University of Skövde

A combination of only 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals. The identified proteins could be used to tailor treatments to the individual based on the expected severity of the disease. A combination of only 11 proteins can predict long-term disability outcomes in multiple sclerosis (MS) for different individuals. The identified proteins could be used to tailor treatments to the individual based on the expected severity of the disease. The study, led by researchers at Linköping University in Sweden, has been published in the journal Nature Communications. "A combination of 11 proteins predicted both short and long-term disease activity and disability outcomes. We also concluded that it's important to measure these proteins in cerebrospinal fluid, which better reflects what's going on in the central nervous system, compared with measuring in the blood," says Julia Åkesson, doctoral student at Linköping University and the University of Skövde. In multiple sclerosis, the immune system attacks the person's own body, damaging nerves in the brain and in the spinal cord. What is attacked primarily is a fatty compound called myelin, which surrounds and insulates the nerve axons so that signals can be transmitted. When myelin is damaged, transmission becomes less efficient. Disease progression in multiple sclerosis varies considerably from person to person. To those for whom a more severe disease is predicted, it is important not to lose valuable time at the onset of the disease but to get the right treatment quickly. The researchers behind the current study, which is a collaboration between Linköping University, the Karolinska Institute and the University of Skövde, wanted to find out whether it was possible to detect at an early stage of disease which patients would require a more powerful treatment. Being able to do so would be relevant both to physicians and those living with MS. "I think we've come one step closer to an analysis tool for selecting which patients would need more effective treatment in an early stage of the disease. But such a treatment may have side effects and be relatively expensive, and some patients don't need it," says Mika Gustafsson, professor of bioinformatics at the Department of Physics, Chemistry and Biology at Linköping University, who led the study. Finding markers linked to disease severity many years ahead is a complicated challenge. In their study, the researchers analysed nearly 1,500 proteins in samples from 92 people with suspected or recently diagnosed MS. Data from the protein analyses were combined with a large amount of information from the patients' journals, such as disability, results from MRI scans of the nervous system, and treatments received. Using machine learning, the researchers found a number of proteins that could predict disease progression. "Having a panel consisting of only 11 proteins makes it easy should anyone want to develop analysis for this. It won't be as costly as measuring 1,500 proteins, so we've really narrowed it down to make it useful for others wanting to take this further," says Sara Hojjati, doctoral student at the Department of Biomedical and Clinical Sciences at Linköping University. The research team also found that a specific protein, leaking from damaged nerve axons, is a reliable biomarker for disease activity in the short term. This protein is called neurofilament light chain, NfL. These findings confirm earlier research on the use of NfL to identify nerve damage and also suggest that the protein indicates how active the disease is. One of the main strengths of the study is that the combination of proteins found in the patient group from which samples were taken at Linköping University Hospital was later confirmed in a separate group consisting of 51 MS patients sampled at the Karolinska University Hospital in Stockholm. This study is the first to measure such a large amount of proteins with a highly sensitive method, proximity extension assay, combined with next-generation sequencing, PEA-NGS. This technology allows for high-accuracy measuring also of very small amounts, which is important as these proteins are often present in very low levels. The study was funded by the Swedish Foundation for Strategic Research, the Swedish Brain Foundation, Knut and Alice Wallenberg Foundation, Margaretha af Ugglas Foundation, the Swedish Research Council, NEURO Sweden and the Swedish Foundation for MS research, and others.

The touch of another person may increase levels of the 'feelgood' hormone oxytocin. But the context really matters. The situation impacts oxytocin levels not only in the moment, but also later. The touch of another person may increase levels of the "feelgood" hormone oxytocin. But the context really matters. The situation impacts oxytocin levels not only in the moment, but also later, as is shown by researchers at Linköping University and the University of Skövde in Sweden. Their study has been published in the scientific journal eLife. An embrace from a parent, a warm hand on your shoulder or a caress from a romantic partner are examples of how touch can strengthen social bonds between people and influence emotions. But although touch and the sense of touch have a very important function, knowledge of how this actually works is still lacking. Studies in animals have shown that the hormone oxytocin is linked to touch and social bonding. However, many questions remain unanswered when it comes to oxytocin's role in human social interactions and how this hormone can influence and be influenced by the brain. To study this closer, researchers have examined what happens in the body when we feel a soft touch. "We saw that the body's oxytocin response to touch was influenced by the situation: what had happened a few moments earlier and with whom the interaction takes place. The hormone does not function like an on/off button, but more like a dimmer switch," says India Morrison, senior associate professor at the Department of Biomedical and Clinical Sciences at Linköping University. 42 women took part in the study, published in eLife. The actual experiment consisted of the woman's male partner stroking her arm with his hand, while her brain activity was monitored using functional magnetic resonance imaging, fMRI. The experiment also involved repeatedly taking blood tests to see whether oxytocin levels in the woman's blood changed over time. Combining the various measurements allowed the researchers to examine whether hormone levels were linked to brain activity. The measurements from the social interaction between the woman and her partner were compared with what happened when instead an unknown, non-threatening man touched her arm in the same way. In half of the experiments, her partner was the first to stroke her arm, and in the other half it was the stranger. The participating women were informed of who was stroking their arm. "Our basic question was whether oxytocin levels would be higher when the woman's partner touched her arm than when a stranger did it. The answer was yes, but only when her partner was the first to stroke her arm," says India Morrison. The researchers found that when her partner was first, the women's oxytocin levels increased during the social interaction, then fell, only to increase again when the stranger did the same thing. However, when the stranger touched her first, there was no change in oxytocin levels. And when her partner then stroked her arm, there was only a slight increase. The changes in oxytocin levels were linked to activity in regions of the brain important for the contextualisation of events. Oxytocin is released in a variety of situations and has several functions in the body. "It might be good to bear in mind that context matters, for instance when providing synthetic oxytocin in the form of a nasal spray as part of the treatment of mood-affecting conditions," says India Morrison. The study was supported by the Swedish Research Council.

GOTHENBURG, Sweden, Oct. 20, 2022 /PRNewswire/ -- As part of a 320m SEK investment program in biological therapies, one research project will play a pivotal role in making Sweden a major leader in Advanced Therapeutic Medical Products (ATMPs) by 2030. Huge investments are being made in the development of cell-based therapies, and a new Vinnova funded project called Transfer learning across technologies for ATMPs (Advanced Therapy Medicinal Products), starting in Gothenburg, Sweden, will spearhead this new and exciting field of drug development. ATMPs are therapies based on biological materials, such as genes and cells. They are the latest modality in drug development and will be able to cure diseases in a completely different way compared with previous methods. The collaborative project focuses on the development of an AI-based Quality Control (QC) method of cells for ATMP manufacturing, adapting it to industrial requirements and needs. It involves five project partners, Takara Bio Europe, TATAA Biocenter, MultiD, RISE and the University of Skövde, who all have strong needs for improving methods of QC testing for ATMP. Limitations of current QC methods QC methods for quality control of cell material today are inefficient, labour and resource intensive and rely on univariate data analysis. There are also difficulties in detecting poor cell quality early in the process, which can lead to higher costs. The project will develop a new AI-based QC method that uses gene expression patterns to classify cells. Two different types of data will be used, RNA sequencing data and qPCR data. By utilizing sequencing data for training the model and then fine-tuning the model with qPCR data, this new method will be more accurate, can be carried out early in the process (and repeatedly during development) and will be more cost-effective. The project group have complementary backgrounds and skills. Takara Bio Europe is an expert in stem cell technologies, GMP and ATMP development. RISE has expertise in single-cell qPCR and validation. TATAA Biocenter has considerable expertise in business development and are pioneers in single-cell technology. MultiD contribute to software development and the University of Skövde provide skills in AI, bioinformatics, and large-scale data analysis. The 2-year project starts in October 2022 and has long-term plans for commiseration offering an opportunity to develop a service for QC testing of ATMPs. Commercialisation of the AI QC Method The method will be implemented in the existing software, GenEx (developed by MultiD and distributed by ThermoFisher) providing access to a worldwide market. Longer term, the method has the prerequisites to become a recommended QC method that can be adapted for various ATMP applications. Results from the project will also identify new stem cell biomarkers, which can be commercialized in a stem cell assay and be included in TATAAs portfolio of assays. Vinnova is making major investments in ATMP with the goal that Sweden will become a major leader in ATMP by 2030. This project will be an important piece of the puzzle and will contribute greatly. CONTACT: Martin Cooke TATAA Biocenter Marketing & Comms Specialist Sofierogatan 3A 412 51 Göteborg, Sweden info@tataa.com The following files are available for download: