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- Brevini, T, et al.
(author)
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FXR inhibition may protect from SARS-CoV-2 infection by reducing ACE2
- 2023
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In: Nature. - : Springer Science and Business Media LLC. - 1476-4687 .- 0028-0836. ; 615:7950, s. 134-
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Journal article (peer-reviewed)abstract
- Preventing SARS-CoV-2 infection by modulating viral host receptors, such as angiotensin-converting enzyme 2 (ACE2)1, could represent a new chemoprophylactic approach for COVID-19 that complements vaccination2,3. However, the mechanisms that control the expression of ACE2 remain unclear. Here we show that the farnesoid X receptor (FXR) is a direct regulator of ACE2 transcription in several tissues affected by COVID-19, including the gastrointestinal and respiratory systems. We then use the over-the-counter compound z-guggulsterone and the off-patent drug ursodeoxycholic acid (UDCA) to reduce FXR signalling and downregulate ACE2 in human lung, cholangiocyte and intestinal organoids and in the corresponding tissues in mice and hamsters. We show that the UDCA-mediated downregulation of ACE2 reduces susceptibility to SARS-CoV-2 infection in vitro, in vivo and in human lungs and livers perfused ex situ. Furthermore, we reveal that UDCA reduces the expression of ACE2 in the nasal epithelium in humans. Finally, we identify a correlation between UDCA treatment and positive clinical outcomes after SARS-CoV-2 infection using retrospective registry data, and confirm these findings in an independent validation cohort of recipients of liver transplants. In conclusion, we show that FXR has a role in controlling ACE2 expression and provide evidence that modulation of this pathway could be beneficial for reducing SARS-CoV-2 infection, paving the way for future clinical trials.
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- Matheson, GJ
(author)
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We need to talk about reliability: making better use of test-retest studies for study design and interpretation
- 2019
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In: PeerJ. - : PeerJ. - 2167-8359. ; 7, s. e6918-
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Journal article (peer-reviewed)abstract
- Neuroimaging, in addition to many other fields of clinical research, is both time-consuming and expensive, and recruitable patients can be scarce. These constraints limit the possibility of large-sample experimental designs, and often lead to statistically underpowered studies. This problem is exacerbated by the use of outcome measures whose accuracy is sometimes insufficient to answer the scientific questions posed. Reliability is usually assessed in validation studies using healthy participants, however these results are often not easily applicable to clinical studies examining different populations. I present a new method and tools for using summary statistics from previously published test-retest studies to approximate the reliability of outcomes in new samples. In this way, the feasibility of a new study can be assessed during planning stages, and before collecting any new data. An R package called relfeas also accompanies this article for performing these calculations. In summary, these methods and tools will allow researchers to avoid performing costly studies which are, by virtue of their design, unlikely to yield informative conclusions.
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