Misexpression of inactive genes in whole blood is associated with nearby rare structural variants

Publication: American Journal of Human Genetics

Thomas Vanderstichele, Katie L. Burnham, Niek de Klein, Michael Inouye, Dirk S. Paul, Emma E. Davenport et al

24 July 2024

Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors.

We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.

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Quantitative 23Na magnetic resonance imaging in the abdomen at 3 T

Publication: Magnetic Resonance Materials in Physics, Biology and Medicine

Jonathan Birchall, Ines Horvat-Menih, Joshua Kaggie, Arnold Benjamin, Martin Graves, Ian Wilkinson, Ferdia Gallagher, Mary McLean

1 June 2024

Summary

We estimated the sodium content and relaxation of organs within the abdomen of healthy human volunteers using magnetic resonance imaging (MRI). Existing techniques for measuring sodium content are non-specific or require an invasive biopsy. Clinical translation of sodium content monitoring may aid in diagnosis of disease such as cancer, chronic kidney disease and hypertension at earlier stages, and more regular monitoring may help to evaluate efficacy of treatment.

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Using machine learning to model older adult inpatient trajectories from electronic health records data

Publication: ScienceDirect

Maria Herrero-Zazo, Tomas Fitzgerald, Vince Taylor, Helen Street, Afzal N. Chaudhry, John R. Bradley, Ewan Birney, Victoria L. Keevil

20 January 2023


  • Time-series blood test & vital sign data from older inpatients were presented to HMM (Hidden Markov Models)
  • Hidden clinically interpretable states were extracted, linked with diagnoses and death
  • States modeled inpatient trajectories, differentiating risk from admission-discharge
  • The clinical interpretation of HMM states helped explain how ML models organise data

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Quantitative 3D imaging parameters improve prediction of hip osteoarthritis outcome

Publication: Scientific Reports

T. D. Turmezei, G. M. Treece, A. H. Gee, S. Sigurdsson, H. Jonsson, T. Aspelund, V. Gudnason & K. E. S. Poole

March 2020


Summary

Hip osteoarthritis is a very common condition that will affect up to 25% of the population in their lifetime. There is no cure for this painful and debilitating disease, with the mainstay of treatment currently being surgical replacement of the joint once it has become too stiff or painful to use. Research trials trying to find effective therapies for osteoarthritis currently rely on x-ray radiograph imaging to test if there have been any meaningful changes in the structure of the joint for a new therapy, but this method suffers from being unable to detect small changes reliably and from only being able to see the joint in 2D.

We developed the joint space mapping (JSM) technique in a collaboration between the Departments of Medicine and Engineering at the University of Cambridge and have since taken it to test on patient data from the widely regarded AGES-Reykjavik patient cohort of healthy older Icelandic adults.

Our research showed that JSM can identify structurally relevant disease features related to the important outcome of joint replacement in hip osteoarthritis better than the current clinical trial 2D imaging gold standards. This means that JSM could be a significantly better way of identifying who might be at high risk from getting hip osteoarthritis, those in whom the disease might be progressing rapidly, and whether any new therapy is effective at stopping the joint destruction that ultimately leads to joint failure. These results have been achieved by using an existing and readily available clinical imaging technique to look at the hip joint in 3D.

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