Genetic Diversity in Mitochondria

Genetic Diversity in Mitochondria

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As I was studying for my GRE exam, I was reading on Mitochondria. I then remembered that they undergo replication similar to bacteria. So I googled for info and found on Wikipedia that there is a " near-absence of genetic recombination in mitochondrial DNA"

So then, if there is a lack of genetic diversity, how can these mitochondria still be functional? If we assume they are affected by random mutations as DNA can be. Is it the case that they replicate infrequently enough to not suffer ill-effects of low diversity?

Your claim that "there is a lack of genetic diversity" in mitochondria is not correct. There is no connection between recombination and mutation rates per se. In fact, mitochondria have much higher mutation rates than nucleus and different types of mitochondrial DNA can even co-exist in one organism, a phenomenon called heteroplasmy (which is found at a frequency of ~90% in human).

But: there is a connection between mutation fixation rate and effective population size. Because mitochondria are normally inherited through maternal line, effective population size for their genome is smaller than that of the nuclear genome, and thus any mutations in the mitochondrial DNA reach fixation faster, but again this is counteracted by high mutation rates.

There is no problem with functionality: as always if something gets broken (and it is easy to break something in the mitochondrial genome, since genes are normally tightly packed in it) respective genotype is eliminated or reduced in frequency by natural selection. In the case of mitochondria this selection acts both upon the level of individual organisms (intra-individual somatic pool of mitochondria: positive and negative selection) and the level of populations (mostly purifying selection in humans). It is nevertheless true, that given the virtual lack of recombination, selection becomes the only force of purging deleterious mutations.

Population biology and genetic diversity of two adjacent shrimp ( Parapenaeopsis coromandelica ) populations exploited under different fishing pressures in the coastal waters of Sri Lanka

Parapenaeopsis coromandelica shrimp populations along the western coast of Sri Lanka have supported coastal trawling for the last hundreds of years. Two non-overlapping adjacent fishing grounds (Hendala and Negombo) sustain different fishing intensities. In order to obtain information on the population structure and genetic diversity of P. coromandelica in these two regions, differences in length–weight relationships, growth, spawning seasons, sex-ratios, gonadosomatic index, length at 50% maturity (L 50 ) and sequence variation of the mitochondrial cytochrome oxidase subunit I were examined. Significant differences in population biology and genetic diversity were revealed from the two fishing grounds. Samples of Hendala showed lower L 50 and genetic diversity which are considered as potential effects and symptoms of extensive selective harvesting. Further, the shrimps' behaviour seems to be triggering the separation through low mixing of individuals at the two fishing grounds resulting in significant divergence based on haplotype frequencies. Management of P. coromandelica should consider the revealed biological and genetic evidences on existence of two sub-populations/stocks together with a routine monitoring of genetic effects due to harvesting.

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Whole genome resequencing of Japanese wild silkworm

The pupa of a single individual of Japanese wild silkworm was used to extract genomic DNA, based on a standard protocol for genomic DNA extraction. We followed the manufacturer's instructions (Illumina) to prepare DNA library. Briefly, we made it by following the workflow on Illumina Genome Analyzer II system: cluster generation, template hybridization, isothermal amplification, linearization, blocking, and denaturization and hybridization of the sequencing primers. Then the Illumina base-calling pipeline (SolexaPipeline-0.3) was applied to get sequences from the fluorescent images. Short read data have been deposited in the NCBI Short Read Archive under the accession number SRA009886.

Public data availability

Through NCBI Short Read Archive with accession number SRA009208, we downloaded all of the 40 silkworm resequencing data (see Table ​ Table1), 1 ), including 29 domesticated silkworms and 11 Chinese wild ones, detailed information of which can be found elsewhere [7], the silkworm nuclear reference genome sequence was derived from the SilkDB [11] ( or From GenBank we obtained three mt genome sequences (accession numbers <"type":"entrez-nucleotide","attrs":<"text":"AB070264","term_id":"18640062","term_text":"AB070264">> AB070264, <"type":"entrez-nucleotide","attrs":<"text":"AY301620","term_id":"60391177","term_text":"AY301620">> AY301620, and <"type":"entrez-nucleotide","attrs":<"text":"NC_003395","term_id":"18644896","term_text":"NC_003395">> NC_003395) and their annotations, which were serving as references. We used these reference genomes in all of our consensus assembly below.

Table 1

The detailed information of samples and sequencing summary

Sample IDName of strainSystem or locationEffective Depth (X)Genome coverage (%)DifferenceRate (%)
D7Lao IILaos12.8897.421.32
D8India M3India51.299.220.44
D10Italy16Italy, Europe24.1299.460.35
D11Soviet Union NO.1Former SU, Europe10.9894.750.41
D15Mutation M2Mutation22.0399.621.32
D16A06EGuangdong province, China75.0499.711.52
D17DamaoSichuan province, China28.6699.110.64
D18Ankang NO.4Shanxi province, China79.9399.910.62
D19ZT500Gansu province, China165.4299.990.61
D20ZhuguiZhejiang province, China77.6199.770.43
D21BilianJiangsu province, China62.8399.491.4
D22ZT900Sichuan province, China80.8199.611.05
D23ZT100Hunan province, China89.799.830.41
D24SihongJiangsu province, China105.0399.750.31
D25XiaoshiwanZhejiang province, China20.4698.310.59
D26C108Chongqing, China27.5792.660.4
D27Sichuang M3Sichuan province, China51.399.670.58
D28ZT000Guizhou province, China166.8599.960.53
D29HandanHebei province, China51.9499.900.32
W1B. mandarina ZiyangSichuan province, China27.696.072.49
W2B. mandarina NanchongSichuan province, China106.1598.182.46
W3B. mandarina HongyaSichuan province, China25.2794.181.94
W4B. mandarina PengshanSichuan province, China76.0395.451.95
W5B. mandarina AnkangShanxi province, China30.5492.302.01
W6B. mandarina YichangHubei province, China108.9896.632.77
W7B. mandarina YanchengJiangsu province, China72.0697.102.03
W8B. mandarina LuzhouSichuan province, China40.5492.452.33
W9B. mandarina HunanHunan province, China114.5796.912.03
W10B. mandarina SuzhouJiangsu province, China49.1195.402.05
W11B. mandarina RongchangChongqing, China79.3195.892.04
W12B. mandarina JapanHokkaido, Japan13.5281.470.81

Reads alignment and consensus assembly

Although we only included the mt genome sequence in the analysis, we prefer to map raw short reads onto the whole genome [including nuclear DNA sequence and mitochondrial DNA (mtDNA) sequence], in order to make alignment more reliable. Due to the fact that three sorts of resequencing data of silkworm have been in hand - which are the domesticated group, the Chinese wild population, and the Japanese wild population - there must be three different references to be used by program SOAP v1.09 [13]. So we built three different reference genomes which are the silkworm nuclear reference genome sequence with C108 mtDNA sequence, with Chinese wild mtDNA sequence and with Japanese wild mtDNA sequence. The mapping results showed that Chinese wild silkworms have a higher mismatch rate than the domesticated ones (see Table ​ Table1), 1 ), which suggests a high sequence diversity in Chinese wild group, even in the intraspecific comparison between Chinese wild variety Ankang and the Chinese wild reference (which is also from Ankang in Shanxi Province of China). Followed by SOAPsnp [14], which based on Bayesian theory, we calculate the posterior probability of each possible genotype at every genome position, from the alignment results for each sample. Then the consensus was structured by the highest probability. 41 consensus sequences have been put into the Genbank under accession number <"type":"entrez-nucleotide","attrs":<"text":"GU966591","term_id":"291575480","term_text":"GU966591">> GU966591- <"type":"entrez-nucleotide","attrs":<"text":"GU966631","term_id":"291576040","term_text":"GU966631">> GU966631.

SNP detection and experimental validation

We used SOAPsnp [14] to call SNPs for each variety. After setting

six steps were used to filter out the unreliable variants: 1) we set Q20 quality cutoff 2) two unique reads were allowed at least 3) the SNPs had to be at least 5 bp away from each other 4) the approximate copy number of flanking sequences had to be no more than 2 5) P value of the rank sum test had to be no less than 0.05 6) the number of unique mapped reads had to be greater than or equal to half of the number of total mapped reads.

To evaluate our SNP calling strategy, we randomly selected 50 sites for PCR-Sanger dideoxy sequencing validation using the AB 3730XL. After manually checking all the intensity trace files, we found that all the sites were confirmed by the PCR-sequencing results.

Linkage Disequilibrium (LD) measure calculation

To measure LD level in the silkworm mt population, we used the normalized measure of allelic association estimate D' [15], which can not be easily influenced by rare alleles examined [16]. We set the parameters in the software Haploview [17] as follows:

-maxdistance 200-dprime-minGeno 0.6-minMAF 0.1-hwcutoff 0.001. Then spot chart was plotted with R scripts which drew averaged D' against pairwise marker distance.

Phylogeny reconstruction

Five silkworm mt sequences (with accession number <"type":"entrez-nucleotide","attrs":<"text":"AB070264","term_id":"18640062","term_text":"AB070264">> AB070264, <"type":"entrez-nucleotide","attrs":<"text":"AY301620","term_id":"60391177","term_text":"AY301620">> AY301620, <"type":"entrez-nucleotide","attrs":<"text":"NC_003395","term_id":"18644896","term_text":"NC_003395">> NC_003395, <"type":"entrez-nucleotide","attrs":<"text":"NC_004622","term_id":"162279939","term_text":"NC_004622">> NC_004622, and <"type":"entrez-nucleotide","attrs":<"text":"NC_012727","term_id":"238563960","term_text":"NC_012727">> NC_012727) were firstly aligned using MUSCLE v3.7 [18] with default settings, adjusting coordinates for the three resequencing groups. From this, we got 46 mt genomes, after integrating these five sequences and 41 consensus that we have, to perform phylogenetic reconstruction following MEGA v4 [19] by using Neighbour-Joining method and Mrbayes v3.1.2 [20] under the Bayesian theory. In Mrbayes, we chose the GTR+gamma+I model, and set the chain length to 50,000,000 (1 sample/1000 generations) and burned in the first 10,000 samples. Almost identical results were obtained in two independent runs. The quality of being dependable of the NJ trees was bootstrapped with 1000 replicate estimates. To evaluate the confidence level in the tree selection, we applied statistical tests with CONSEL [21]. The site-wise likelihood file is derived from PhyML calculatation [22].

Tests of effective population size

First, using the SNP information of each group, we inferred the mt sequence mismatch distributions for domesticated group and Chinese wild group, where a ragged distribution implies the stable population size, and where a bell-shaped pattern is often related to population growth [23,24]. Further, Tajima's D and Fu and Li's D tests for population size were also performed with DnaSP v5 [25].

Neutrality tests

It became clear to us that, during the domestication process of the silkworm, 354 genes in the nuclear genome bear a strong human selection footprint [7]. The mt genome may therefore also experience selective pressure. In order to examine mtDNA protein evolution, we compare the rate of nonsynonymous to synonymous mutation within and between species in each gene. If these ratios differ significantly, they provide evidence of selection pressure [26]. We then applied Williams' correction to calculate the G statistic [27]. Moreover, we also preformed the two likelihood ratio tests (LRTs), based on widely used branch-site models from PAML 4.2 [28], to detect any positive selection.

Materials and Methods

DNA Samples

A total of 74 samples were collected, including blood samples (16), hair samples (16), and dried leather samples (42). Due to degradation, DNA extractions were successful for only 21 of the 42 dried leather samples ( table 1 ). Therefore, the total number of DNA samples was reduced to 53. Both of the two subspecies were included, with five of them being Ailurus fulgens fulgens and the others being Ailurus fulgens styani ( table 1 ). The blood and hair samples were obtained from the Chongqing Zoo and Chengdu Zoos of China, and their wild origins were known. Blood samples were anticoagulated with heparin and stored at −70°C before DNA extraction. The hair samples were collected by plucking and stored at −70°C. The dried leather samples were obtained from collections of the Kunming Institute of Zoology, Chinese Academy of Sciences, and stored at −70°C after sampling. The 53 red pandas were originally from 8 different geographic locations in the Sichuan and Yunnan provinces of China ( fig. 1 ). Although efforts were made to avoid sampling related individuals, the relationships among animals in the sample were generally unknown.

DNA Extraction, Polymerase Chain Reaction, and Sequencing

DNA extractions from blood samples follow the standard phenol-chloroform method. The fresh hair and dried leather samples were first treated with proteinase K at 56°C for 2 h and then incubated with 10% Chelex 100 (Bio-Rad) at 98°C for 30 min. After centrifugation at a high speed (10,000 rpm) for 10 min, the supernatants were collected and directly used as DNA templates for PCR ( Walsh 1990 ). The PCR was conducted by predenaturing at 94°C for 2 min, cycling at 94°C for 1 min, 56°C for 1 min, and 72°C for 1 min for 35–40 cycles, and a final extension at 72°C for 5 min. The primer sequences are CAC CAT CAA CAC CCA AAG CTG (forward) and TTC ATG GGC CCG GAG CGA G (reverse), which amplify a 276-bp fragment located upstream of the mtDNA D-loop region. The PCR products were purified through low-melting-point agarose gel electrophoresis. Sequencing was conducted on an ABI377 automatic sequencer with both forward and reverse primers.

Phylogenetic Analysis and Statistical Tests of Neutrality

For phylogenetic analysis, parsimony (PAUP, version 3.1.1 Swofford 1993 ) and median-joining network analyses ( Bandelt, Forster, and Röhl 1999 ) were used. The homologous sequence of the raccoon (Procyon lotor), the closest living relative of the red panda, was included as an outgroup. The pairwise mismatch distribution was generated using Arlequin, version 2.000 (Schneider, Roessli, and Excoffier 2000). The essential population parameter θ was estimated using Watterson's (1975) estimate, Tajima's (1983) estimate, and Fu's (1994) UPBLUE estimate. Watterson's estimate is based on the number of segregating sites among the sequences. Tajima's estimate is based on the calculation of the mean number of pairwise differences of the sequences, while Fu's UPBLUE estimate is done by incorporating the genealogical information of the sequences. A statistical test of neutrality was carried out using Fu's (1997)FS test. Strictly speaking, all three of these estimators of θ are based on the infinite-sites model ( Watterson 1975 Tajima 1983 Fu 1997 ). Since the sequences generated in this study are from the D-loop region that has mutation hot spots, the infinite-sites model is violated to some extent. To minimize the effect of violation of the model on the estimation of θ, as well as statistical tests of neutrality, we inferred all the required information for parameter estimation and neutrality testing from the parsimony analysis. This was done by first reconstructing a parsimony tree from the sequences and then inferring the required information from the tree. For example, to infer the total number of mutations in the sample, we counted the total number of steps in the parsimony tree. For each pair of sequences, the distance needed for UPBLUE could easily be computed from the parsimony tree as well.

Fu's FS test of neutrality was used to infer the population history of the red panda. The FS value tends to be negative when there is an excess of recent mutations, and therefore a large negative value of FS will be taken as evidence against the neutrality of mutations, an indication of deviation caused by population growth and/or selection.

Jodi Nunnari

Jodi Nunnari received her BA from the College of Wooster and her PhD from Vanderbilt University. She began her studies of mitochondria as a post-doctoral fellow at the University of California, San Francisco in the lab of Peter Walter. Nunnari is currently a Professor of Molecular and Cellular Biology at the University of California, Davis… Continue Reading

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Restriction fragment length polymorphisms in the D-loop,tRNA Met+Glu+Il and ND3 gene fragments

MtDNA D-loop, tRNA Met+Glu+Il and ND3 fragments from T739, TA2,615, BALB/C, C3H, C57BL/6J and DBA/2, were cleaved by 16 restriction endonucleases, including HaeIII, HinfI, EcoRV, HindIII, HpaI, BamHI, ApaI, NdeII, XhoI, XbaI, AluI, RsaI, StuI, DraI, AvaI and HaeII, respectively. No differences in restriction maps were observed, nor were there any variations in the 46 restriction sites in mtDNA D-loop,tRNA Met+Glu+Ile and ND3 fragments in these strains(Fig. 1, Table 2).

Restriction patterns of mouse mt DNA D-Loop, tRN Ile+GlN+Met and ND3 fragments

. D-loop (1100 bp) . . tRNA Ile+GlN+Met (1126 bp) . . ND3 (534 bp) . .
Enzyme . Sites . Fragment length . Sites . Fragment length . Sites . Fragment length .
HaeIII 4 456,445,122. 4 470,290,136. 1 392,142
HinfI 1 700,400 0 1126 4 253,84.
ApaI 1 655,445 1 658,469 0 534
AluI 4 545,269,102. 3 665,318,135. 1 310,224
BamHI 0 1100 2 710,252,164 0 534
DraI 0 1100 1 634,493 1 451,83
StuI 0 1100 2 605,297,225 1 391,143
NdeII 2 845,118,37 0 1126 0 534
RsaI 6 720,178,125. 4 392,259,228. 2 353,103,78
XbaI 1 680,420 0 1126 0 524
. D-loop (1100 bp) . . tRNA Ile+GlN+Met (1126 bp) . . ND3 (534 bp) . .
Enzyme . Sites . Fragment length . Sites . Fragment length . Sites . Fragment length .
HaeIII 4 456,445,122. 4 470,290,136. 1 392,142
HinfI 1 700,400 0 1126 4 253,84.
ApaI 1 655,445 1 658,469 0 534
AluI 4 545,269,102. 3 665,318,135. 1 310,224
BamHI 0 1100 2 710,252,164 0 534
DraI 0 1100 1 634,493 1 451,83
StuI 0 1100 2 605,297,225 1 391,143
NdeII 2 845,118,37 0 1126 0 534
RsaI 6 720,178,125. 4 392,259,228. 2 353,103,78
XbaI 1 680,420 0 1126 0 524

Note: There are no other restriction enzyme sites.

Single-stranded conformation polymorphisms in D-loop 5′ and 3′ end fragments

To further analyze the genetic variations in these strains, D-loop 5′and 3′ end fragments, both of high variability, were subjected to PCR-SSCP analysis. No differences in the SSCP electrophoresis bands were observed (Fig. 2).

Genetic Diversity in Mitochondria - Biology

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Genetic differences between people across the world are no greater than differences between pigeons

In terms of genetics, the difference between any two humans is about the same as the difference between two pigeons.

These results from a massive “DNA barcoding” study will come as a surprise to anyone who thinks of themselves as truly unique, or who places great store by differences between groups.

The typical genetic distinctions within a species, including humans, is one in 1,000 of the "letters" that make up a DNA sequence.

"Culture, life experience and other things can make people very different but in terms of basic biology, we're like the birds,” said Dr Mark Stoeckle from the University of Basel, who co-led the study.


"By determining the genetic variety within species of the animal kingdom, made possible only recently by the burgeoning number of DNA sequences, we've documented the absence of human exceptionalism."

Dr Stoeckle and his colleagues made use of a database containing over five million genetic barcodes from more than 100,000 animal species, assembled by scientists worldwide over the past 15 years.

They looked specifically at mitochondrial sequences – pieces of DNA found in the mitochondria, which are tiny structures providing cells with power.

"If a Martian landed on Earth and met a flock of pigeons and a crowd of humans, one would not seem more diverse than the other according to the basic measure of mitochondrial DNA," said Jesse Ausubel, director of the program for the human environment at The Rockefeller University.

In a paper published in the journal Human Evolution, the researchers concluded that in fact there is very little genetic diversity within most animal species, and very clear genetic distinctions between species.

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