NEW FINDINGS — IN THE CLINIC
Rethinking Pain Through Machine Learning
Finding Answers with Long-Read Sequencing
Pain is a complex experience that involves both physical and emotion- al components. Traditional pain assessments, such as rating pain on a scale, are often too simplistic and are not individualized. A new study used machine learning to measure pain in individuals and determine the proportion of their reported pain that was due to physiological versus other factors.
Short-read genome sequencing (srGS) has been used for years as a powerful tool to diagnose rare diseases. However, many patients with suspected genetic conditions remain undiagnosed after testing. In a study conducted by scientists, 96 patients who had previously undergone srGS without identifying a genetic cause were re-evaluat- ed using a more advanced technique—long-read genome sequencing (lrGS). Unlike short-read sequencing, which examines shorter DNA fragments, lrGS reads longer strands of DNA, offering a more accurate and comprehensive view of the genome. The researchers found that lrGS uncovered new, disease- related genetic changes in about 17% of the patients, with half of those being classified as likely or definitely disease-causing. While some of these genetic changes could have been detected by srGS, they were not initially recognized as significant, often due to evolving scientific understanding of certain genes and their association with diseases. In seven cases, however, lrGS identified genetic changes variations, insertions, deletions, inversions, and repeat expansions, which are challenging for older sequencing methods to detect. This study highlights that lrGS is more effective than srGS in identifying complex genetic changes that can lead to rare diseases. entirely missed by srGS— such as large structural As lrGS technology continues to advance and become more accessible, it holds promise for diagnosing more patients with rare genetic disorders. n REFERENCE: Hiatt, S.M., et al. Long-read genome sequencing and variant reanalysis increase diagnostic yield in neurodevelopmental disorders. Genome Research [2024] 34:1747-1762 https://doi.org/10.1101/gr.279227.124
The study analyzed data from individuals with chronic pain and healthy volunteers. Researchers collected information on the expe- rience of specific pain stimuli from brain and skin signals along with psychological and social factors such as age, depression and anxiety, sleep quality, perceived health, history of sick leave, and other demo- graphic variables. Using machine learning, they identified patterns of physical response to pain and distinguished this physiologic response from subjective experiences of pain with high accuracy. From this data, two innovative metrics were developed. An individual’s Ф value measures their physical response to pain, while TIP identifies how emotional and social factors influence their pain perception. Higher TIP scores were found in individuals with frequent sick leave, stronger anxiety and depression, and a history of chronic pain. The researchers suggest that measures of both physiological and subjective pain components should be included in patient care to identify those who would benefit from medication treatment, psychosocial support, or both. By combining technology with an understanding of the emotional and physical aspects of pain, this research represents a potential step forward in improving care for people living with chronic pain. n REFERENCE: Gozzi, N. et al. Unraveling the physiological and psychosocial signatures of pain by machine learning. Med [2024] 5:1495-1509 https://doi.org/10.1016/j.medj.2024.07.016 spread in cell cultures. It continued to spread when tested in infected mice, especially in brain and nerve tissues. Remarkably, it continued to mix with the virus, making the engineered strain increasingly prevalent. Researchers tested whether the gene drive was effective against dormant HSV-1, which hides in the body and can reactivate later. Although the spread was reduced, the gene drive successfully mixed with the dormant virus when it became active again. This discovery demonstrates the feasibility of using viral gene drives to treat herpes infections, potentially targeting the virus in both its active and dormant states. It represents a significant step toward developing more effective treatments for HSV-1 and other persistent viral infections that current antiviral therapies cannot eliminate. n REFERENCE: Walter, M., et al. Viral gene drive spread during herpes simplex virus infection in mice. Nature Communications [2024] 15, 8161 https://doi.org/10.1038/s41467-024-52395-2
The laboratory of HudsonAlpha faculty researcher Greg Cooper, PhD, contributed to this work.
Gene Drives and Virus Control In typical sexual reproduction, genes are passed down ran- domly, much like flipping a coin. Gene drives are elements that increase the likelihood that a specific gene will be inherited, es- sentially like using a loaded coin. Gene drives were discovered in insects. Researchers have leveraged gene drives to create malaria-resistant mosquitoes that are currently being tested as a possible solution to controlling the deadly disease. Recently, scientists explored the concept of gene drives to create a new way to fight herpes simplex virus 1 (HSV-1). Viral gene drives were designed to spread changes through a virus population by mixing engineered DNA and natural viruses during infections. The gene drive worked well in lab experiments, replacing up to 95% of the natural virus without stopping its ability to
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