Biology subjects

Newcomb, C. E.

Publications and source records attributed to Newcomb, C. E..

5 recordsLinked to original sources

bioRxiv2025

Transcriptomic analysis of cells following decreased mitochondrial DNA-copy number reveals compensatory mechanisms in mitochondrial DNA replication and cellular energetics

Mitochondrial DNA copy number (mtDNA-CN) is a metric of mitochondrial function that has been associated with a variety of diseases including cardiovascular disease and all-cause mortality. To investigate genes and pathways affected by mtDNA-CN variation, we perturbed HEK 293T cells with ethidium bromide to deplete mtDNA. Using RNASeq and methylation microarrays, we evaluated transcriptomic and methylomic changes in treated cell lines. We observed an 8-fold decrease in mtDNA-CN and compensatory shifts in mitochondrial transcription to support mtDNA replication. Nuclear transcriptomic and methylomic analysis highlighted changes in metabolic pathways, including oxidative phosphorylation and canonical glycolysis. Longitudinal analyses revealed that the identified genes and pathways have different response timing, with nuclear response lagging behind mitochondrial response. These findings further elucidate the mechanisms behind mtDNA maintenance and responses to cellular energetics as well as mitochondrial-nuclear crosstalk dynamics.

genetics↗

bioRxiv2021

Rare instances of non-random dropout with the monochrome multiplex qPCR assay for mitochondrial DNA copy number

Mitochondrial DNA copy number (mtDNA-CN) is a proxy for mitochondrial function and has been of increasing interest to the mitochondrial research community. There are a number of ways to measure mtDNA-CN, ranging from qPCR to whole genome sequencing [1]. A recent article in the Journal of Molecular Diagnostics [2] described a novel method for measuring mtDNA-CN that is both inexpensive and reproducible. After adapting the assay for use in our lab, we have found it to be reproducible and well-correlated with mtDNA-CN derived from whole genome sequencing. However, certain individuals show poor concordance between the two measures, particularly individuals with qPCR mtDNA-CN measurements >3 standard deviations below the sample mean, which corresponds to roughly 1% of assayed individuals (Figure 1). After examining whole genome sequencing data, this seems to be due to specific polymorphisms within the D-loop primer region, at positions MT 338, 340, 452, 457, 458, 460, 461, 466, and 467. All individuals with a variant in at least one of these positions have non-concordant mtDNA-CN measurements. Meanwhile, variants observed at other positions within the primer region do not appear to cause dropout. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/463983v1_fig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@134ca51org.highwire.dtl.DTLVardef@ce9196org.highwire.dtl.DTLVardef@1b82af6org.highwire.dtl.DTLVardef@c9701_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFigure 1.C_FLOATNO Discrepancy between the monochrome multiplex qPCR mtDNA-CN and the whole genome sequencing mtDNA-CN for 1,732 distinct individuals. Data are centered at 0 and scaled so that the standard deviation = 1. The dotted red line represents 3 standard deviations beneath the sample mean. Individuals in the U, L1, L4, and T haplogroups have a disproportionately higher risk of discordant measures between the two assays. C_FIG

genetics↗

bioRxiv2019

Mitochondrial DNA Copy Number (mtDNA-CN) Can Influence Mortality and Cardiovascular Disease via Methylation of Nuclear DNA CpGs

BackgroundMitochondrial DNA copy number (mtDNA-CN) has been associated with a variety of aging-related diseases, including all-cause mortality. However, the mechanism by which mtDNA-CN influences disease is not currently understood. One such mechanism may be through regulation of nuclear gene expression via the modification of nuclear DNA (nDNA) methylation. MethodsTo investigate this hypothesis, we assessed the relationship between mtDNA-CN and nDNA methylation in 2,507 African American (AA) and European American (EA) participants from the Atherosclerosis Risk in Communities (ARIC) study. To validate our findings we assayed an additional 2,528 participants from the Cardiovascular Health Study (CHS) (N=533) and Framingham Heart Study (FHS) (N=1,995). We further assessed the effect of experimental modification of mtDNA-CN through knockout of TFAM, a regulator of mtDNA replication, via CRISPR-Cas9. ResultsThirty-four independent CpGs were associated with mtDNA-CN at genome-wide significance (P<5x10-8). Meta-analysis across all cohorts identified six mtDNA-CN associated CpGs at genome-wide significance (P<5x10-8). Additionally, over half of these CpGs were associated with phenotypes known to be associated with mtDNA-CN, including coronary heart disease, cardiovascular disease, and mortality. Experimental modification of mtDNA-CN demonstrated that modulation of mtDNA-CN directly drives changes in nDNA methylation and gene expression of specific CpGs and nearby transcripts. Strikingly, the neuroactive ligand receptor interaction KEGG pathway was found to be highly overrepresented in the ARIC cohort (P= 5.24x10-12), as well as the TFAM knockout methylation (P=4.41x10-4) and expression (P=4.30x10-4) studies. ConclusionsThese results demonstrate that changes in mtDNA-CN influence nDNA methylation at specific loci and result in differential expression of specific genes that may impact human health and disease via altered cell signaling.

genomics↗

bioRxiv2019

The effect of sex and underlying disease on the genetic association of QT interval and sudden cardiac death

BackgroundSudden cardiac death (SCD) accounts for ~300,000 deaths annually in the US. Men have a higher risk of SCD and are more likely to have underlying coronary artery disease (CAD) than women. In contrast, women are more likely to have arrhythmic events in the setting of inherited or acquired QT prolongation. Moreover, there is evidence of sex differences in the underlying genetics of QT interval duration. Using sex- and CAD-stratified analyses, we assess differences in genetic association between prolonged QT interval and SCD risk.\n\nMethodsWe examined 2,282 SCD subjects with autopsy-confirmed underlying disease from the Fingesture cohort and 3,561 Finnish controls. The SCD subjects were stratified by underlying disease (ischemic vs. non-ischemic) and by sex. We used logistic regression to test for association between the top QT interval associated SNP, rs12143842 (in the NOS1AP locus), and SCD risk. We also performed Mendelian randomization to test for causal association of QT interval in the various subgroups.\n\nResultsFemale SCD victims with underlying non-ischemic disease had the strongest association between rs12143842 and SCD risk (OR=1.37; 95% CI, 1.07-1.75) and the strongest causal association, established using Mendelian randomization, between prolonged QT interval and SCD (OR in SCD risk per SD increase in QT, 3.60; 95% CI, 1.22-10.49). Ischemic SCD victims, irrespective of sex, did not show an association between rs12143842 and SCD risk or a causal association for QT interval.\n\nConclusionsThis study provides evidence that the causal effect of QT prolongation on SCD risk differs by sex and underlying disease.

genetics↗

bioRxiv2019

Evaluation of mitochondrial DNA copy number estimation techniques

Mitochondrial DNA copy number (mtDNA-CN), a measure of the number of mitochondrial genomes per cell, is a minimally invasive proxy measure for mitochondrial function and has been associated with several aging-related diseases. Although quantitative real-time PCR (qPCR) is the current gold standard method for measuring mtDNA-CN, mtDNA-CN can also be measured from genotyping microarray probe intensities and DNA sequencing read counts. To conduct a comprehensive examination on the performance of these methods, we use known mtDNA-CN correlates (age, sex, white blood cell count, Duffy locus genotype, incident cardiovascular disease) to evaluate mtDNA-CN calculated from qPCR, two microarray platforms, as well as whole genome (WGS) and whole exome sequence (WES) data across 1,085 participants from the Atherosclerosis Risk in Communities (ARIC) study and 3,489 participants from the Multi-Ethnic Study of Atherosclerosis (MESA). We observe mtDNA-CN derived from WGS data is significantly more associated with known correlates compared to all other methods (p < 0.001). Additionally, mtDNA-CN measured from WGS is on average more significantly associated with traits by 5.6 orders of magnitude and has effect size estimates 5.8 times more extreme than the current gold standard of qPCR. We further investigated the role of DNA extraction method on mtDNA-CN estimate reproducibility and found mtDNA-CN estimated from cell lysate is significantly less variable than traditional phenol-chloroform-isoamyl alcohol (p = 5.44x10-4) and silica-based column selection (p = 2.82x10-7). In conclusion, we recommend the field moves towards more accurate methods for mtDNA-CN, as well as re-analyze trait associations as more WGS data becomes available from larger initiatives such as TOPMed.

genetics↗