An international research team led by scientists at the Department of Medicine and Life Sciences (MELIS) at Pompeu Fabra University, in collaboration with Hospital del Mar, Hospital Clínic, Charité – Medical University of Berlin, and the universities of Oslo and Genoa, has developed a computational biology tool, based on multi-level network analysis that considers the relationship between basic biology and applied medicine, to achieve an integrated vision of multiple sclerosis. Their approach involves a network analysis of multiomic data—genomic, phosphoproteomic and cytomic—together with imaging and clinical outcomes.
The results of their study involving the analysis of data from hundreds of MS patients and controls, highlighted a pathway involving the kinase MK03, T cells, and retinal nerve fiber layer thickness as a predictor of disability, which could point to future strategies for personalized MS therapeutics.
“In this study we have analyzed five levels at once: genes, proteins, cells, parts of the brain and behavior,” explained Jordi Garcia-Ojalvo, PhD, professor of systems biology and director of the Dynamical Systems Biology Laboratory at the UPF Department of Medicine and Life Sciences. “The proximity of the elements of each level in each person has determined the connection between the elements within each level and between levels and, through Boolean dynamics, considering each element as being active or inactive, and the introduction of disturbances in the system, we have made the elements of the network oscillate. Thus, we have managed to identify which elements of the different levels are related at the biological level.”
The team says the tool could be applied to the study of other complex diseases such as Alzheimer’s disease and other types of dementia, as well as autoimmune disorders. Garcia-Ojalvo is co-senior and co-corresponding author of the team’s published paper in PLOS Computational Biology, titled “Multiscale networks in multiple sclerosis,” in which they authors concluded “The genes, proteins and cell paths explained variation in central nervous system damage, and in metrics of disease severity. Such multilayer paths explain the different phenotypes of the disease and can be developed as biomarkers of MS.”
Multiple sclerosis is an autoimmune disease that occurs when the immune system attacks the brain and spinal cord. The disorder is characterized by inflammatory attacks to the central nervous system (CNS), which damages the neural tissue and leads to significant disability. and covers a wide range of biological scales, ranging from genes and proteins to cells and tissues, passing through the entire organism.
Symptoms of multiple sclerosis vary among patients, but the most common range from vision problems, asthenia, difficulty walking and keeping balance, to numbness or weakness in the arms and legs. All of them can appear and disappear or last over time.
MS is a complex disease that is not always easy to diagnose. The authors further explained, Complex diseases such as MS involve the contribution of a wide range of biological processes, and “… the interaction of multiple biological scales, including tissues, cells, and molecules (genes, proteins, and metabolites), all of which regulate biological function and modulate the susceptibility to a given clinical phenotype.” But while significant efforts have been made to understand each of these levels, few attempts have succeeded in integrating multiple scales and the flow of information across them, the team continued. “Such integration would definitely improve our understanding of disease pathogenesis and wellness. Multilayer networks provide a framework to integrate complex biological data across different scales, which should allow us to understand the flow of biological information in health and disease.” This level of understanding would be particularly important for diseases such as MS, which have a complex genetic and molecular basis.
“In complex diseases, as in society, many things happen at once, and they do so on multiple scales and over time,” commented study co-lead Pablo Villoslada, MD, an associate professor at the UPF Department of Medicine and Life Sciences, director of the Neurosciences program of the Hospital del Mar Research Institute and head of the neurology service at Hospital del Mar. “So, for human beings, researchers and physicians, it is hard to visualize it if it is not by using these types of tools that allow us to discern and identify the related elements.”
For their newly reported study the investigators conducted what they described as “a multilayer network analysis and deep phenotyping with multi-omics data (genomics, phosphoproteomics and cytomics), brain and retinal imaging, and clinical data,” using data obtained from a multicenter prospective cohort of 328 patients and 90 healthy controls. The study, they said, is one of the first to simultaneously analyze data from very different scales, covering everything from genes to the whole organism, to help understand the complexity of chronic diseases. “Our multilayer network analysis allowed us to assess the relationship between different biological scales in the disease and to identify paths linking the five layers (genomics, proteomics, cytomics, imaging and clinical) based on statistical associations.”
The results identified a correlation a kinase protein, total T cells, the thickness of the layer of retinal nerve fibers, and the timed gait test. “The validated path with the highest joint cross-correlation connects the protein MK03, previously associated with MS, with total T cells, the thickness of the retinal nerve fiber layer (RNFL) and the timed 25 foot walk test (T25WT) … This highlights the importance of the molecular and cellular scales when considering explaining the phenotypes of complex diseases,” they wrote. “Indeed, these paths could be the target of a future treatment of personalized medicine in MS. This could also be transferable to other autoimmune disorders, commonly sharing disease underlying mechanisms.”
While the size of the study on its own isn’t sufficient to validate use of the discovered correlation as a biomarker to diagnose and possibly treat multiple sclerosis, the results have provided an integrated view of this complex system and revealed the relationship between four biological scales: proteins, cells, tissues and behavior. “Overall, the identified paths provide a means to connect the molecular aspects of MS with the overall phenotype,” the team added.
“In complex diseases it is very difficult to have genetic biomarkers,” Garcia-Ojalvo noted. “They are often determined by multiple genes and there is a lot of “background noise”. And here we are studying sets of genes, proteins, and phenotypes, and if they are related to each other, we have an indication of the existence of the disease.”
Villoslada further stated, “With multiple sclerosis we have to build a puzzle whose aspect we can more or less intuit. We are not totally in the dark, which is why we use systems biology, which informs us of the relevant relationships between the elements so that the puzzle is coherent, fits and we learn. And once we know how the disease works, we can find out how to deal with it.”
The authors further concluded, “The results from the multilevel network analysis with the omics data and phenotype data highlight the importance of considering MS as a multiscale disease, where the layers connect with varying strengths and information is filtered or strengthened across the layers.” However, they pointed out, “Other genomic information such as DNA sequencing, epigenetics and RNA expression, or more global approaches is likely needed for a more thorough analysis in multiscale complex diseases.”
