A landmark new study shows COVID-19 isn’t ‘just a cold’: One infection left people with long-lasting immune damage, and those with heart disease lost up to 70% of key immune cells. Reinfections may wo...

DMZ – WISSENSCHAFT ¦ A. Aeberhard ¦ Eine aktuelle Übersichtsarbeit aus der Slowakei beleuchtet die bisher wenig verstandenen kardiovaskulären Folgen von Long COVID (LC), der chronischen Folgeerkrankun...

DecodeME is the biggest study ever on myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). In our previousContinue readingGenes pointing to the brain: DecodeME part II

Background: Myalgic encephalomyelitis (also known as ME/CFS or simply ME) has severely impacted the lives of tens of millions of people globally, but the disease currently has no accurate diagnostic tools or effective treatments. Identifying the biological causes of ME has proven challenging due to its wide range of symptoms and affected organs, and the lack of reproducible genetic associations across ME populations. This has prolonged misunderstanding, lack of awareness, and denial of the disease, further harming patients. Methods: We used the PrecisionLife combinatorial analytics platform to identify disease signatures (i.e., combinations of 1-4 SNP-genotypes) that are significantly enriched in two cohorts of ME participants from DecodeME relative to controls from UK Biobank (UKB). We tested whether the number of these signatures possessed by an individual is significantly associated with increased prevalence of ME in a third disjoint cohort of DecodeME participants. We characterized a number of drug repurposing opportunities for a set of candidate core genes whose disease signatures had the strongest association with ME and which were linked to different mechanisms. We then tested gene overlap between the ME signatures identified and previous studies in long COVID, using two independent approaches to explore these shared genetic commonalities. Results: We identified 22,411 reproducible disease signatures, comprising combinations of 7,555 unique SNPs, that are consistently associated with increased prevalence of ME in three disjoint patient cohorts. The count of reproducible signatures was significantly associated with increased prevalence of ME (p = 4x10-21), and participants with a top 10% signature count had an odds ratio of disease 1.64 times greater than participants with a bottom 10% signature count, confirming that these genetic signatures increase susceptibility for developing ME. These disease signatures map to 2,311 genes. We identified substantial overlap between the genes found by this combinatorial analysis and previous studies. We found that the 259 candidate core genes most strongly associated with ME are enriched in disease mechanisms including neurological dysregulation, inflammation, cellular stress responses and calcium signaling. We demonstrated that 76 out of 180 genes previously linked to long COVID in UKB and the US All of Us cohorts are also significantly associated with ME in the DecodeME cohort. These findings allowed identification of many existing and novel repurposing opportunities, including candidates linked to several genes with shared etiology for long COVID. Conclusion: These findings provide further evidence that ME is a complex multisystemic condition where the risk of developing the disease has a very clear genetic and biological basis. They give a substantially deeper level of insight into the genetic risk factors and mechanisms involved in ME. The discovery of so many multiply reproducible genetic associations implies that ME is highly polygenic, which has important consequences for its future study and the delivery of clinical care to patients. The striking overlap in genes and mechanisms between long COVID and ME (76 / 180 long COVID genes tested) suggests the potential for development of novel or repurposed drug therapies that could be used to successfully treat either condition. However, although they share significant genetic commonalities, long COVID and ME appear to be best considered as partially overlapping but different diseases. ### Competing Interest Statement JMS, SD, MP, DK, ARM, LMF, MS, MAS and SG are employees of PrecisionLife Ltd. SG is a shareholder of PrecisionLife, Ltd. ### Funding Statement This study formed part of the LOCOME project and was funded in part by Innovate UK's Advancing Precision Medicine programme (10083274). ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Research described in this article has been conducted using data from DecodeME study and UK Biobank (application number 44288). The DecodeME study has approval from North West - Liverpool Central Research Ethics Committee (21/NW/0169). DecodeME is registered in the Research Registry (identifying number 6395). More information on the study is available online (www.decodeme.org). UK Biobank has approval from the North West Multi-centre Research Ethics Committee (MREC) as a Research Tissue Bank (RTB) approval, and researchers do not require separate ethical clearance and can operate under this RTB approval. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Only data from existing DecodeME and UK Biobank study cohorts were analyzed and no new source data were collected for this study. DecodeME data can be accessed by approved studies (https://institute-genetics-cancer.ed.ac.uk/decodeme-the-worlds-largest-mecfs-study/researcher-access) and UK Biobank data can be accessed by approved registered users (https://www.ukbiobank.ac.uk/about-us/how-we-work/access-to-uk-biobank-data/). PrecisionLife will make the results of its analysis available and will support bona fide researchers in testing the drug repurposing candidates identified.

Long-term consequences of SARS-CoV-2 infection are unknown since recovered individuals can experience symptoms and latent viral reactivation for months. Indeed, acute post-infection sequelae have als....

“You Do You”—unless you are trying to protect yourself from Covid