Ramat Gan 52900, Israel; eThe People’s Hospital of Qinghai Province, Xining 810007, China; fFaculty ofMedicine, Bar-Ilan University, Safed 13195, Israel; gBeijing Genomics Institute, BGI-Shenzhen, Shenzhen 518083, China; hDepartment of Zoology, Faculty ofScience, University of South Bohemia, 37005 Ceske Budejovice, Czech Republic; iUniversity of Chinese Academy of Sciences, Beijing 100049, China; jCollegeof Chemistry and Life Science, Zhejiang Normal University, Jinhua, Zhejiang 321004, China; and kSchool of Life Sciences, Zhengzhou University, Zhengzhou450001, Henan, China

Contributed by Eviatar Nevo, May 18, 2016 (sent for review April 1, 2016; reviewed by Sergey Gavrilets and Morris Soller)

Incipient sympatric speciation in blind mole rat, Spalax galili, inIsrael, caused by sharp ecological divergence of abutting chalk–basalt ecologies, has been proposed previously based on mito-chondrial and whole-genome nuclear DNA. Here, we present newevidence, including transcriptome, DNA editing, microRNA, andcodon usage, substantiating earlier evidence for adaptive diver-gence in the abutting chalk and basalt populations. Genetic diver-gence, based on the previous and new evidence, is ongoingdespite restricted gene flow between the two populations. Theprincipal component analysis, neighbor-joining tree, and geneticstructure analysis of the transcriptome clearly show the clustereddivergent two mole rat populations. Gene-expression level analy-sis indicates that the population transcriptome divergence is dis-played not only by soil divergence but also by sex. Gene ontologyenrichment of the differentially expressed genes from the two abut-ting soil populations highlights reproductive isolation. Alternativesplicing variation of the two abutting soil populations displays twodistinct splicing patterns. L-shaped FST distribution indicates that thetwo populations have undergone divergence with gene flow. Tran-scriptome divergent genes highlight neurogenetics and nutritioncharacterizing the chalk population, and energetics, metabolism,musculature, and sensory perception characterizing the abuttingbasalt population. Remarkably, microRNAs also display divergencebetween the two populations. The GC content is significantly higherin chalk than in basalt, and stress-response genes mostly prefernonoptimal codons. The multiple lines of evidence of ecological–genomic and genetic divergence highlight that natural selectionoverrules the gene flow between the two abutting populations,substantiating the sharp ecological chalk–basalt divergence drivingsympatric speciation.

natural selection | ecological adaptive speciation | DNA editing |microRNA regulation | nonoptimal codon usage

Sympatric speciation (SS)—that is, speciation in a free breedingpopulation—has been proposed by Darwin (1) but refuted by

Ernst Mayr, who accepted it later in life (2, 3). Four criteria arerequired for establishing SS: sympatry, reproductive isolation (RI),sister species, and no historical allopatry (4). However, whether allthese criteria could be detected in all of the emergent SS cases isstill unclear. Rising empirical (5) (SI Appendix, Suggested Reading)and theoretical studies (6, 7) demonstrated that SS may be commonin nature, although it is still being highly debated (4). Recently, wedemonstrated SS in the blind mole rat, Spalax galili, by both mito-chondrial genome (8) and by whole-genome resequencing (5),demonstrating divergence across the whole genome with ongoing

gene flow. The functions of selected genes adaptively met the di-vergent ecological stress very well, which suggests that natural se-lection overruled gene flow and drove ecological SS (5). Ecologicalspeciation predicts that (i) population pairs from abutting divergentecologies have greater RI than those from similar but distantecologies (9, 10); (ii) adaptive divergence will limit genetic exchangebetween populations (11, 12), expediting RI evolution. Gene ex-pression, both down- or up-regulation, and alternative splicing, playan important role in shaping the phenotype of organisms colonizinga new environment (13). Consequently, it will enhance the adaptivespectrum, affecting genetic traits promoting RI.DNA and RNA editing have the potential to accelerate

genome evolution (14). DNA editing by apolipoprotein B mRNA-editing enzymes, catalytic polypeptide-like (APOBECs), func-tions in various biological pathways in health and disease. Ingenome defense, it restricts retroelements by introducing dele-terious hypermutation into the retroelement DNA synthesized

Significance

Speciation is the basis of the origin of biodiversity in nature.Sympatric speciation (SS) is still a controversial model of the originof new species, since first proposed by Darwin in 1859. Here, wecomplement earlier genomic evidence with new analyses of tran-scriptome profiling, DNA editing, and microRNA, examined in theblind subterranean rodent, Spalax galili, in the Galilee Mountains,Israel, all substantiating SS with gene flow. Gene ontology en-richment of differentially expressed genes, in the abutting soilpopulations, highlights evolving reproductive isolation, despite afew interpopulation recombinants. Because sharply divergentgeological, edaphic, climatic, and biotic interfaces abound in na-ture, we conclude that SSmay be a commonmodel of the origin ofnew species, as envisaged by Darwin.

Author contributions: E.N. designed research; K.L., E.Y.L., and E.N. performed research;K.L., A.B., and Z.L.W. contributed new reagents/analytic tools; K.L., L.W., B.A.K., Q.X.,H.W., M.F.-M., S.T., X.F., L.B., I.B., Y.Z., M.L., X.L., L.H., Z.F., Y.B.C., and E.N. analyzed data;and K.L., L.W., B.A.K., M.F.-M., and E.N. wrote the paper.

Reviewers: S.G., University of Tennessee; and M.S., Hebrew University of Jerusalem.

The authors declare no conflict of interest.

Data deposition: The sequences reported in this paper have been deposited in the Gen-Bank database (accession nos. SRP075890 and SRP075930).1K.L., L.W., B.A.K., and Q.X. contributed equally to this work.2To whom correspondence may be addressed. Email: nevo@research.haifa.ac.il,likexin323@gmail.com, sky109@zjnu.edu.cn, or wzl@zzu.edu.cn.

This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.1073/pnas.1607497113/-/DCSupplemental.

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during reverse transcription (among other mechanisms). In somecases, DNA-edited elements successfully enter the genome despitebeing hypermutated, containing a series of G-to-A mutations (15,16). These sequences enhance genomic diversity and, therefore,increase the potential of developing new traits. A-to-I RNAediting by adenosine deaminases acting on RNAs (ADARs)enhances diversity through a different mechanism—it confersplasticity to the transcriptome by enabling a single mRNA iso-form to give rise to a multitude of protein variants (17).Here, we show SS in S. galili in upper Galilee, Israel, by tran-

scriptome divergence, DNA editing, microRNA regulation, and codonusage associated with the abutting but sharply divergent ecologicalenvironments, basalt and chalk, in the central eastern upper Galilee,Israel, complementing and reinforcing the genome divergence (5),thereby advancing our in-depth understanding of SS with gene flow.

ResultsTranscriptome Sequencing and Population Divergence Analyses. Thesubterranean blind mole rats, S. galili (SI Appendix, Table S1),were collected from the two abutting but sharply divergent rock,soil, and vegetation types (SI Appendix, Fig. S1) for the presenttranscriptome study in 2014. Kinship of pairwise samples esti-mation show that the individuals used in the present study werenot closely related (SI Appendix, Fig. S2). There are 30,928 SNPsshared by both basalt and abutting chalk mole rat populations intranscriptome, and 10,992 and 8,581 SNPs unique to basalt andchalk populations, respectively (SI Appendix, Fig. S3A). Phylo-genetic tree analysis, principal component analysis (PCA), and amaximum-likelihood analysis were performed using all of theSNPs called above, showing the genetic divergence in SS affect-ing the entire transcriptome as it showed earlier in whole-genomicadaptive patterns between the soil divergent populations (5). Inphylogenetic analysis, animals from the same soil population wereclustered together, and the two soil sister species, the chalk pro-genitor, and basalt derivative (5) were distinguished clearly byneighbor-joining (NJ) tree method (SI Appendix, Fig. S3B). InPCA, all of the animals were clustered into two distinct groups (SIAppendix, Fig. S3C) corresponding to the two sibling species, andthe first and second components explain 21.3% and 18.4% of thegenetic variants, respectively. The variance is by far higher in thechalk population, possibly reflecting the much larger distance be-tween territories and individuals, and the higher stress in the chalkecology (5, 8). In ancestral estimation analysis (SI Appendix, Fig.S3D), population structure was estimated based on the maximum-likelihood model with varying K values. When assuming K = 2, aclear division was observed between the 10 animals correspondingto the two soil populations, which reflect the sharp ecological di-vergence between chalk and basalt populations, as was alsoobtained in the genome analysis (5) (see geological and vegetationdivergence in SI Appendix, Fig. S1). When the K value was set to 3,each of the chalk and basalt populations was separated into twosubpopulations but shared one subpopulation (in green in SI Ap-pendix, Fig. S3D), and one animal (SCN_2) was identified as arecombinant, as was the case in the genome (5) and mitochondrialgenome studies (6). When the K value was increased to 4, therewas also one recombinant (SCN_1) with genetic background fromthe two populations, and two shared subpopulations (SI Appendix,Fig. S3D). So, altogether, we identified two recombinants among the10 animals analyzed. In addition, both of them have a larger pro-portion of basalt genetic background (SI Appendix, Fig. S3D).Cross-validation (CV) error was calculated for each corres-

ponding K value. The number of groups, in other words, the propervalue of K, exhibits the lowest CV error compared with other Kvalues. In the present study, K = 2 shows the lowest cross-validationvalue, and we conclude that the animals could be clustered into twodistinct soil populations (SI Appendix, Fig. S4). The distributionsof FST values are highly congruent between the genome and

transcriptome (Kolmogorow–Smirnov test, P = 0.002215), andboth of them appear as “L-shaped” (SI Appendix, Fig. S5).The allele number of SNPs, which is defined as the total

number of alleles in all individuals studied for that SNP at thatsite, was 1.60 and 1.58 in chalk and abutting basalt populations,respectively. It is significantly more in chalk than in basalt (P <0.00001). The heterozygosity of chalk and basalt populations are0.1660 ± 0.2260 and 0.1454 ± 0.2220, respectively, and it is sig-nificantly more in the chalk population than in basalt (P < 2.2e-16,Wilcoxon test).

Gene Expression Divergence Between the TwoMole Rat Soil Populations.A hierarchical cluster analysis was conducted based on the expres-sion level of 89,860 transcripts to estimate the contribution of sexand environments to gene expression. Principal variance componentanalysis was also performed to estimate the expression divergencebetween the two soil populations (Fig. 1A). The first and secondprincipal components (PCs) explain 55% and 17% expression var-iance, respectively. The largest PC1 separates the 10 animals by thetwo sexes, and the second PC divided the animals (without the tworecombinants with larger basaltic genetic background) into two soilchalk and basalt populations (Fig. 1A). The males, including SCN_7(SCN, animals from chalk), SCN_9 from the chalk population, andSBN_9 (SBN, animals from basalt) from the basalt population (SIAppendix, Table S1), were clustered into one group, whereas theseven females were clustered into another group (Fig. 1A). Anotherhierarchical cluster analysis of 1,600+ transcripts from eight samples(without the two recombinants of SCN_1 and SCN_2) was con-ducted and shown in heatmap (Fig. 1B). There is no differentiallyexpressed gene (DEG) between the chalk and basalt populationsif the two recombinants were not removed. Individuals from thesame soil could be clustered into their original soil clusters, al-though one individual from basalt (SBN_9) was not clear.Pre-mRNA alternative splicing (AS) plays an important role in

enhancing genomic diversity in eukaryotes. In addition, the geneticpolymorphism will help to cope with environmental stresses (18).AS analysis demonstrates that all of the animals from the chalkpopulation, where they suffered the same environmental stresses,are grouped into one cluster (Fig. 1C); however, animals from thebasalt population were divided to two subclusters (Fig. 1C).Genetic divergence in the face of gene flow was shown by the

distribution of FST. About 80% of the regions across the transcriptomedisplay FST < 0.25. However, there are a few regions showing bigdivergence with a value of FST > 0.5 or even close to 1 (Fig. 1D). Thegene expression divergence and population differentiation measuredby SNPs was weakly positively related (SI Appendix, Fig. S6). Theputatively selected genes were identified by using both FST andθπ (SI Appendix, Fig. S7). Interestingly, the gene OXNAD1 is re-lated to oxidoreductase activity in the basalt population.

Gene Ontology of the DEGs. Gene ontology (GO) enrichment wasperformed using the gene ontology (GO) database (19) and theknockout mouse phenotypes ontology using Mouse GenomeInformatics (MGI)–Mammalian Phenotype (MP) browser (20).DEGs of the two abutting chalk and basalt populations werecharacterized with knockout phenotypes affecting abnormal sexdetermination (MP0002210), of which the definition is ananomaly of primary or secondary sexual development or char-acteristics, abnormal male reproductive (MP0001145), which isdefined as any structural anomaly of the organs associated withproducing offspring in the gender that produces spermatozoa;abnormal sex gland (MP0000653), defined as any structural anomalyof any of the organized aggregations of cells that function as secre-tory or excretory organs and are associated with reproduction; andabnormal fertility/fecundity (MP0002161). Another two categoriesare related to morphology, including abnormal brain morphology(MP0002152) [defined as any structural anomaly of the brain, one ofthe two components of the central nervous system and the center of

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thought and emotion; controls coordination, bodily activities, and theinterpretation of information from the senses (sight, hearing, smell,etc.)] and mammalian phenotype (MP0000001). These geneknockout mice exhibit abnormal reproduction.The GO enrichment was performed on the 59 genes, which

were identified from the FST outlier analysis, separating thechalk and basalt abutting populations. We found similarity withthe genome results. These GO categories include energetics,neurogenetics, nutrition, senses, metabolism, hypoxia, and mus-culature (SI Appendix, Fig. S8).

DNA- and RNA-Editing Comparison Between Spalax Chalk and BasaltPopulations. Following the previous finding that DNA and RNAediting are elevated in S. galili (21), we were interested in exploringthe possibility that they could have a role in SS. To do so,we comprehensively screened the basalt and chalk genomes and

transcriptomes for DNA and RNA editing (SI Appendix, Meth-ods) and identified both differential DNA and RNA editing. Thescreen for DNA editing revealed a total of 16,386 and 20,347DNA-edited sites in basalt and chalk, respectively. Validity ofthe DNA-editing sites was derived from the presence of theexpected rodent APOBEC3-editing preference (G-to-A muta-tions in the GxA context) (16, 22) and by high ratios of G-to-Aover other mismatches (SI Appendix, Figs. S9 and S10). In-terestingly, most families (e.g., ERV1, ERVK) contained similarrates of DNA editing in chalk and basalt populations; however,ERVL had almost a fourfold increase in editing in the chalkgenome (4,140 edited sites; and only 1,168 in basalt; SI Appendix,Fig. S11). This coincides with the previous observations of in-creased genetic diversity in molecular markers (AFLP), mtDNA,and whole genome in the ancestral chalk population (21).

A B

C D

Fig. 1. Genetic and expression divergence in SS of S. galili. (A) PCA of gene-expression level of the 10 animals. Triangles denote males, and circles denote females.Animals from basalt were marked in red, and animals from chalk in blue. Animals marked by a white circle are the two recombinants. Note the separation of males andfemales; and the separation of chalk from basalt by a dashed line. (B) Hierarchical clustering analysis of differential expressed transcript (DET) between animals of thechalk and abutting basalt populations. (C) Hierarchical cluster analysis of alternative splicing variation of the 10 animals. All of the individuals from chalk were clusteredinto one group, and the animals from basalt were subdivided into two subclusters. Red bar denotes animals from basalt, and blue bar represent animals from chalk. (D)The L-shaped FST distribution of the chalk and abutting basalt populations across the whole transcriptome. SBN, animals from basalt; SCN, animals from chalk.

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To identify soil population-specific differences in RNA editing,two analyses were conducted—one of the major retroelementfamilies (B1, B2, and B4) and the other of sites in mRNA knownto be evolutionarily conserved RNA-editing targets in mammals(23) (SI Appendix). The former revealed that RNA editing issimilarly prevalent in both soil types (SI Appendix, Figs. S12–S18).However, the latter revealed two conserved sites with significantlyaltered editing levels between chalk and basalt populations(χ2; false-discovery rate < 0.05). One resides in transmembraneprotein 63B (TMEM63B) mRNA and was elevated in chalk(68.6% vs. 60.6% in basalt), whereas the other is in insulin-likegrowth factor binding protein 7 (IGFBP7) and was elevated inbasalt (78.8% vs. 73.6% in chalk) (SI Appendix, Fig. S19 and TableS2). Although these changes are not severe, it cannot be ruled outthat such changes have functional impact. The current findings donot directly link DNA or RNA editing to speciation; however, theediting sites identifiable with contemporary computational ap-proaches are only a small fraction of all editing sites. Thus, it is

possible that yet-unidentified novel editing events in Spalax con-tribute to their speciation (Fig. 2).

MicroRNA Divergence in Regulation of the Chalk and Abutting BasaltPopulations. MicroRNA plays main roles in responding to stressesthrough restoring or reprogramming gene expression patterns (24).MicroRNAwas isolated from the same total RNA that were used forthe transcriptome study above from mole rat populations of basaltand abutting chalk soils. Reads smaller than 18 bp or longer than30 bp were removed from the downstream analysis. The generateddata for each individual (BN6, BN9, CN1, and CN9) are 12.7M,11.0M, 9.8M, and 12.5M (SI Appendix, Tables S3 and S4), re-spectively. The length distribution of microRNA of each sample isshown in SI Appendix, Fig. S20. The first nucleotide bias andnucleotide bias at each site are shown in SI Appendix, Figs. S21and S22. The number of matured, and pre-microRNA, as well asthe number of kinds of noncoding RNA, and the number ofnoncoding microRNA are listed in SI Appendix, Tables S5 and S6.MicroRNA expression level was estimated with 32 microRNA up-regulated, and 28 down-regulated in the basalt population (Fig. 3Aand SI Appendix, Fig. S23). Target mRNA was predicted and GOenrichment of differentially expressed target genes was performed.Interestingly, these GO categories include response to stimulus(GO:0050896), and cellular response to stimulus (GO:0051716), pro-tein dephosphorylation (GO:0006470), G-protein–coupled receptoractivity (GO:0004930), and signaling receptor activity. We found614 microRNA shared by the two populations, and there are 30 and27 microRNA unique to basalt and chalk populations (Fig. 3B),respectively. Three out of 27 from the chalk, and 5 of 30 micro-RNAs from the basalt are known, and the others are novel ones.

Nonoptimal Codon Usage Preference in Spalax Stress-Response Genes.We have analyzed the codon preferences of the stress-responsegenes in the two sibling species, i.e., in the chalk and abutting basaltsoil populations. We found that stress response genes, identified inour previous paper (5), mostly prefer nonoptimal codons (SI Ap-pendix, Table S7). We observed a consistent trend for the non-optimal codon usage preferences for both chalk and basalt soilpopulations. However, some preferences are significantly over-represented for basalt populations. To verify the evolutionarymechanism of the preferences observed, we submitted all of thestress-response genes for the GC content analysis. Interestingly, wefound that chalk populations have significantly higher GC content

Fig. 2. DNA-editing divergence of the chalk and basalt populations in SS.

A B

Fig. 3. MicroRNA expression divergence of the chalk and abutting basalt populations. (A) Cluster analysis of differentially expressed microRNA in the chalkand basalt mole rat populations. (B) Venn diagram displaying the number of microRNA shared by the two mole rat populations, and unique to basalt andchalk populations, respectively.

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at the third position (SI Appendix, Table S8). We may speculatethat such GC content might be connected to the temperaturestress being higher in chalk than in basalt.

DiscussionPopulation Genetic Divergence Analyses. The two mole rat soilpopulations that differentiated sympatrically and ecologically,are abutting, and the macroclimate there is completely the same.As the animals are not from the same biological family, we canassume that the divergence of the two abutting chalk and basaltpopulations derive from other reasons than family relationship.All of the genetic analyses, NJ tree (SI Appendix, Fig. S3B), PCA(SI Appendix, Fig. S3C), and ancestry estimation (SI Appendix,Fig. S3D) structure, showed clear divergence between the twosoil mole rat populations. The chalk and abutting basalt populationswere clustered into their original soil types, respectively, whichprecisely follows the sharp ecological divergence into basalt andchalk ecologies (SI Appendix, Fig. S1) and the genetic divergencethat was estimated in the previous studies of the whole genome(5) and mitochondrial genome (8). In PCA (SI Appendix, Fig.S3C), the individuals from chalk were more scattered than thatfrom basalt, probably because of stronger environmental stress[less food resources and hotter temperature leading to fivefoldsparser population density in chalk than basalt (8)]. These di-vergent demographies (25) and ecologies, and higher environ-mental stresses in chalk than in basalt select for higher geneticdiversity, leading to larger and more distinct territories as wasshown by AFLP markers (26), mtDNA (8), and whole-genomedata (5). In ancestry estimation analysis (SI Appendix, Fig. S3D),there was a clear division between the two soil populations, as-suming the K value of 2, demonstrating genetic divergence of thechalk and abutting basalt populations. One recombinant wasrevealed by K = 3 (SCN_2) and a second recombinant by K = 4(SCN_1) (SI Appendix, Fig. S3D), respectively, suggesting on-going gene flow and hybridization between the two soil pop-ulations, conforming with the results of the mitochondrialgenome (8) and whole genome (5). Notably, natural hybrid zonesoccur also between the four species of mole rats in Israel thatevolved peripatrically (27–29).Recombinants were found in chalk, which complements the

genomic result suggesting that more animals migrate from basaltto chalk (5). The animals marked in blue (Fig. 1C) are mainlyfrom basalt. Even in the recombinant, there is a larger geneticproportion from basalt, suggesting larger gene flow from basalt tochalk. The cross-validation number was the smallest when K = 2(SI Appendix, Fig. S4), suggesting that the chalk and abuttingbasalt mole rat populations diverged into two populations in theprocess of SS (5, 8).Most of the genome regions display a relatively small di-

vergence without substantial divergent selection between thetwo abutting soil populations, which is in conformity with ourformer genome discovery (5). The small part of genome regionswith big divergence (large FST) is supposed to harbor genesunder divergent selection, and these genes may be related toreproductive isolation (30, 31). “L-shaped” FST distribution(Fig. 1D) was also predicted as a character of speciation withgene flow (32).

Expression Level Comparison Between the Two Soil Populations.SCN_1 and SCN_2 appear to be recombinants of the two sym-patrically originated mole rat populations (SI Appendix, Fig. S3D).This suggests the ongoing restricted gene flow between the twopopulations, and the occurrence of SS with gene flow (5). Thebasalt population is fivefold denser than the chalk population (8);hence, the pressure of migration is from basalt to chalk, and asexpected we found more recombinants in chalk (5, 8). The ex-pression divergence of the two sexes was explained by PC1 (55%variance), and the expression divergence between the two soil types

was explained by PC2 (17% variance). There are larger proportionsof basaltic genetic background in the two recombinants of SCN_1and SCN_2 (SI Appendix, Fig. S3D), which may influence the geneexpression level (25, 33) and the clustering of these two recombi-nant animals in the basalt group. This result suggests that envi-ronmental stress affects the gene expression level, which facilitatesSS with gene flow (13). There are no DEGs in the analysis with thetwo recombinants, and differences are present only when the tworecombinants are removed (Fig. 1B). We could conclude that theDEGs are mainly caused by different genetic background ratherthan due to transient plasticity.In the MGI database, the functions of all of the genes were

verified by gene knockout experiments. The DEGs between theabutting chalk and basalt populations were enriched to the on-tology categories from MGI database. Four of the ontologycategories were related to RI, suggesting a possible ongoing RIreinforcement between the two populations.Two DEGs, ZGLP1 and APLP2, were found between the

chalk and abutting basalt populations. ZGLP1 is required forovary or granulosa cell development in various species (34, 35),suggesting that this DEG possibly plays a role in RI, which willexpedite incipient SS; other DEGs are listed in Dataset S1.

MicroRNA Divergence of the Chalk and Basalt Populations. Stressescan harm the organisms by damaging the proteins, lipids, DNA,and mRNA (24). Much genetic evidence indicates that microRNAplays important roles in adjusting stress responses (36). The GOenrichment of the target genes of the differentially expressedmicroRNA between the chalk and abutting basalt shows the twopopulations are under different stresses. These responses mayrelate to regulation and adaptation of the mole rat populations.miR-184 is one microRNA unique to chalk population, and thismicroRNA plays an important role in neural stem cell prolifer-ation and differentiation (37), which meet our previous resultsthat chalk population is more developed in neurogenetics sys-tems (5). miR-615-5p is one of the microRNAs unique to basaltpopulation, and it plays an important role in suppressing cancer(38), which is congruent with the more hypoxic environmentalbasalt.

Conclusions and ProspectsIs SS a rare or a common model of the origin of new species innature? Our studies suggest that SS may be common in naturewherever ecological contrasts abut. The current transcriptome,DNA editing, microRNA, and codon usage in the abutting eco-logically divergent chalk–basalt populations of S. galili highlightincipient SS. Natural selection in contrasting ecologies predomi-nates over homogenizing gene flow at a microsite. This previouslyunidentified multiple-line evidence complements and deepensearlier mtDNA and genomic evidence that SS is ongoing despiterestricted gene flow and hybridization between the abutting soilpopulations. GO enrichment divergence of DEGs highlight re-productive isolation, and alternative splicing variations underlinecontrasting populations’ adaptive evolution. Many biologicalfunctions such as neurogenetics, nutrition, hypoxia, energetics,metabolism, musculature, and sensory perception are divergentbetween the two soil populations. MicroRNA regulation andcodon usage are also divergent between the evolving siblingspecies. All of these predict that SS may be a common speciationmodel because geological, edaphic, climatic, and biotic contrastsabound in nature.What next? A system approach to generating functional evo-

lutionary novelties seems highly recommended. The processes ofnonrandom mutations, epigenetics, genome and transcriptomerestructuring, and complex adaptive architecture, repetitive non-coding DNA dynamics and regulation, and natural genetic engi-neering (39) could be profitably explored in SS. The environmentalstresses and changes affecting the dynamic genome read–write

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involved in SS, under differential abutting stressful and contrastingecologies, is a fertile ground for probing evolutionary dynamics ofadaptation and speciation.

Materials and MethodsThe experiments on animals in this study followed the rules and guidelines ofthe Ethics Committee of University of Haifa and Zhejiang Normal University.Transcriptomes of animals from the two abutting soil S. galili populationswere sequenced by Illumina HiSeq 2500. NJ tree, PCA, and maximum-likeli-hood structure analyses were performed based on all of the SNPs. DEGsbetween the chalk and abutting basalt soil populations were identifiedand processed for GO enrichment. Heatmap and PCA were carried outon DEGs. The differences between the two abutting soil populations in

alternative splicing were shown in the heatmap. DNA and RNA editing wascompared between the two populations. MicroRNA differences between thetwo soil populations were displayed in both expression level and kinds ofmicroRNA. Full details of the materials and methods are described in SIAppendix, Methods.

ACKNOWLEDGMENTS. We thank Huabin Zhao and Wei Hong for theircontribution of genome sequence; and Shay Zur for his photo of thesampling site. The following foundations supported this work: the Agricul-tural Science and Technology Innovation Program (CAAS-ASTIP-2016-IAR),the Ancell–Teicher Research Foundation for Genetics and Molecular Evolu-tion, the National Natural Science Foundation of China (Grants 31371209and 31372193), the foundation of Henan Educational Committee (Grant13A180717), and the Czech Science Foundation (Project 14-31670P).

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