Briefly, we mapped filtered reads from each individual to the chromosome-level reference genome of R. SAMtools Version 1. We used BCFtools Version 1. Second, based on SNP datasets we calculated the Weir and Cockerham estimator of Fst [ 48 ] between the two himalayanus groups using Vcftools v0.
Because our main aim here is to estimate the general level of genetic differentiation between the two himalayanus groups, we applied a nonoverlapping window of kb, and only windows with the minimum of 10 SNPs were used to calculate the weighted Fst values. Third, to further test for the genome similarity of the two himalayanus groups, we examined sequence differences at nuclear-encoded OXPHOS genes because their proteins show direct interactions with mitochondrial encoded proteins.
We generated sequences of 68 OXPHOS genes [ 50 ]from each of the 14 individuals 10 himalayanus and 4 macrurus see details in Additional file 3 : Supporting information and Additional file 9 : Table S6. These sequences were then aligned using MEGA 6 and amino acid changes were calculated across the 14 individuals. We previously generated a complete mitogenome for Him GenBank accession: MT [ 51 ], belonging to matched group.
To compare sequence differences of the complete mitogenome between mismatched and matched groups, we generated the complete mitogenome for Him mismatched group using similar procedures as in Ding et al. Nucleotide differences between Him and Him were calculated for 37 mitochondrial genes and control region using MEGA. To investigate whether sequence differences between Him and Him were fixed in each group, we generated the 13 mitochondrial protein-coding genes PCGs for the remaining eight himalayanus individuals and four macrurus individuals see details in Additional file 3 : Supporting information.
SNPs were generated using the mitogenome of Him as the reference. For the sliding-window Fst analysis, we used a nonoverlapping window size of 1 Kb. For each tissue, filtered reads from each sample were mapped to the reference genome of R. Mapped reads were quantified using Featurecount [ 53 ]. We removed those lowly expressed transcripts with less than one CPM counts per million across samples. Read count matrices across samples were normalized in DESeq2 [ 54 ].
Prior to differential expression analysis DE , we checked for potential outliers in each tissue using PcaGrid method [ 55 ] implemented in the rrcov R package with default parameters. Out of the 59 samples, one brain sample, two liver samples, one cochlea sample, and one intestine sample were identified as significant outliers and therefore were not used in DE analysis.
Samples were renormalized after removal of outliers in each tissue. We identified differentially expressed genes DEGs between the two R. We found a total of nucleotide differences 2. This overall high genetic differentiation across the whole mitogenome was also shown by a sliding-window analysis of Fst between the two groups Additional file 2 : Figure S2d.
Among the differences observed, 17 were non-synonymous, giving rise to 16 amino acid changes in mitochondrial protein-coding genes PCGs Additional file 5 : Table S2. We observed 14 fixed amino acid changes between matched and mismatched groups Additional file 6 : Table S3.
Thirteen of these were shared between mismatched group and macrurus , indicating the occurrence of introgression from macrurus to himalayanus. Lastly, we did not detect fixed amino acid changes between the two himalayanus groups at 68 nuclear-encoded OXPHOS genes. Instead, we found eight fixed amino acid changes between himalayanus and macrurus occurring at seven genes Additional file 7 : Table S4.
To investigate transcriptional response to mitonuclear mismatch in natural populations, we examine changes of gene expression between mitonuclear mismatched and matched groups see Fig. Overall, we identified a small number of differentially expressed genes DEGs with a total of 60 DEGs across all six tissues.
No DEGs were common to all six tissues. Volcano plots showing overall gene expression in each tissue between matched and mismatched groups with expression fold changes and adjusted P values for each gene. Differentially expressed genes that were discussed in the text were shown with their names.
After reducing redundancy, we obtained 27 GO terms and most of them are related to immune response including cell killing, positive regulation of cytokine production, lymphocyte activation, leukocyte proliferation, and interferon-gamma production Fig. We also found terms associated with actin-myosin filament sliding and calcium ion transport Fig. In contrast to muscle, we did not find any significant GO terms among DEGs identified in the other five tissues examined.
Multidimensional scaling plot showing shared GO terms enriched for differentially expressed genes identified in muscle. Clustering was conducted based on semantic similarity of GO terms. The colour and size of circles correspond to the q-value calculated by Metascape and the frequency of the GO term in GO annotation database, respectively.
Only the highly shared GO terms are shown with names. Mitochondrial function relies on epistatic interactions among mitochondrial and nuclear genes.
This mitonuclear interaction leads to co-adapted mitonuclear genotypes in isolated populations or lineages. In cases where allopatric populations show high levels of divergence in their mitogenomes, then mtDNA introgression on secondary contact can disrupt the co-adapted mitonuclear genotypes and lead to mitonuclear mismatches in hybrid populations [ 7 , 8 ]. Yet while introgression of mtDNA has been commonly documented [ 60 , 61 ], very few studies have examined the effects of this mtDNA introgression i.
In this study, we conducted, to our knowledge, the first assessment of the transcriptional effects of mismatched mitonuclear genotype within species across multiple adult tissues. We sampled one R. This introgression of mtDNA was clear in phylogenetic analyses and in our PCA Additional file 2 : Figure S2c , with several fixed amino acid changes at mitochondrial protein coding genes Additional file 6 : Table S3 observed among the two himalayanus groups and macrurus see also [ 35 , 37 ].
Next we examine the nuclear divergence between the two himalayanus groups. The results of the PCA and sliding-window Fst analysis supported the overall similarity across the nuclear genome between these two groups Additional file 2 : Figure S2c and Additional file 2 : Figure S2e and our recent study based on a wider range of sampling also indicated that no nuclear DNA introgression had occurred between the two subspecies following mtDNA introgression [ 37 ].
Currently, more work is needed to determine the exact evolutionary forces underlying the introgression of mtDNA, alongside little or no trace of introgression of ncDNA see a recent example in chipmunks [ 62 ]. One possible explanation is that the mitogenome confers an adaptive advantage e. However, while signatures of positive selection have been detected in the mitogenome of R.
Alternatively, the mtDNA introgression might simply be a signal that has been left behind following hybridization, whereas ncDNA introgression was eroded via recombination following backcrossing [ 60 ]. In the future whole-genome resequencing data from multiple individuals will be needed to assess the level of nuclear introgression between the two mainland subspecies. In addition, whole-genome data can also be used to test for other mechanisms, such as demographic effects and selection against hybrids [ 18 ].
Nevertheless, the current findings provide strong support that the sampled R. Previously, the effects of mitonuclear mismatch on nuclear gene expression have been studied in Drosophila from RNA-seq data obtained from the whole organism [ 27 ]. In contrast, we examined transcriptional variation across multiple tissue types, and found clear tissue-specific effects of mismatched mitonuclear genotype.
Specifically, mitonuclear mismatch showed a larger effect in pectoral muscle than in other tissues, with 43 DEGs identified in muscle but fewer than 10 in the other tissues, a pattern that is consistent with the fact that muscle usually requires more energy than other tissues for animals, which is likely to be especially true of volant species such as bats. An alternative explanation for why muscle shows greater number of DEGs than other tissues is that bulk RNA-seq, applied in this study, may have a larger effect on the identification of DEGs in heterogeneous tissues e.
In the future, the application of single-cell transcriptomics approaches [ 64 ] might be informative in testing this possibility. Although mtDNA from mismatched and matched groups shows only 14 fixed amino acid changes in protein-coding genes, our results demonstrate that this level of mtDNA difference still can likely have profound impacts on nuclear gene expression. In Drosophila , Mossman et al.
On the other hand, work on killifish revealed that only three nuclear genes were significantly differentially expressed between different mitochondrial genotypes in skeletal muscle [ 29 ].
However, because in this latter study the differences among mitochondrial genotypes e. Thus to draw general conclusions about the effects of different levels of divergent mitochondrial genotypes in natural populations, additional comparative transcriptomic studies of multiple tissues are needed. We identified several significantly upregulated genes in mismatched individuals, the majority of which are associated with protection against the oxidative damage putatively caused by inefficiency of the OXPHOS pathway in the mitochondria.
These genes may play essential roles for organisms in response to cellular stress in nature, and below we discuss some of these in each tissue specifically. Most differentially expressed genes DEGs in muscle 37 of 43 showed upregulation in the mismatched group Table 2. The loss of this gene has been linked to aberrations in the fragmentation of DNA molecules [ 65 ] and the formation of anti-DNA antibodies and autoimmunity in mice and human [ 66 ].
We also found very high upregulation 4x in the mismatched group of GPx3 glutathione peroxidase 3. The gene HSPB6 encoded small heat shock protein HSPB6 which is most highly expressed in different types of muscle [ 69 ] and can be induced by oxidative stress [ 70 ].
Our functional enrichment analyses of DEGs in muscle revealed that a majority of DEGs are related to immune response, with other important terms associated with muscle contraction and calcium ion transport. Another important GO term GO includes six DEG, of which CXCL10 showed almost fold higher expression in the mismatched group compared to matched group; this gene was previously shown to be important in mitochondrial dysfunction and cellular apoptosis [ 72 ].
Although DEGs from other tissues did not reveal any significant functional enrichment based on GO terms, some of these loci nevertheless have known roles in protecting against mitochondrial dysfunction. Specifically, among the two DEGs from heart that were upregulated in the mismatched group, ISG15 interferon-stimulated gene 15 encodes a protein that is strongly associated with antiviral immune response [ 73 ] as well as regulation of mitochondrial OXPHOS and mitophagy processes during viral infection [ 74 ].
Recently, ISG15 has also been shown to play key roles in genome stability as a sensor of the DNA damage response and its expression is closely related to pmediated cellular processes [ 75 , 76 ]. The second gene, IFI6 interferon alpha inducible protein 6 , is also one of the interferon-stimulated genes, and encodes a mitochondrial localized protein.
We also found upregulation in the brain of the mismatched group, with an almost four-fold change in CYP26A1 , which encodes cytochrome P, a protein that has been shown to suppress oxidative stress-mediated apoptosis [ 78 ]. One upregulated gene in the cochlea of the mismatched group, SLC40A1 , encodes an iron transporter and can regulate cell oxidative stresses [ 79 ]. Finally, in contrast to other tissues, almost all DEGs identified in the liver are downregulated in the mismatched group, although it is unclear whether these genes function in oxidative stress.
In our study system, we compared bats with near-identical nuclear genetic backgrounds but with contrasting histories of mtDNA introgression, and found significant and tissue-specific effects of mitonuclear mismatch on nuclear gene expression. Several genes upregulated in mismatched individuals encode proteins with known roles in responding to oxidative stress, making these potentially important candidates for future studies, including those of mitochondrial replacement therapy in human oocytes, a method used to treat mitochondrial diseases [ 82 ].
No parents survive into the next generation, so overcrowding can be the result only of offspring from matings that have already taken place during earlier processing of the current generation. Since each individual is the father in exactly one mating, and the mother in an average of one mating, and each mating has an average of one offspring, the population size should stay about the same from generation to generation.
Certain factors, however, could result in the population size shifting from the target size, which is addressed in the next phase. In a supplementary step, adjustment , the algorithm makes an effort to keep the population close to the initial target population size.
More rarely, if the population ends up above the target level, the algorithm randomly selects individuals for culling.
Given any two diploid individuals on the grid, we draw one haploid genome from each individual by randomly selecting one chromosome in each diploid pair they contain a process identical to constructing a haploid gamete for that individual.
The genetic distance as plotted in Figure 1 is then defined by counting the number of mismatched base pairs between these two haploid genomes and dividing by the total number of base pairs.
Due to the random assortment of chromosomes, this quantity is not necessarily the same every time it is computed for a given pair of individuals. A mismatch distribution reveals the shape of genetic diversity in a sample through pairwise comparisons of genetic differences, which are displayed in a frequency histogram [32] Figure 3 and Figure 4. The number of peaks on the histogram does not directly correspond to the number of genetically self-similar subpopulations. However, if clusters B and C also happened to be 0.
A peak typically represents a set of comparisons between two subpopulations defining the degree of divergence between the subpopulations. Scatterplots with geographic distance on the x -axis and genetic distance on the y -axis Figure 2 are commonly presented as a way to examine isolation-by-distance in data from spatial genetic surveys. It is expected that shorter dispersal distances will yield a steeper slope for this relationship, although the relationship may not be linear [46].
An effectively well-mixed population should show no relationship between geographic and genetic distances, because thorough mixing would randomize location with respect to genotype.
A genetic cluster plot Figure 5 is constructed by randomly selecting pairs of haploid genomes and computing their genetic distance, similarly to the mismatch distributions, then only retaining pairs that are distant below a certain level set by the user. When a sufficient number of pairs with low distances have been gathered, we build a nondirected graph that contains the individuals as nodes and edges representing genetic distances smaller than the threshold.
By analyzing this graph we can then identify regions of self-connected clusters. Since it is not computationally feasible to examine all pairs of individuals, the clustering might depend on the random selection of pairs of individuals i. We have found, however, that repeated application of our clustering algorithm with different random seeds leading to different pairs being examined leads to qualitatively identical results. On the other hand, reducing the distance threshold has the expected effect of connecting formerly disconnected genetic clusters.
The authors wish to thank Philip H. Goodman, director of the Brain Computation Laboratory at UNR, for giving them access to the lab's processor cluster for their numerical simulations. Conceived and designed the experiments GH RD. Performed the experiments RPD. Abstract A commonly held view in evolutionary biology is that speciation the emergence of genetically distinct and reproductively incompatible subpopulations is driven by external environmental constraints, such as localized barriers to dispersal or habitat-based variation in selection pressures.
Author Summary A commonly held view in evolutionary biology is that new species form in response to environmental factors, such as habitat differences or barriers to individual movements that sever a population. Funding: This work was conducted without funding. Introduction The most common framework for understanding the process of biological speciation is geographical. Results Overview of the Model We have implemented a generalized cellular automaton model of evolutionary processes, called EvoSpace.
Isolation-by-Distance Gene flow distances in the model resulted from a combination of factors: the dispersal of individual agents, the location of female mates, and the settlement of offspring. Download: PPT. Figure 2. Scatterplots of Genetic Distance versus Spatial Distance.
Evolutionary Dynamics To assess the pattern of genetic diversity in our model system, we measured the genetic difference between two randomly selected, haploid gametes, as though these gametes were about to merge in fertilization and produce more or less viable offspring. Spatial Self-Organization The mismatch distribution provided good insight into the existence of distinct, internally homogenous subpopulations, but it did not demonstrate whether clusters were spatially segregated on the lattice or show where they were located.
Figure 5. Discussion Spatially explicit computational models of evolution are a relatively recent development made possible by the rapid rise in the power of computing hardware, although the earliest studies date back to the s [21]. Sewall Wright [16] originally conceived of isolation-by-distance as a model of the evolutionary process in which there is complete continuity of distribution, but interbreeding is restricted to small distances by the occurrence of only short range means of dispersal.
Sexual Reproduction and Offspring Viability Each individual contains its personal genetic information as a diploid set of chromosomes. Figure 7. Computation of Generations The simulation begins at generation 0 with a population of genetically identical individuals. Genetic Distance Computation Given any two diploid individuals on the grid, we draw one haploid genome from each individual by randomly selecting one chromosome in each diploid pair they contain a process identical to constructing a haploid gamete for that individual.
Mismatch Distributions A mismatch distribution reveals the shape of genetic diversity in a sample through pairwise comparisons of genetic differences, which are displayed in a frequency histogram [32] Figure 3 and Figure 4. Isolation-by-Distance IBD Scatterplots Scatterplots with geographic distance on the x -axis and genetic distance on the y -axis Figure 2 are commonly presented as a way to examine isolation-by-distance in data from spatial genetic surveys.
Genetic Cluster Plots A genetic cluster plot Figure 5 is constructed by randomly selecting pairs of haploid genomes and computing their genetic distance, similarly to the mismatch distributions, then only retaining pairs that are distant below a certain level set by the user. Supporting Information. Video S1. Short dispersal distance with oubreeding depression. Video S2. Video S3. Acknowledgments The authors wish to thank Philip H.
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