Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel

Abstract

The nature and extent of mitochondrial DNA variation in a population and how it affects traits is poorly understood. Here we resequence the mitochondrial genomes of 169 Drosophila Genetic Reference Panel lines, identifying 231 variants that stratify along 12 mitochondrial haplotypes. We identify 1,845 cases of mitonuclear allelic imbalances, thus implying that mitochondrial haplotypes are reflected in the nuclear genome. However, no major fitness effects are associated with mitonuclear imbalance, suggesting that such imbalances reflect population structure at the mitochondrial level rather than genomic incompatibilities. Although mitochondrial haplotypes have no direct impact on mitochondrial respiration, some haplotypes are associated with stress- and metabolism-related phenotypes, including food intake in males. Finally, through reciprocal swapping of mitochondrial genomes, we demonstrate that a mitochondrial haplotype associated with high food intake can rescue a low food intake phenotype. Together, our findings provide new insight into population structure at the mitochondrial level and point to the importance of incorporating mitochondrial haplotypes in genotype–phenotype relationship studies.

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Fig. 1: Sequencing of mtDNA-enriched libraries for 169 DGRP lines.
Fig. 2: Mitochondrial genomic variation within the DGRP.
Fig. 3: Mitochondrial haplotypes of the DGRP.
Fig. 4: The GRD landscape of the DGRP.
Fig. 5: GRDs between mitochondrial and nuclear variants in the DGRP.
Fig. 6: MHA analysis.

Data availability

The sequencing data available at the NCBI Sequence Read Archive is currently submitted under accession no. SRP168326 (see also Supplementary Table 20). Source data for Fig. 3 is provided with the paper. All other data (that is, variant files, GRDs) can be found in the remaining Supplementary Tables.

Code availability

All code used for this study has been deposited with GitHub (https://github.com/DeplanckeLab/BeversLitovchenko2018).

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Acknowledgements

We thank the laboratory members of the Deplancke laboratory for helpful suggestions regarding the experiments and analyses and in particular D. Alpern, V. Gardeux and M. Frochaux. We are also grateful for the assistance from the Auwerx and Schoonjans laboratories and in particular from V. Lemos, A. Mottis and P. Luan. We thank the genomics core facilities at the Université de Lausanne and École Polytechnique Fédérale de Lausanne (EPFL) for sequencing the libraries, and B. Habermann for valuable suggestions on mitonuclear genes. We are also grateful for the computational infrastructure provided by the Swiss Institute of Bioinformatics and Vital-IT at EPFL. This project was funded by a grant from SystemsX.ch to B.D. (AgingX, 51RTP0_151019) and by institutional support from EPFL.

Author information

R.P.J.B., M.L. and B.D. conceptualized the study. R.P.J.B., M.L. and V.S.B. performed the experiments. M.L., R.P.J.B. and A.K. performed the computational analyses. M.R.R., J.A. and B.H. provided critical suggestions and comments on the manuscript. R.P.J.B., M.L. and B.D. wrote the manuscript.

Correspondence to Bart Deplancke.

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Peer review information Primary Handling Editors: Christoph Schmitt, Ana Mateus.

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Extended data

Extended Data Fig. 1 Quality assessment of mtDNA-enriched libraries.

a) Each bar represents the coverage of a single DGRP line. Samples sequenced using a paired-end strategy are shown in grey and samples sequenced using a single-end strategy are shown in blue. See also Supplementary Table 1 for a detailed overview of sequence statistics per sample. b) Comparison of the normalized coverage between our mtDNA sequencing method (blue) and the regular sequencing profile of Mackay et al. 2012 [11] (pink) across the mitochondrial genome. Solid lines depict the coverage profile per DGRP line and dashed lines depict the overall coverage per bp achieved by each of the studies. GC-content is depicted with a grey solid line (200 bp bins). Light green blocks represent the mitochondrial genes excluding tRNAs. c) Dots represent the coverage per DGRP line in the coding region. The barplot represents the mean coverage over all DGRP lines in the coding region. Error bars represent the standard error of means. d) Number of DGRP lines with loci containing a coverage below 10× . e) Average number of loci with a coverage below 10 × . Dots represent number of loci below 10× per DGRP line, and the barplot represents the mean of all populations with loci below 10× coverage. Error bars represent the standard error of means of all represented populations.

Extended Data Fig. 2 NUMT analysis schematic.

A schematic representation for our newly developed computational strategy to detect NUMTs. The upper part shows the mapping of short Illumina reads solely to the mitochondrial genome and the detection of points that could be putative integration sites of mitochondrial fragments into the nuclear genome (breakpoints) as transitions from soft clipped bases to matching bases (SM) or the other way around (MS). All possible combinations of Illumina paired end reads in relation to their alignment to the breakpoints are depicted. The reads that could not be used in the breakpoint inference are shaded. Parts of the read matched to the mitochondrial genome is colored in orange, whereas soft clipped parts, putatively matching to the nuclear genome, are colored in green. The nuclear genome harboring the NUMT is shaded and represented with a dotted line to indicate that the reads were not aligned to it. A similar scenario would take place for the single end Illumina reads. For the second part, alignment of the 454 reads to SM and MS genomes is performed. SM reads are reads with the first part being soft clipped and the second part matching to the mitochondrial genome. Those reads mark the start of putative NUMTs. The consensus sequence of reads was inferred and was considered as an SM genome. Similarly, MS reads mark the end of putative NUMTs and thus the consensus of the reads presents an MS genome. Next, 454 reads were mapped to both SM and MS genomes and only reads aligned to both SM and MS ‘chromosomes’ were used to infer a consensus sequence of a putative NUMT.

Extended Data Fig. 3 Flow scheme of genotyping and variant calling.

Samples were first genotyped based on nuclear fragments that were sequenced by Mackay et al. 2012 [11]. Corrections for the genotype (where necessary) were applied prior to mitochondrial variant calling.

Extended Data Fig. 4 Variant distribution and heterozygosity within the DGRP.

a) The average number of mitochondrial variants per DGRP. b) Number of variants per basepair per gene per type corrected for gene length. Next to each bar, the number of variants and gene length are displayed. c) Flanking regions that were used to accurately retrieve reads containing the heteroplasmic repeat sequence. d) Sanger sequencing of randomly selected DGRP lines of the intergenic repeat region. e) Frequency of the dominant (maximum number of reads) types of the heteroplasmic intergenic repeat region in the DGRP. The reference (iso-1) type is in red. f) The frequency of all types of heteroplasmic intergenic repeat regions observed in all DGRP lines that are supported by 10 or more reads. The reference (iso-1) type is in red. g-j) Amplicon sequencing results for variant 3596_G/A for DGRP-528 (g), variant 10670_G/A for DGRP-136 (h) and variant 10868_G/A for DGRP-21 (i) and DGRP-373 (j). From left to right, results are depicted for eight males (upper), and females (lower) for amplicon sequencing of the respective DGRP line. Next, results are shown for the iso-1 reference strain followed by the initial sequencing results and variant calling from our mtDNA-enriched sequencing for the respective DGRP line and iso-1. Alternate alleles are displayed in light grey whereas reference alleles are dark grey. k) Heteroplasmy example. The upper panel shows amplicon sequencing results for male #1 for DGRP-21 versus male #1 for iso-1 (lower panel). In red, variants of the alternate allele are shown. l) Relationship between the number of mitochondrial variants in DGRP lines and Wolbachia infection. The lines within the violin represent the 25th (lower), 50th, and 75th (upper) percentile. Lower and upper end of the violin plot represent the bottom 25% and upper 75% of the data.

Extended Data Fig. 5 Population structure and metabolic effects of mitochondrial haplotypes.

a) Permutations of the genetic distance between haplogroups from Fig. 3a using the GST estimator. b) Outline of the analysis to quantify relative mitochondrial complex I levels. Relative intensity ratios between complex I of w1118 over complex I of a given DGRP line are calculated. c) Examples of DGRP lines displaying high, mid, and low relative levels of complex I. d) Oxygen consumption rates (OCR) in pMoles per minute for different mitochondrial respiration states. e) Oxygen consumption rates (OCR) for individual lines/genotypes per state. For each genotype, two biological replicates were used, except DGRP-235 for which only one replicate was available. For each biological replicate, four time-measurements were made for state II, five for state III, three for state IVo, and three for Rotenone. For the calculations, the individual measurements are presented as dots and the mean of the measurements are represented by the barplot. The error bars are the standard error of means. f) Respiratory control ratio (RCR) as calculated from a ratio of state III over state IVo. Statistical tests in d, e, and f were calculated using ANOVA. For the boxplots in d and f boxes represent the 25th (lower) to 75th (upper) percentile. Whiskers correspond to the bottom 25% and upper 75% of the data with the median indicated by a thick black line.

Extended Data Fig. 6 Variation within the sex-lethal gene region.

The location of nuclear genomic variants in the DGRP is shown above the genes.

Extended Data Fig. 7 Crossing scheme for genotype ratio distortion (GRD).

Detailed overview of the crossing scheme that was used to construct the ACI and ACR conplastic populations.

Extended Data Fig. 8 Supporting figures related to genotype ratio distortion (GRD) analysis and experiments.

a) Climbing activity of imposed (ACI) and rescue (ACR) allelic-combination populations (ANOVA, p-value = 0.7). Dots represent individual (n = 9) ACI or ACR populations (n = 3 biological replicates per group). Red dots represent the mean over all populations. Error bars represent standard error of means. For genotype w1118 n = 6 biological replicates were used. b) Epistatic interactions between the mitochondrial variant chrM 791 and nuclear variant located at position 3 L 10690916. The variants influence starvation resistance in males. The p-value is adjusted for multiple testing (p-value = 0.00022, χ2-test as implemented in PLINK). c) Epistatic interactions between the mitochondrial variant chrM 791 and nuclear variant located at position 3 L 10687695. The p-value is adjusted for multiple testing (p-value = 0. 00075, χ2-test as implemented in PLINK). Notably, there is a 3 kb distance between the epistatic nuclear variants that associated with starvation resistance in males versus females. For boxplots on b,c boxes represent the 25th (lower) to 75th (upper) percentile. Whiskers correspond to the bottom 25% and upper 75% of the data with the median indicated by a thick black line.

Extended Data Fig. 9 Food intake of males for individual F1 populations.

In blue, populations are shown with the mitochondrial haplotype 5 (MH5; high food intake), and in red, populations with the mitochondrial haplotype 1 (MH1; low food intake). Mat = maternal, pat = paternal. Boxes represent the 25th (lower) to 75th (upper) percentile. Whiskers correspond to the bottom 25% and upper 75% of the data with the median indicated by a thick black line.

Supplementary information

Supplementary Information

Supplementary Notes 1–7

Reporting Summary

Supplementary Tables

Supplementary Tables 1–24

Source data

Source Data Fig. 3

Unprocessed BN–PAGE gels.

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Bevers, R.P.J., Litovchenko, M., Kapopoulou, A. et al. Mitochondrial haplotypes affect metabolic phenotypes in the Drosophila Genetic Reference Panel. Nat Metab 1, 1226–1242 (2019) doi:10.1038/s42255-019-0147-3

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