The mitochondrion is a complex and essential organelle whose dysfunction is linked to a panoply of diverse human pathologies and diseases. The understanding of the traditional role of mitochondria as the powerhouses of the cell has evolved as its multiple roles in cellular and physiological homeostasis have been functionally enumerated far beyond its commonly known role in energy production via the electron transport chain (ETC) (Spinelli and Haigis, 2018). All these functions rely on a coordinated cross-talk between the nuclear genome and its mitochondrial counterpart (Vafai and Mootha, 2012). The widespread activity of mitochondria provides ample opportunities for mitochondrial dysfunction to play a role in human disease, but current understanding does not provide specificity in differential biological function or capacity in accurately predicting pathogenic details suitable for therapeutic development.

Here we focus on Leigh Syndrome, French Canadian Type (LSFC), an autosomal recessive mitochondrial disease with onset in infancy that manifests with diagnostic liver dysfunction (Morin et al., 1993). The most common allelic variant is due to an A354V transition in exon 9 of the leucine-rich pentatricopeptide repeat-containing motif (LRPPRC) protein (Mootha et al., 2003). Patients with mutations in the LRPPRC gene experience a diverse array of clinical features centered around cytochrome c oxidase (COX) deficiency. These can include early onset of hepatic micro vesicular steatosis and chronic lactic acidosis in neonates. Individuals born with LSFC have an average lifespan of less than 5 years of age with most succumbing to a series of extreme, acute metabolic crises. Patients that make it past the early life crises show a lessened disease state characterized by hypotonia, language and mobility deficits and delays, and muscle weakness (Morin et al., 1993; Debray et al., 2011).

LRPPRC belongs to the pentatricopeptide repeat (PPR) containing motif family of proteins. The LRPPRC protein in humans has been reported to have 33 predicted PPR domains and is promiscuous, preferentially binding to different mitochondrial RNAs (Spåhr et al., 2016). LRPPRC plays role in the regulation of both nuclear and mitochondrial RNA expression at the transcriptional and post-transcriptional levels (Liu and McKeehan, 2002; Mili and Piñol-Roma, 2003). Loss of functional LRPPRC in human fibroblasts, C. elegans, and mice revealed decreased stability of mitochondrial mRNAs, defects in mitochondrial biogenesis, decreased complex 1 and cytochrome c oxidase activity, and dysregulated mitochondrial translation (Cuillerier et al., 2017; Gohil et al., 2010; Xu et al., 2012; Köhler et al., 2015). Given that onset of LSFC is marked by the neurological and metabolic crisis (Morin et al., 1993) and the liver is the major metabolic factory of the organism, therefore, the role of liver impairment in LSFC represents a unique aspect of mitochondrial disease unseen in traditional Leigh syndrome. Mice harboring liver-specific inactivation of Lrpprc mirrored similar clinical manifestations marked by mitochondrial hepatopathy, growth delay, and reduced fatty acid oxidation compared to controls (Cuillerier et al., 2017). Recently, a lipidomic profile study on LSFC patients highlighted a novel role of this protein in the peroxisomal lipid metabolism (Ruiz et al., 2019). The current diagnosis of LSFC hinges on measuring lactate levels in the blood and brain, COX activity in patient fibroblasts, and sequencing of the LRPPRC gene for mutations (Debray et al., 2011). The lack of a cure or effective therapies for LSFC patients signifies that current clinicians must shift their focus to relieving and controlling symptoms. Focus on dietary restrictions and lifestyle changes, including exercise and infection risk management, are made to reduce the physiological and metabolic stress on the patient. To build upon a heuristic paradigm for therapeutic interventions for these classes of disorders, we need to establish disease models that serve as a suitable substrate to recapitulate the key functional tissue-specific pathology of the disease.

Zebrafish have been employed as a powerful potential vertebrate model organism to study human mitochondrial disorders (Broughton et al., 2001; Steele et al., 2014; Sabharwal et al., 2019a). Previously, we have reported a compendium of approximately 1200 zebrafish transgenic lines created using the gene-breaking transposon approach out of which 204 were clonally validated and molecularly characterized (Ichino et al., 2020). The GBT construct is incorporated into the intron of an endogenous gene via Tol2 transposase and uses a splice acceptor site to create a fusion protein between the native transcript and red fluorescent protein (mRFP) gene that contains a transcription termination site. When inserted intergenically, this results in the translation of a truncated endogenous gene product fused to mRFP. This enables spatiotemporal tracking of the trapped gene’s natural protein expression in real-time. This insertion is reversible due to two loxP sites located near each terminal repeat that enables the removal of the GBT cassette using Cre recombinase. The revertible nature of this construct allows for the reversion of the mutant back to wild-type alleles to allow for tissue-specific or ubiquitous rescue of the mutated gene (Ichino et al., 2020; Clark et al., 2011). The previous work (Ichino et al., 2020) reported over thirty new potential new models of human disease; we report here the characterization and detailed investigation of one, the mitochondrial protein-encoding locus lrpprc.

We describe here the first revertible zebrafish mutant with a single disruptive insertion in the lrpprc gene to model LSFC. lrpprc homozygous mutants mimic many hallmarks observed in patients such as early lethality, defective muscle development, and decreases in mitochondrial transcript levels. In addition, altered dietary lipid metabolism along with mitochondrial dysmorphology was also observed in these mutants. A key outcome of this study is the reversion of the LSFC-associated phenotype and survival using liver-specific Cre recombinase in the homozygous lrpprc mutants, demonstrating a critical role of this organ in the pathogenesis of disease in this animal model. These results contribute to a novel paradigm for the identification of therapeutic approaches for LSFC and other related disorders.

In this study, we present a novel zebrafish model for Leigh Syndrome, French Canadian Type (LSFC) identified through the RP2 gene-breaking transposon insertional mutagenesis screen. Phenotypic assessment of this novel allele demonstrates how zebrafish serve as an excellent model to study LSFC. Zebrafish Lrpprc shares 51% homology with its human ortholog and has 31 predicted similar PPR domains indicating the conserved mitochondrial mRNA binding role of this gene. The spatiotemporal analysis of Lrpprc-mRFP shows a co-localization with an established mitochondrial marker in the caudal fin region and myocytes, as the truncated fusion proteins retain the mitochondrial targeting sequence maintaining its predicted subcellular localization. We also observed remarkable ubiquitous expression patterns throughout the organism and hotspots in the liver, skeletal muscle, and eye. In addition, increased expression of the fusion protein in certain tissues such as the liver, skeletal muscle, and eyes is indicative of the observation that mitochondrial density is tied to energy-intensive processes like metabolism, movement, and vision. Differential expression data will nevertheless help in expanding our understanding of the different roles of this gene in tissue-specific niches.

Sequence analysis of the predicted zebrafish Lrpprc protein revealed the presence of some known homology domains like secretory (SEC)1, RPN7, DUF2517, and EAD11. SEC1 homology domain-containing proteins are known to be involved in vesicular trafficking, synaptic transmission, and general secretion (Liu and McKeehan, 2002; Halachmi and Lev, 1996). In addition, the human LRPPRC protein interacted with various other protein partners like fibronectin through its SEC1 domain (Liu and McKeehan, 2002). The effector-associated domains (EADs) are small, non-catalytic domains and are predicted to be involved in protein-protein interactions (Kaur et al., 2021). The RPN7 domain is one of the regulatory subunits of the 26 S proteasome and is known to be required for structural integrity in Saccharomyces cerevisiae (Isono et al., 2004). The region of zebrafish Lrpprc from amino acids 966–1012 had homology towards the protein of unknown function (DUF2517) domain, which is common to proteobacteria. The function of the DUF2517 homology domain is still unknown. Our in silico structural studies demonstrated that the C-terminus of human LRPPRC was well aligned with the C-terminus of zebrafish Lrpprc (Figure 2—figure supplement 3), strengthening the hypothesis that the zebrafish Lrpprc is playing a critical role in cell metabolism as discussed above. Additionally, the multiple sequence alignment of LRPPRC showed that the C-terminus region of this protein was conserved between several model organisms (Figure 2—figure supplement 3). Primary sequence and structural analyses highlighted the loss of domains important for maintaining the stability of mtRNA transcripts such as RPN7, DUF2517, SEC1, and EAD11 present in the C-terminus region of LRPPRC where a protein truncation led to LSFC-associated phenotype in zebrafish.

To understand the phenotypic and metabolic findings in the Lrpprc-deficient zebrafish model, we also analyzed the potential RNA binding targets of the Lrpprc protein. Lrpprc contains multiple PPR motifs, and these functional domains determine the target RNA-binding sites. We first annotated the PPR motifs for the Lrpprc protein for zebrafish and human orthologs to explore RNA binding sites. Preliminary analysis using the TPRpred server identified 22 PPR motifs for both human and zebrafish Lrpprc proteins (Hammani et al., 2016). TPRpred is a profile-based method that uses a P-value-dependent score offset and exploits the tendency of repeats to occur in tandem (Karpenahalli et al., 2007). This tool not only predicts the Tetratrico Peptide Repeat (TPR) motifs, but also the related PPRs and SEL1-like repeats. However, a study by Spåhr and colleagues had predicted 33 PPR motifs for the human LRPPRC protein (Spåhr et al., 2016) which was more than what was predicted by the TPRpred (Karpenahalli et al., 2007) server. Additionally, we evaluated the secondary structure containing the Lrpprc proteins by PSIPRED server, we found that both humans and zebrafish are all α-helical and may contain more hairpin of α-helices than what TPRpred predicted. To address this discrepancy and to conduct a meticulous study to systematically identify PPR motifs in human and zebrafish Lrpprc, we employed a deep-learning-based structure modeling tool, RoseTTAFold (Baek et al., 2021). Using RoseTTAFold we designed a workflow to predict 31 and 34 PPR motifs for the zebrafish and human orthologs respectively of Lrpprc proteins.

We investigated the sequences of the identified PPR motifs in each of the orthologs and deciphered the RNA recognition residues, which are typically found at the 5th and 35th positions in each PPR motif. These two amino acids play a major role in specifying the target nucleotide and constitute a code for nucleotide recognition. The potential target recognition codes for human and zebrafish Lrpprc PPR motifs were predicted to exhibit a range of nucleotide targets (Figure 2—figure supplements 1 and 2). We used the FIMO search algorithm to predict the potential binding sites for human and zebrafish Lrpprc proteins. In silico RNA-binding analysis revealed that transcripts coding for proteins involved in the complex IV activity such as mt-co1 and mt-co2 were among the hits with a high confidence score. Apart from complex IV genes, transcripts belonging to complex I and complex V were also identified to be recognized by the PPR motifs of the Lrpprc protein. There was a consensus for the majority of the target transcripts for the zebrafish and human orthologs indicating a conserved function of Lrpprc in the maintenance of the mitochondrial genome both in humans and zebrafish. Previously, human LRPPRC was reported to regulate transcripts coding for complex IV of mitochondrial electron transport chain (Xu et al., 2004; Barchiesi and Vascotto, 2019) which was very well aligned with these findings and supports the hypothesis that both human and zebrafish LRPPRC likely have similar mitochondrial targets. To the best of our knowledge, this is the first such analysis that has been performed and validated for PPR-based proteins.

Previously published reports have highlighted the role of PPR-containing proteins in mediating mRNA stability and translational activation (Prikryl et al., 2011; Mesitov et al., 2019). PPR proteins have been shown to stabilize the RNA structure by blocking the access to exonucleolytic cleavage upstream and downstream of their binding sites in a way acting as protein caps at the 5’ and 3’ ends of the processed transcripts (Prikryl et al., 2011). It also confers a site-specific remodeling activity by sequestering the target RNA by preventing it from binding to the translation initiation region (Prikryl et al., 2011). Another functionality contributed by this class of proteins is aiding in the inhibition of uridylation of A-tailed transcripts, thereby preventing the degradation of mRNAs (Mesitov et al., 2019). These roles highlight the role(s) of PPR motif-containing proteins in the stability of the mitochondrially encoded transcriptome.

Upon further investigation using independent transcriptomics sequencing and targeted mitochondrial RNA qPCR, it was observed that downregulated mtDNA genes in the lrpprc homozygous zebrafish mutants overlapped with those predicted by the above algorithm. These results possibly explain the reduced expression of mitochondrial RNAs in the homozygous mutants, underscoring the role of Lrpprc as loss of this protein impedes the binding of this protein, therefore leading to mRNA instability and degradation.

As the pathophysiology of LSFC is not well understood, we set out to explore the physiological consequences of lrpprc loss-of-function mutation in vivo and whether we could mimic the clinical hallmarks seen in LSFC patients. Similar to human patients, lrpprc^{GBT0235/GBT0235} mutants displayed complete larval lethality by 12 dpf. However, factors triggering the crisis and associated death in both patients and animal model was initially unclear. Consistent with previous in vitro studies (Gohil et al., 2010), expression of mitochondrial protein-coding transcripts was observed to be downregulated in these homozygous mutants and was further investigated using RNASeq, indicating LRPPRC to be a critical post-transcriptional regulator of mitochondrial transcripts. LRPPRC protein in mitochondria forms a complex with SRA stem-loop interacting RNA binding protein (SLIRP) protein in the mitochondrial matrix. The absence of LRPPRC leads to oligoadenylation of the transcripts. LRPPRC-SLIRP is also observed to relax RNA structure, making the 5' end of the mRNA accessible for the mitochondrial ribosome to initiate the translation (Cui et al., 2019). The mechanistic insights in our zebrafish in vivo model from the structure of Lrpprc protein and target sites of the predicted PPR domains as described earlier correlate with observations that have been well established in patient-derived cell culture model displaying the altered signature of mitochondrial transcripts (Gohil et al., 2010; Sasarman et al., 2015).

Decreased expression of these protein-coding transcripts is crucial for eliciting defects in oxidative phosphorylation and mitochondrial respiratory chain. These results were in concordance with the pathways responsible for mitochondrial biogenesis and homeostasis predicted to be depleted in the lrpprc homozygous mutants. Various functions including energy production, genome maintenance, ion/metabolite homeostasis, membrane dynamics, and transport of biomolecules can be attributed to approximately 1136 proteins residing in the mitochondria (Rath et al., 2021). We did note an overlap between the set of differentially expressed genes and those in MitoCarta catalogue, suggesting an altered mitochondrial transcriptome signature. Apart from classical respiratory chain subunits, we identified some key genes from this overlapped data to be differentially expressed that were responsible for pathways such as aldehyde metabolism, fatty acid metabolism, and most important, unfolded protein response. Genes belonging to fatty acid metabolism and cytochrome P450 were observed to be downregulated in the null mutants. lrpprc homozygous mutants exhibited an upregulation of chaperone proteins, heat shock protein 5 (hspa5), and heat shock 60_1 (hspd1), possibly eliciting mitochondrial unfolded protein response that forms an integral part of mitochondrial protein homeostasis.

Another high energy-requisition tissue on the phenotypic spectrum of LSFC that we explored was muscle. LSFC patients display muscle defects such as hypotonia and COX deficiency (Oláhová et al., 2015) and therefore, we adopted a birefringence measurement to assess the muscle phenotype in our null mutants. It takes a complex and highly organized structure (like the sarcomeres in skeletal muscle) to rotate the plane-polarized light and enable the visualization of birefringence. The noted decrease in birefringence in lrpprc mutants is most likely due to the disorganization of sarcomeres and other structures in skeletal muscle. It has also been noted that LRPPRC deficiency in patient-derived muscle cells resulted in combined complex I and complex IV deficiency (Sasarman et al., 2015). These skeletal muscle defects due to impaired lrpprc function may reflect the tissue-specific adaptation to mitochondrial bioenergetics response. The structural analysis of identifying PPR motifs in zebrafish Lrpprc does explain some of these observations as it was revealed that the top potential binding sites of this protein were genes related to complex IV. A few of them were mt-co1, mt-co2, and mt-co3 which encode subunits of the complex IV of the mitochondrial electron transport chain. Interestingly, muscle phenotypes that were displayed by lrpprc homozygous mutants were not mitigated by the liver-specific reversion of the mutagenicity cassette. This not only underscores the specificity of the revertible cassette in a given tissue but also indicates the improved survival and lipid homeostasis observed in rescued animals being at least partially independent of muscle dysfunction. One of the possible explanations for the impaired muscle function not contributing to lethality in rescued animals can be compensatory mitochondrial biogenesis or mitochondrial proliferation in muscle, a tissue with high metabolic demands that are also observed in patients with LSFC (Cuillerier et al., 2021). There might be adaptive OXPHOS compensation in these kinds of tissues to adapt to the physiological demands in models with a mutation in genes affecting global mtDNA translation leading to multi-complex deficiency. Further studies will be required to explain any of these possibilities exploring the role of bioenergetic adaptation happening in a specific tissue through unknown metabolite and protein players in the cellular milieu.

The liver-specific dysfunction coupled with the episodes of acute metabolic crises distinguishes LSFC from classic Leigh Syndrome (LS). A previous study suggested that liver dysfunction may factor into the increased lactic acidosis and metabolic disturbances as the liver play a key role in lactate homeostasis via the Cori cycle (Sun et al., 2017). In addition, it is important to note that other basic liver functions that are dependent on mitochondrial activity such as lipid metabolism, serum detoxification, and gluconeogenesis may be key to the development of metabolic crises since they have secondary effects in other organ systems. Mitochondrial morphology is the classical hallmark of underlying mitochondrial dysfunction. Mitochondria can undergo changes in shape accompanied by fusion and fission events which may involve remodeling at cristae junctions that dynamically appose and separate from each other (Cogliati et al., 2016; Kondadi et al., 2020). Under TEM, hepatic mitochondria had an altered morphology in the homozygous mutants, potentially impairing mitochondrial function. From the transcript studies and motif-based target prediction, we did observe a decreased expression of mt-atp6 and mt-atp8 transcripts due to Lrpprc deficiency. Proteins coded by these mRNAs constitute the complex V of the electron transport chain. It has been reported that complex V plays an essential role in defining the cristae morphology specifically in the areas of high membrane curvature at cristae ridges (Cuillerier et al., 2017).

It has been observed that mitochondrial disorders increase predisposition to intracellular lipid accumulation (Morino et al., 2006). Homozygous lrpprc mutants manifested early-onset phenotypes with hepatomegaly and progressive accumulation of dark-colored granules in the liver at 6 dpf. These granules appeared to be lipid droplets, which were validated with both Oil Red O staining and extensive in vivo feed/chase (i.e., metabolize) labeling lipid studies. Dietary lipid metabolism studies in the homozygous mutants revealed an accumulation of non-polar lipids such as triglycerides and diglycerides. Accumulation of these lipid species highlights an impaired lipid metabolism in the mutants similar to what is observed in LSFC patients (Ruiz et al., 2019). Metabolic diseases such as nonalcoholic fatty liver disease are associated with the accumulation of triglyceride levels and high low-density lipoprotein levels with an underlying mitochondrial dysfunction (Simões et al., 2018). Hepatic accumulation of such lipids may induce mitochondria to undergo dynamic changes such as depolarization and loss of ATP content (Domínguez-Pérez et al., 2019). Further probing of the transcriptomics study revealed that pathways for fatty acid degradation were depleted in these null mutants. The gene encoding for hepatic lipase_a (Lipca), a protein that plays an important role in triglyceride hydrolysis was downregulated by fold change of log₂ ~2. Accumulation of triglyceride levels in homozygous mutants was corroborated with findings from the RNA-Seq study, where genes such as Cytochrome P450 Family 7 Subfamily A or Cholesterol 7 alpha-hydroxylase (cyp7a1) and lipca were observed to be downregulated. These genes are involved in the Farnesoid X receptor pathway that plays an important role in hepatic triglyceride metabolism (Jiao et al., 2015). Interestingly, we observed the targets for the sterol regulatory element-binding proteins (SREBPs) to be differentially expressed. Prominent amongst those were ELOVL fatty acid elongase 2 (elovl2), fatty acid desaturase 2 (fads2), and lipca. Elovl2 is essential for lipid homeostasis, wherein it regulates the levels of poly-unsaturated fatty acids preventing liver steatosis (Pauter et al., 2014). The binding proteins are thought to manage intracellular fatty acids and serve to provide them to specific enzyme pathways in the cytoplasm. Similarly, reduced lipca would result in fewer cytoplasmic fatty acids consistent with the less cellular requirement of fatty acid-binding proteins (also observed to be downregulated in our data). Reduced availability of fatty acids would allow fewer substrates for ELOVL2 and FADS2. Another lipid metabolism-associated gene observed to be downregulated was fatty acid-binding protein 10 a (fabp10a) by fold change of log₂ ~2.27. Intrigued to explore, if the abolishment of lrpprc had an inter-organelle effect, we analyzed the expression of genes that are involved in peroxisome-associated pathways. To summarize, the transcriptomic analysis did reveal that lrpprc homozygous mutants displayed altered expression of genes that modify and regulate fatty acids and were consistent with less fatty acid reflux and metabolism observed in lrpprc homozygous mutants. Surprisingly, MPV17 mitochondrial inner membrane protein-like 2 (mpv17l2), one of the regulator genes for the expression of antioxidant enzymes (Iida et al., 2006), was upregulated by fold change of log₂ 1.74. Overall, our zebrafish LSFC model exhibited a dysfunctional mitochondrial signature both at the transcriptional and phenotypic level, which was explored to design a genetic model therapy for LSFC-associated phenotypes.

The key translational and clinical utility of this study is the reversion of the LSFC-associated phenotype and survival using liver-specific Cre recombinase in the homozygous lrpprc mutants, demonstrating a critical role of this organ in the pathogenesis of disease in this animal model. Because GBT-RP2 contains two loxP sites flanking the protein trap component, we could exploit this utility to rescue the organ-specific function of lrpprc^{GBT0235/GBT0235}. Interestingly, reversion of both lethality and biochemical phenotypes was observed in the rescued mutants, suggesting the liver plays a critical role in the pathology of LSFC. Although the rescue of mortality phenotype in the Cre lrpprc homozygous mutants was remarkable, it should be noted that longitudinal studies are needed to follow up on the survival of the rescued larvae. However, there will be a few points such as variables in zebrafish husbandry settings and the expression of the transgenic recombinase in the hepatocytes that will need to be considered while designing such experiments. Further experiments are required to delineate the juvenile or adult-onset mortality in the rescued animals due to the expression of truncated Lrpprc protein in the whole body minus liver or due to constitutive expression of Cre recombinase in the liver.

Remarkably, these liver-specific rescued larvae exhibited similar levels of non-polar lipids as compared to the wild-type larvae, post-high-fat meal underscoring the lipid homeostasis as a key therapeutic paradigm in the progression of this disease. Mitigation of abnormal mitochondrial structure was also observed in the hepatocytes of the rescued larvae. Although we did observe that rescue was not completely reversible in the liver-specific Cre homozygous mutants. There may be several factors that can contribute to this observation such as incomplete penetrance or rescue of the mutant allele. As cristae are dynamic structures of mitochondria whose morphology is altered under various bioenergetics and physiological conditions (Cogliati et al., 2016; Kondadi et al., 2020), reversion of mutagenicity cassette in the hepatocytes is not sufficient to correct cristae alterations that appeared to be aggravated in the lrpprc homozygous mutants. A possible explanation for the restoration in the morphology may be attributed to the restoration of levels of mitochondrial transcripts and thus restoring mitochondrial-mediated liver functions such as beta-oxidation and fatty acid elongation.

With advances in gene editing alongside developments in both viral and non-viral vector delivery, there is hope for tissue-specific gene therapy as a potential avenue to help mitochondrial disease patients. Our findings highlight the potential therapeutic applications of advances in gene therapy for patients of LSFC by rescuing lrpprc expression in the liver. We anticipate that these results, together with those from the conditional knockout experiments will enhance our understanding of the function of the lrpprc gene in hepatic lipid homeostasis and shine a light on new therapeutic options.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data is provided along with the manuscript. Raw sequencing data has been uploaded on NCBI SRA (PRJNA683704).

1. 1. Sabharwal A
   2. Wishman MD
   3. Cervera RL
   4. Serres MR
   5. Anderson JL
   6. Treichel AJ
   7. Ichino N
   8. Liu W
   9. Yang J
   10. Ding Y
   11. Deng Y
   12. Farber SA
   13. Clark KJ
   14. Xu X
   15. Ekker SC

   (2020) NCBI Sequence Read Archive

   ID PRJNA683704. Zebrafish Model for LSFC.

   https://www.ncbi.nlm.nih.gov/bioproject/PRJNA683704/

1. 1. Baek M
   2. DiMaio F
   3. Anishchenko I
   4. Dauparas J
   5. Ovchinnikov S
   6. Lee GR
   7. Wang J
   8. Cong Q
   9. Kinch LN
   10. Schaeffer RD
   11. Millán C
   12. Park H
   13. Adams C
   14. Glassman CR
   15. DeGiovanni A
   16. Pereira JH
   17. Rodrigues AV
   18. van Dijk AA
   19. Ebrecht AC
   20. Opperman DJ
   21. Sagmeister T
   22. Buhlheller C
   23. Pavkov-Keller T
   24. Rathinaswamy MK
   25. Dalwadi U
   26. Yip CK
   27. Burke JE
   28. Garcia KC
   29. Grishin NV
   30. Adams PD
   31. Read RJ
   32. Baker D

   (2021) Accurate prediction of protein structures and interactions using a three-track neural network

   Science 373:871–876.

   https://doi.org/10.1126/science.abj8754

   • PubMed
   • Google Scholar
2. 1. Barchiesi A
   2. Vascotto C

   (2019) Transcription, processing, and decay of mitochondrial RNA in health and disease

   International Journal of Molecular Sciences 20:E2221.

   https://doi.org/10.3390/ijms20092221

   • PubMed
   • Google Scholar
3. 1. Barkan A
   2. Rojas M
   3. Fujii S
   4. Yap A
   5. Chong YS
   6. Bond CS
   7. Small I

   (2012) A combinatorial amino acid code for RNA recognition by pentatricopeptide repeat proteins

   PLOS Genetics 8:e1002910.

   https://doi.org/10.1371/journal.pgen.1002910

   • PubMed
   • Google Scholar
4. Book

   1. Bernard Thisse CT

   (2004)

   Fast Release Clones: A High Throughput Expression Analysis

   ZFIN.

   • Google Scholar
5. 1. Bolger AM
   2. Lohse M
   3. Usadel B

   (2014) Trimmomatic: a flexible trimmer for illumina sequence data

   Bioinformatics 30:2114–2120.

   https://doi.org/10.1093/bioinformatics/btu170

   • PubMed
   • Google Scholar
6. 1. Bray NL
   2. Pimentel H
   3. Melsted P
   4. Pachter L

   (2016) Near-optimal probabilistic RNA-seq quantification

   Nature Biotechnology 34:525–527.

   https://doi.org/10.1038/nbt.3519

   • PubMed
   • Google Scholar
7. 1. Broughton RE
   2. Milam JE
   3. Roe BA

   (2001) The complete sequence of the zebrafish (Danio rerio) mitochondrial genome and evolutionary patterns in vertebrate mitochondrial DNA

   Genome Research 11:1958–1967.

   https://doi.org/10.1101/gr.156801

   • PubMed
   • Google Scholar
8. 1. Carten JD
   2. Bradford MK
   3. Farber SA

   (2011) Visualizing digestive organ morphology and function using differential fatty acid metabolism in live zebrafish

   Developmental Biology 360:276–285.

   https://doi.org/10.1016/j.ydbio.2011.09.010

   • PubMed
   • Google Scholar
9. 1. Clark KJ
   2. Balciunas D
   3. Pogoda H-M
   4. Ding Y
   5. Westcot SE
   6. Bedell VM
   7. Greenwood TM
   8. Urban MD
   9. Skuster KJ
   10. Petzold AM
   11. Ni J
   12. Nielsen AL
   13. Patowary A
   14. Scaria V
   15. Sivasubbu S
   16. Xu X
   17. Hammerschmidt M
   18. Ekker SC

   (2011) In vivo protein trapping produces a functional expression Codex of the vertebrate proteome

   Nature Methods 8:506–515.

   https://doi.org/10.1038/nmeth.1606

   • PubMed
   • Google Scholar
10. 1. Cogliati S
    2. Enriquez JA
    3. Scorrano L

    (2016) Mitochondrial cristae: where beauty meets functionality

    Trends in Biochemical Sciences 41:261–273.

    https://doi.org/10.1016/j.tibs.2016.01.001

    • PubMed
    • Google Scholar
11. 1. Cui J
    2. Wang L
    3. Ren X
    4. Zhang Y
    5. Zhang H

    (2019) Lrpprc: a multifunctional protein involved in energy metabolism and human disease

    Frontiers in Physiology 10:595.

    https://doi.org/10.3389/fphys.2019.00595

    • PubMed
    • Google Scholar
12. 1. Cuillerier A
    2. Honarmand S
    3. Cadete VJJ
    4. Ruiz M
    5. Forest A
    6. Deschênes S
    7. Beauchamp C
    8. LSFC Consortium
    9. Charron G
    10. Rioux JD
    11. Des Rosiers C
    12. Shoubridge EA
    13. Burelle Y

    (2017) Loss of hepatic LRPPRC alters mitochondrial bioenergetics, regulation of permeability transition and trans-membrane ROS diffusion

    Human Molecular Genetics 26:3186–3201.

    https://doi.org/10.1093/hmg/ddx202

    • PubMed
    • Google Scholar
13. 1. Cuillerier A
    2. Ruiz M
    3. Daneault C
    4. Forest A
    5. Rossi J
    6. Vasam G
    7. Cairns G
    8. Cadete V
    9. Des Rosiers C
    10. Burelle Y
    11. LSFC Consortium

    (2021) Adaptive optimization of the OXPHOS assembly line partially compensates lrpprc-dependent mitochondrial translation defects in mice

    Communications Biology 4:989.

    https://doi.org/10.1038/s42003-021-02492-5

    • PubMed
    • Google Scholar
14. 1. Debray F-G
    2. Morin C
    3. Janvier A
    4. Villeneuve J
    5. Maranda B
    6. Laframboise R
    7. Lacroix J
    8. Decarie J-C
    9. Robitaille Y
    10. Lambert M
    11. Robinson BH
    12. Mitchell GA

    (2011) Lrpprc mutations cause a phenotypically distinct form of Leigh syndrome with cytochrome c oxidase deficiency

    Journal of Medical Genetics 48:183–189.

    https://doi.org/10.1136/jmg.2010.081976

    • PubMed
    • Google Scholar
15. 1. Domínguez-Pérez M
    2. Simoni-Nieves A
    3. Rosales P
    4. Nuño-Lámbarri N
    5. Rosas-Lemus M
    6. Souza V
    7. Miranda RU
    8. Bucio L
    9. Uribe Carvajal S
    10. Marquardt JU
    11. Seo D
    12. Gomez-Quiroz LE
    13. Gutiérrez-Ruiz MC

    (2019) Cholesterol burden in the liver induces mitochondrial dynamic changes and resistance to apoptosis

    Journal of Cellular Physiology 234:7213–7223.

    https://doi.org/10.1002/jcp.27474

    • PubMed
    • Google Scholar
16. 1. Gohil VM
    2. Nilsson R
    3. Belcher-Timme CA
    4. Luo B
    5. Root DE
    6. Mootha VK

    (2010) Mitochondrial and nuclear genomic responses to loss of LRPPRC expression

    The Journal of Biological Chemistry 285:13742–13747.

    https://doi.org/10.1074/jbc.M109.098400

    • PubMed
    • Google Scholar
17. 1. Grant CE
    2. Bailey TL
    3. Noble WS

    (2011) FIMO: scanning for occurrences of a given motif

    Bioinformatics 27:1017–1018.

    https://doi.org/10.1093/bioinformatics/btr064

    • PubMed
    • Google Scholar
18. 1. Halachmi N
    2. Lev Z

    (1996) The Sec1 family: a novel family of proteins involved in synaptic transmission and general secretion

    Journal of Neurochemistry 66:889–897.

    https://doi.org/10.1046/j.1471-4159.1996.66030889.x

    • PubMed
    • Google Scholar
19. 1. Hammani K
    2. Takenaka M
    3. Miranda R
    4. Barkan A

    (2016) A PPR protein in the PLS subfamily stabilizes the 5’-end of processed rpl16 mRNAs in maize chloroplasts

    Nucleic Acids Research 44:4278–4288.

    https://doi.org/10.1093/nar/gkw270

    • PubMed
    • Google Scholar
20. 1. Ichino N
    2. Serres MR
    3. Urban RM
    4. Urban MD
    5. Treichel AJ
    6. Schaefbauer KJ
    7. Tallant LE
    8. Varshney GK
    9. Skuster KJ
    10. McNulty MS
    11. Daby CL
    12. Wang Y
    13. Liao H-K
    14. El-Rass S
    15. Ding Y
    16. Liu W
    17. Anderson JL
    18. Wishman MD
    19. Sabharwal A
    20. Schimmenti LA
    21. Sivasubbu S
    22. Balciunas D
    23. Hammerschmidt M
    24. Farber SA
    25. Wen X-Y
    26. Xu X
    27. McGrail M
    28. Essner JJ
    29. Burgess SM
    30. Clark KJ
    31. Ekker SC

    (2020) Building the vertebrate Codex using the gene breaking protein trap library

    eLife 9:e54572.

    https://doi.org/10.7554/eLife.54572

    • PubMed
    • Google Scholar
21. 1. Iida R
    2. Yasuda T
    3. Tsubota E
    4. Takatsuka H
    5. Matsuki T
    6. Kishi K

    (2006) Human Mpv17-like protein is localized in peroxisomes and regulates expression of antioxidant enzymes

    Biochemical and Biophysical Research Communications 344:948–954.

    https://doi.org/10.1016/j.bbrc.2006.04.008

    • PubMed
    • Google Scholar
22. 1. Isono E
    2. Saeki Y
    3. Yokosawa H
    4. Toh-e A

    (2004) Rpn7 is required for the structural integrity of the 26 S proteasome of Saccharomyces cerevisiae

    The Journal of Biological Chemistry 279:27168–27176.

    https://doi.org/10.1074/jbc.M314231200

    • PubMed
    • Google Scholar
23. 1. Jiao Y
    2. Lu Y
    3. Li X

    (2015) Farnesoid X receptor: a master regulator of hepatic triglyceride and glucose homeostasis

    Acta Pharmacologica Sinica 36:44–50.

    https://doi.org/10.1038/aps.2014.116

    • PubMed
    • Google Scholar
24. 1. Karpenahalli MR
    2. Lupas AN
    3. Söding J

    (2007) TPRpred: a tool for prediction of TPR-, PPR- and SEL1-like repeats from protein sequences

    BMC Bioinformatics 8:2.

    https://doi.org/10.1186/1471-2105-8-2

    • PubMed
    • Google Scholar
25. 1. Kaur G
    2. Iyer LM
    3. Burroughs AM
    4. Aravind L

    (2021) Bacterial death and TRADD-N domains help define novel apoptosis and immunity mechanisms shared by prokaryotes and metazoans

    eLife 10:e70394.

    https://doi.org/10.7554/eLife.70394

    • PubMed
    • Google Scholar
26. 1. Kim MJ
    2. Kang KH
    3. Kim CH
    4. Choi SY

    (2008) Real-Time imaging of mitochondria in transgenic zebrafish expressing mitochondrially targeted GFP

    BioTechniques 45:331–334.

    https://doi.org/10.2144/000112909

    • PubMed
    • Google Scholar
27. 1. Kim S-H
    2. Scott SA
    3. Bennett MJ
    4. Carson RP
    5. Fessel J
    6. Brown HA
    7. Ess KC

    (2013) Multi-Organ abnormalities and mTORC1 activation in zebrafish model of multiple acyl-CoA dehydrogenase deficiency

    PLOS Genetics 9:e1003563.

    https://doi.org/10.1371/journal.pgen.1003563

    • PubMed
    • Google Scholar
28. 1. Köhler F
    2. Müller-Rischart AK
    3. Conradt B
    4. Rolland SG

    (2015) The loss of LRPPRC function induces the mitochondrial unfolded protein response

    Aging 7:701–717.

    https://doi.org/10.18632/aging.100812

    • PubMed
    • Google Scholar
29. 1. Kondadi AK
    2. Anand R
    3. Hänsch S
    4. Urbach J
    5. Zobel T
    6. Wolf DM
    7. Segawa M
    8. Liesa M
    9. Shirihai OS
    10. Weidtkamp-Peters S
    11. Reichert AS

    (2020) Cristae undergo continuous cycles of membrane remodelling in a MICOS-dependent manner

    EMBO Reports 21:e49776.

    https://doi.org/10.15252/embr.201949776

    • PubMed
    • Google Scholar
30. 1. Lambert CJ
    2. Freshner BC
    3. Chung A
    4. Stevenson TJ
    5. Bowles DM
    6. Samuel R
    7. Gale BK
    8. Bonkowsky JL

    (2018) An automated system for rapid cellular extraction from live zebrafish embryos and larvae: development and application to genotyping

    PLOS ONE 13:e0193180.

    https://doi.org/10.1371/journal.pone.0193180

    • PubMed
    • Google Scholar
31. 1. Liu L
    2. McKeehan WL

    (2002) Sequence analysis of LRPPRC and its Sec1 domain interaction partners suggests roles in cytoskeletal organization, vesicular trafficking, nucleocytosolic shuttling, and chromosome activity

    Genomics 79:124–136.

    https://doi.org/10.1006/geno.2001.6679

    • PubMed
    • Google Scholar
32. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-Seq data with deseq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
33. 1. Mesitov MV
    2. Yu T
    3. Suematsu T
    4. Sement FM
    5. Zhang L
    6. Yu C
    7. Huang L
    8. Aphasizheva I

    (2019) Pentatricopeptide repeat poly (a) binding protein KPAF4 stabilizes mitochondrial mRNAs in Trypanosoma brucei

    Nature Communications 10:146.

    https://doi.org/10.1038/s41467-018-08137-2

    • PubMed
    • Google Scholar
34. 1. Mi H
    2. Muruganujan A
    3. Huang X
    4. Ebert D
    5. Mills C
    6. Guo X
    7. Thomas PD

    (2019) Protocol update for large-scale genome and gene function analysis with the Panther classification system (v.14.0)

    Nature Protocols 14:703–721.

    https://doi.org/10.1038/s41596-019-0128-8

    • PubMed
    • Google Scholar
35. 1. Mili S
    2. Piñol-Roma S

    (2003) Lrp130, a pentatricopeptide motif protein with a noncanonical RNA-binding domain, is bound in vivo to mitochondrial and nuclear RNAs

    Molecular and Cellular Biology 23:4972–4982.

    https://doi.org/10.1128/MCB.23.14.4972-4982.2003

    • PubMed
    • Google Scholar
36. 1. Mootha VK
    2. Lepage P
    3. Miller K
    4. Bunkenborg J
    5. Reich M
    6. Hjerrild M
    7. Delmonte T
    8. Villeneuve A
    9. Sladek R
    10. Xu F
    11. Mitchell GA
    12. Morin C
    13. Mann M
    14. Hudson TJ
    15. Robinson B
    16. Rioux JD
    17. Lander ES

    (2003) Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics

    PNAS 100:605–610.

    https://doi.org/10.1073/pnas.242716699

    • PubMed
    • Google Scholar
37. 1. Morin C
    2. Mitchell G
    3. Larochelle J
    4. Lambert M
    5. Ogier H
    6. Robinson BH
    7. De Braekeleer M

    (1993)

    Clinical, metabolic, and genetic aspects of cytochrome c oxidase deficiency in Saguenay-Lac-Saint-Jean

    American Journal of Human Genetics 53:488–496.

    • PubMed
    • Google Scholar
38. 1. Morino K
    2. Petersen KF
    3. Shulman GI

    (2006) Molecular mechanisms of insulin resistance in humans and their potential links with mitochondrial dysfunction

    Diabetes 55 Suppl 2:S9–S15.

    https://doi.org/10.2337/db06-S002

    • PubMed
    • Google Scholar
39. 1. Ni TT
    2. Lu J
    3. Zhu M
    4. Maddison LA
    5. Boyd KL
    6. Huskey L
    7. Ju B
    8. Hesselson D
    9. Zhong TP
    10. Page-McCaw PS
    11. Stainier DY
    12. Chen W

    (2012) Conditional control of gene function by an invertible gene trap in zebrafish

    PNAS 109:15389–15394.

    https://doi.org/10.1073/pnas.1206131109

    • PubMed
    • Google Scholar
40. 1. Oláhová M
    2. Hardy SA
    3. Hall J
    4. Yarham JW
    5. Haack TB
    6. Wilson WC
    7. Alston CL
    8. He L
    9. Aznauryan E
    10. Brown RM
    11. Brown GK
    12. Morris AAM
    13. Mundy H
    14. Broomfield A
    15. Barbosa IA
    16. Simpson MA
    17. Deshpande C
    18. Moeslinger D
    19. Koch J
    20. Stettner GM
    21. Bonnen PE
    22. Prokisch H
    23. Lightowlers RN
    24. McFarland R
    25. Chrzanowska-Lightowlers ZMA
    26. Taylor RW

    (2015) Lrpprc mutations cause early-onset multisystem mitochondrial disease outside of the French-Canadian population

    Brain 138:3503–3519.

    https://doi.org/10.1093/brain/awv291

    • PubMed
    • Google Scholar
41. 1. Pauter AM
    2. Olsson P
    3. Asadi A
    4. Herslöf B
    5. Csikasz RI
    6. Zadravec D
    7. Jacobsson A

    (2014) Elovl2 ablation demonstrates that systemic DHA is endogenously produced and is essential for lipid homeostasis in mice

    Journal of Lipid Research 55:718–728.

    https://doi.org/10.1194/jlr.M046151

    • PubMed
    • Google Scholar
42. 1. Prikryl J
    2. Rojas M
    3. Schuster G
    4. Barkan A

    (2011) Mechanism of RNA stabilization and translational activation by a pentatricopeptide repeat protein

    PNAS 108:415–420.

    https://doi.org/10.1073/pnas.1012076108

    • PubMed
    • Google Scholar
43. 1. Quinlivan VH
    2. Wilson MH
    3. Ruzicka J
    4. Farber SA

    (2017) An HPLC-CAD/fluorescence lipidomics platform using fluorescent fatty acids as metabolic tracers

    Journal of Lipid Research 58:1008–1020.

    https://doi.org/10.1194/jlr.D072918

    • PubMed
    • Google Scholar
44. 1. Rath S
    2. Sharma R
    3. Gupta R
    4. Ast T
    5. Chan C
    6. Durham TJ
    7. Goodman RP
    8. Grabarek Z
    9. Haas ME
    10. Hung WHW
    11. Joshi PR
    12. Jourdain AA
    13. Kim SH
    14. Kotrys AV
    15. Lam SS
    16. McCoy JG
    17. Meisel JD
    18. Miranda M
    19. Panda A
    20. Patgiri A
    21. Rogers R
    22. Sadre S
    23. Shah H
    24. Skinner OS
    25. To T-L
    26. Walker MA
    27. Wang H
    28. Ward PS
    29. Wengrod J
    30. Yuan C-C
    31. Calvo SE
    32. Mootha VK

    (2021) MitoCarta3.0: an updated mitochondrial proteome now with sub-organelle localization and pathway annotations

    Nucleic Acids Research 49:D1541–D1547.

    https://doi.org/10.1093/nar/gkaa1011

    • PubMed
    • Google Scholar
45. 1. Ruiz M
    2. Cuillerier A
    3. Daneault C
    4. Deschênes S
    5. Frayne IR
    6. Bouchard B
    7. Forest A
    8. Legault JT
    9. LSFC Consortium
    10. Vaz FM
    11. Rioux JD
    12. Burelle Y
    13. Des Rosiers C

    (2019) Lipidomics unveils lipid dyshomeostasis and low circulating plasmalogens as biomarkers in a monogenic mitochondrial disorder

    JCI Insight 4:123231.

    https://doi.org/10.1172/jci.insight.123231

    • PubMed
    • Google Scholar
46. Preprint

    1. Sabharwal A
    2. Campbell JM
    3. WareJoncas Z
    4. Wishman M
    5. Ata H
    6. Liu W
    7. Ichino N
    8. Bergren JD
    9. Urban MD
    10. Urban R
    11. Poshusta TL
    12. Ding Y
    13. Xu X
    14. Clark KJ
    15. Ekker SC

    (2019a) A Primer Genetic Toolkit for Exploring Mitochondrial Biology and Disease Using Zebrafish

    bioRxiv.

    https://doi.org/10.1101/542084

    • Google Scholar
47. 1. Sabharwal A
    2. Sharma D
    3. Vellarikkal SK
    4. Jayarajan R
    5. Verma A
    6. Senthivel V
    7. Scaria V
    8. Sivasubbu S

    (2019b) Organellar transcriptome sequencing reveals mitochondrial localization of nuclear encoded transcripts

    Mitochondrion 46:59–68.

    https://doi.org/10.1016/j.mito.2018.02.007

    • PubMed
    • Google Scholar
48. Preprint

    1. Sabharwal A
    2. Wishman MD
    3. Cervera RL
    4. Serres MR
    5. Anderson JL
    6. Treichel AJ
    7. Ichino N
    8. Liu W
    9. Yang J
    10. Ding Y
    11. Deng Y
    12. Farber SA
    13. Clark KJ
    14. Xu X
    15. Ekker SC

    (2020) A Genetic Model Therapy Proposes A Critical Role for Liver Dysfunction in Mitochondrial Biology and Disease

    bioRxiv.

    https://doi.org/10.1101/2020.05.08.084681

    • Google Scholar
49. 1. Sabharwal A
    2. Kar B
    3. Restrepo-Castillo S
    4. Holmberg SR
    5. Mathew ND
    6. Kendall BL
    7. Cotter RP
    8. WareJoncas Z
    9. Seiler C
    10. Nakamaru-Ogiso E
    11. Clark KJ
    12. Ekker SC

    (2021) The fusx tale base editor (fusxtbe) for rapid mitochondrial DNA programming of human cells in vitro and zebrafish disease models in vivo

    The CRISPR Journal 4:799–821.

    https://doi.org/10.1089/crispr.2021.0061

    • PubMed
    • Google Scholar
50. 1. Sasarman F
    2. Nishimura T
    3. Antonicka H
    4. Weraarpachai W
    5. Shoubridge EA
    6. LSFC Consortium

    (2015) Tissue-Specific responses to the LRPPRC founder mutation in French Canadian Leigh syndrome

    Human Molecular Genetics 24:480–491.

    https://doi.org/10.1093/hmg/ddu468

    • PubMed
    • Google Scholar
51. 1. Sievers F
    2. Wilm A
    3. Dineen D
    4. Gibson TJ
    5. Karplus K
    6. Li W
    7. Lopez R
    8. McWilliam H
    9. Remmert M
    10. Söding J
    11. Thompson JD
    12. Higgins DG

    (2011) Fast, scalable generation of high-quality protein multiple sequence alignments using clustal omega

    Molecular Systems Biology 7:539.

    https://doi.org/10.1038/msb.2011.75

    • PubMed
    • Google Scholar
52. 1. Simões ICM
    2. Fontes A
    3. Pinton P
    4. Zischka H
    5. Wieckowski MR

    (2018) Mitochondria in non-alcoholic fatty liver disease

    The International Journal of Biochemistry & Cell Biology 95:93–99.

    https://doi.org/10.1016/j.biocel.2017.12.019

    • PubMed
    • Google Scholar
53. 1. Smith LL
    2. Beggs AH
    3. Gupta VA

    (2013) Analysis of skeletal muscle defects in larval zebrafish by birefringence and touch-evoke escape response assays

    Journal of Visualized Experiments e50925:e50925.

    https://doi.org/10.3791/50925

    • PubMed
    • Google Scholar
54. 1. Soneson C
    2. Love MI
    3. Robinson MD

    (2015) Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences

    F1000Research 4:1521.

    https://doi.org/10.12688/f1000research.7563.2

    • PubMed
    • Google Scholar
55. 1. Spåhr H
    2. Rozanska A
    3. Li X
    4. Atanassov I
    5. Lightowlers RN
    6. Chrzanowska-Lightowlers ZMA
    7. Rackham O
    8. Larsson N-G

    (2016) SLIRP stabilizes LRPPRC via an RRM-PPR protein interface

    Nucleic Acids Research 44:6868–6882.

    https://doi.org/10.1093/nar/gkw575

    • PubMed
    • Google Scholar
56. 1. Spinelli JB
    2. Haigis MC

    (2018) The multifaceted contributions of mitochondria to cellular metabolism

    Nature Cell Biology 20:745–754.

    https://doi.org/10.1038/s41556-018-0124-1

    • PubMed
    • Google Scholar
57. 1. Steele SL
    2. Prykhozhij SV
    3. Berman JN

    (2014) Zebrafish as a model system for mitochondrial biology and diseases

    Translational Research 163:79–98.

    https://doi.org/10.1016/j.trsl.2013.08.008

    • PubMed
    • Google Scholar
58. 1. Stöckel D
    2. Kehl T
    3. Trampert P
    4. Schneider L
    5. Backes C
    6. Ludwig N
    7. Gerasch A
    8. Kaufmann M
    9. Gessler M
    10. Graf N
    11. Meese E
    12. Keller A
    13. Lenhof HP

    (2016) Multi-omics enrichment analysis using the genetrail2 web service

    Bioinformatics 32:1502–1508.

    https://doi.org/10.1093/bioinformatics/btv770

    • PubMed
    • Google Scholar
59. 1. Sun S
    2. Li H
    3. Chen J
    4. Qian Q

    (2017) Lactic acid: no longer an inert and end-product of glycolysis

    Physiology 32:453–463.

    https://doi.org/10.1152/physiol.00016.2017

    • PubMed
    • Google Scholar
60. 1. Takenaka M
    2. Zehrmann A
    3. Brennicke A
    4. Graichen K

    (2013) Improved computational target site prediction for pentatricopeptide repeat RNA editing factors

    PLOS ONE 8:e65343.

    https://doi.org/10.1371/journal.pone.0065343

    • PubMed
    • Google Scholar
61. 1. Vafai SB
    2. Mootha VK

    (2012) Mitochondrial disorders as windows into an ancient organelle

    Nature 491:374–383.

    https://doi.org/10.1038/nature11707

    • PubMed
    • Google Scholar
62. 1. Waterhouse AM
    2. Procter JB
    3. Martin DMA
    4. Clamp M
    5. Barton GJ

    (2009) Jalview version 2 -- a multiple sequence alignment editor and analysis workbench

    Bioinformatics 25:1189–1191.

    https://doi.org/10.1093/bioinformatics/btp033

    • PubMed
    • Google Scholar
63. 1. Wilkinson RN
    2. Elworthy S
    3. Ingham PW
    4. van Eeden FJM

    (2017) Fin clipping and genotyping embryonic zebrafish at 3 days post-fertilization

    BioTechniques 62:xxiv.

    https://doi.org/10.2144/000114509

    • Google Scholar
64. 1. Wilson MH
    2. Rajan S
    3. Danoff A
    4. White RJ
    5. Hensley MR
    6. Quinlivan VH
    7. Recacha R
    8. Thierer JH
    9. Tan FJ
    10. Busch-Nentwich EM
    11. Ruddock L
    12. Hussain MM
    13. Farber SA

    (2020) A point mutation decouples the lipid transfer activities of microsomal triglyceride transfer protein

    PLOS Genetics 16:e1008941.

    https://doi.org/10.1371/journal.pgen.1008941

    • PubMed
    • Google Scholar
65. 1. Wu J
    2. Choi T-Y
    3. Shin D

    (2017) Tomm22 knockdown-mediated hepatocyte damages elicit both the formation of hybrid hepatocytes and biliary conversion to hepatocytes in zebrafish larvae

    Gene Expression 17:237–249.

    https://doi.org/10.3727/105221617X695195

    • PubMed
    • Google Scholar
66. 1. Xu F
    2. Morin C
    3. Mitchell G
    4. Ackerley C
    5. Robinson BH

    (2004) The role of the LRPPRC (leucine-rich pentatricopeptide repeat cassette) gene in cytochrome oxidase assembly: mutation causes lowered levels of COX (cytochrome c oxidase) I and COX III mRNA

    The Biochemical Journal 382:331–336.

    https://doi.org/10.1042/BJ20040469

    • PubMed
    • Google Scholar
67. 1. Xu F
    2. Addis JBL
    3. Cameron JM
    4. Robinson BH

    (2012) Lrpprc mutation suppresses cytochrome oxidase activity by altering mitochondrial RNA transcript stability in a mouse model

    The Biochemical Journal 441:275–283.

    https://doi.org/10.1042/BJ20110985

    • PubMed
    • Google Scholar
68. 1. Yin P
    2. Li Q
    3. Yan C
    4. Liu Y
    5. Liu J
    6. Yu F
    7. Wang Z
    8. Long J
    9. He J
    10. Wang H-W
    11. Wang J
    12. Zhu J-K
    13. Shi Y
    14. Yan N

    (2013) Structural basis for the modular recognition of single-stranded RNA by PPR proteins

    Nature 504:168–171.

    https://doi.org/10.1038/nature12651

    • PubMed
    • Google Scholar
69. 1. Zeituni EM
    2. Wilson MH
    3. Zheng X
    4. Iglesias PA
    5. Sepanski MA
    6. Siddiqi MA
    7. Anderson JL
    8. Zheng Y
    9. Farber SA

    (2016) Endoplasmic reticulum lipid flux influences enterocyte nuclear morphology and lipid-dependent transcriptional responses

    The Journal of Biological Chemistry 291:23804–23816.

    https://doi.org/10.1074/jbc.M116.749358

    • PubMed
    • Google Scholar
70. 1. Zhang X
    2. Zhang Z
    3. Zhao Q
    4. Lou X

    (2020) Rapid and efficient live zebrafish embryo genotyping

    Zebrafish 17:56–58.

    https://doi.org/10.1089/zeb.2019.1796

    • PubMed
    • Google Scholar

Author details

1. Ankit Sabharwal

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Contributed equally with

   Mark D Wishman and Roberto Lopez Cervera

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4355-0355

2. Mark D Wishman

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft

   Contributed equally with

   Ankit Sabharwal and Roberto Lopez Cervera

   Competing interests

   No competing interests declared

3. Roberto Lopez Cervera

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Conceptualization, Data curation, Validation, Investigation, Visualization, Methodology

   Contributed equally with

   Ankit Sabharwal and Mark D Wishman

   Competing interests

   No competing interests declared

4. MaKayla R Serres

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Data curation, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

5. Jennifer L Anderson

   Department of Embryology, Carnegie Institution for Science, Baltimore, United States

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft

   Competing interests

   No competing interests declared

6. Shannon R Holmberg

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

7. Bibekananda Kar

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Investigation, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

8. Anthony J Treichel

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4393-7034

9. Noriko Ichino

   Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

   Contribution

   Validation, Investigation, Visualization, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7009-8299

10. Weibin Liu

    1. Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States
    2. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

11. Jingchun Yang

    1. Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States
    2. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

12. Yonghe Ding

    1. Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States
    2. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Investigation, Visualization, Methodology

    Competing interests

    No competing interests declared

13. Yun Deng

    1. Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States
    2. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Validation, Investigation, Methodology

    Competing interests

    No competing interests declared

14. Jean M Lacey

    Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

15. William J Laxen

    Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

16. Perry R Loken

    Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

17. Devin Oglesbee

    Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

18. Steven A Farber

    Department of Embryology, Carnegie Institution for Science, Baltimore, United States

    Contribution

    Resources, Supervision, Funding acquisition, Investigation, Project administration

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8037-7312

19. Karl J Clark

    Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Conceptualization, Resources, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Project administration

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9637-0967

20. Xiaolei Xu

    1. Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States
    2. Division of Cardiovascular Diseases, Department of Medicine, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Formal analysis, Supervision, Investigation, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-4928-3422

21. Stephen C Ekker

    Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine, Rochester, United States

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    ekker.stephen@mayo.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-0726-4212

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