Na Cai Profile picture
Aug 23, 2021 12 tweets 10 min read Read on X
We identified common #mtDNA variants in 16K British individuals with EUR ancestry from the INTERVAL study, performed the first large-scale assoc study between #mtDNA variants and ~5000 molecular traits that directly tag metabolic processes. 2/n
We found and replicated significant associations between #mtDNA variants in Haplogroups Uk (found in 10% of the EUR population) and H3 (2%) and levels of N-formylmethionine (fMet), an amino acid known to be important for intra-mitochondrial translation. 3/n
These #mtDNA effects are independent on nuclear DNA effects on levels of fMet, and account for 5.9% of the variation in fMet levels between individuals, a high percentage, for such a small (16KB genome)! 4/n
As #mtDNA Haplogroup Uk has previously been shown to be protective against diseases such as #Parkinson’s (PD) and ischaemic stroke (IS), we investigated the molecular mechanisms that may mediate these protective effects. 5/n
We first show that #mtDNA variants in Haplogroup Uk regulate the expression of many #mtDNA genes encoding for proteins in the mitochondrial complexes, in a majority of tissues in the GTEx dataset. For eg, this figure shows effects of fMet associated variants on MT-ND3. 6/n
We then show using extensive experiments on cybrid cell lines (same nuclear DNA, different #mtDNA) that #mtDNA variants in Haplogroup Uk regulate protein synthesis and degradation in both mitochondria and cytoplasm, and this affects cellular processes beyond #bioenergetics. 7/n
As for whether and how increased fMet in Haplogroup Uk may mediate protective effects against diseases: First, we measured fMet in a clinical cohort of IS, and found part of the protective effect of Haplogroup Uk on IS may be attributed to fMet. 8/n
Second, we assessed the effects of fMet on the incidence of range of late-onset diseases in a prospective cohort with a 20-year follow-up EPIC-Norfolk, and found that fMet is associated with a wide range of diseases, and a potential biomarker for ageing and disease. 9/n
In summary, our went from quantitative genetics ➡️ cellular models ➡️ clinical cohorts ➡️ prospective study, and sheds new light on unknown mechanisms through which #mtDNA variation contributes to metabolic processes, protein proteostasis, and liability to complex diseases. 10/n
Our results support the need for broad and hypothesis-free investigations on the impact of #mtDNA variation and nuclear-mitochondrial interactions on ageing and disease. With greater sample size and power, we will be sure to discover more. 🧐11/n
This is truly to a team effort, and a great example of how interdisciplinary collaboration can lead to real insight!! ❤️❤️❤️

@aidanbutty @Claudia20755263 @pietznerm @kousikbioinfo @mjbonder and many many others :)

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Na Cai

Na Cai Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @caina89

Aug 16, 2022
New preprint! In this paper @andywdahl and I et al show how phenotype integration can improve GWAS power and PRS prediction for Major Depressive Disorder (MDD), and a new and simple metric for quantifying how specific these improvements are for MDD tinyurl.com/mry37nry
🧵 1/n
Bit of context: in 2020 we published a paper saying minimal phenotypes widely collected in biobanks and EHRs (eg self-reported depression, ICD10 codes) do not capture specific genetic effects for MDD, old tweetorial here:
2/n
It brought about a lot of constructive discussions: some lament the deepest MDD phenotype in UKB (LifetimeMDD, N=67K unrelated white British indivs) doesn't have enough N to find genetic signals, others say "surely there’s a way to use both shallow and deep phenotypes!" 🤔
3/n
Read 18 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Don't want to be a Premium member but still want to support us?

Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal

Or Donate anonymously using crypto!

Ethereum

0xfe58350B80634f60Fa6Dc149a72b4DFbc17D341E copy

Bitcoin

3ATGMxNzCUFzxpMCHL5sWSt4DVtS8UqXpi copy

Thank you for your support!

Follow Us!

:(