This is the fourth issue of the METHYLOMIC Project Newsletter!
We are excited to share updates on our progress with all our stakeholders.
This newsletter is released quarterly, aiming to keep everyone informed about the significant developments and achievements within our consortium.
On 21 October 2024, EFCCA hosted the online workshop. The workshop brought together patients, researchers and clinicians to foster mutual understanding of the challenges, expectations and perspectives on the use of Artificial Intelligence (AI) and IBD care. Throughout the session, the different stakeholders explained how AI technologies are applied in research and IBD care, and the extraordinary impact they can have on the management of IBD.
The workshop featured a Q&A session, and a panel discussion designed to encourage dialogue and active participation from attendees. EFCCA’s aim was to reach consensus on a joint Call to Action on AI and IBD care.
View the agenda, including the programme and speakers.
You can watch the recorded workshop via the below button.
This is the third issue of the METHYLOMIC Project Newsletter!
We are excited to share updates on our progress with all our stakeholders.
This newsletter is released quarterly, aiming to keep everyone informed about the significant developments and achievements within our consortium.
This is the second issue of the METHYLOMIC Project Newsletter!
We are excited to share updates on our progress with all our stakeholders.
This newsletter is released quarterly, aiming to keep everyone informed about the significant developments and achievements within our consortium.
This study is unique because it focuses on the predictive power of DNA methylation patterns for therapy response. While most Crohn’s disease researches emphasize genetic mutations or inflammatory biomarkers, this study investigates the role of epigenetic biomarkers, leading to a new perspective on therapy response prediction. In addition, this research emphasizes the importance of epigenetic modifications in the pathophysiology of complex diseases such as Crohn’s disease and Psoriasis, leading to advancements in precision medicine.
Unlike single nucleotide polymorphisms (SNPs) and mutations, that involve permanent changes in the DNA, methylation affects gene expression by altering DNA accessibility to transcription, not altering the DNA sequence itself. This process is highly dynamic and can therefore be influenced by several factors, including environment, lifestyle, and diet. This makes DNA methylation potentially responsive to treatment or disease progression. Hopefully, DNA methylation patterns can help to identify disease subtypes, monitor treatment responses, and predict relapses, offering a more personalized approach to disease management. Therefore, DNA methylation models might be a valuable tool for managing complex diseases such as Crohn’s disease and Psoriasis.
This research might significantly impact future treatment strategies for Crohn’s disease. By identifying specific methylation signatures associated with response to different biologic agents available, it can enable more precise and personalized treatment plans. Current therapy choices rely on a so-called ‘trial-and-error’ approach, often leading to long trajectories of inadequate treatment. By using DNA methylation-based therapy prediction models, the suitable therapy option can be started in an earlier phase.
Welcome to the first issue of the METHYLOMIC Project Newsletter!
We are excited to share updates on our progress with all our stakeholders.
This newsletter will be released quarterly, aiming to keep everyone informed about the significant developments and achievements within our consortium.
Andrew Li Yim and Anje te Velde lead the team in WP5 of the METHYLOMIC project, alongside Wouter de Jonge, Peter Henneman and Femke Mol. In its inaugural year, this team aims to discern whether DNA methylation signals originate from methylation itself or variations in cellular composition. Utilizing advanced single-cell technologies, they target specific DNA methylation patterns unique to various cell types. The following text provides a summary of the first year of research within WP5.
The goal of work package 5 is to understand the origin of the observed DNA methylation signal of the predictor CpGs. Simply put, does the signal originate from actual DNA methylation, or a difference in cellular composition. To this end, we aim to pinpoint specific DNA methylation patterns that are unique to different cell types. To achieve this, we’ve looked for single-cell technologies that are capable of characterizing the cellular composition based on gene expression while simultaneously quantifying the DNA methylome at a single-cell level. Our strategy integrates two methodologies: smartSeq2 for characterizing cell populations via RNA expression, and reduced representation bisulfite sequencing (RRBS) for exploring the DNA methylome.
The first step is to isolate white individual white blood cells from blood samples, followed by the isolation of both DNA and RNA from each cell. Ultimately, this will result in a paired DNA methylome and transcriptome per cell, for which we will develop a novel bioinformatic pipeline to dissect and interpret the data.
We anticipate that one of the challenges lies in the complexity of the blood samples. Blood encompasses different cell types, including erythrocytes, leukocytes, platelets, and plasma. In context of inflammation, our focus narrows down to the leukocyte fraction. Yet, within this subset there are more subdivisions: granulocytes, lymphocytes, and monocytes. Granulocytes are particularly challenging due to their susceptibility to degradation after collection.
We are now getting ready to put our techniques to the test using blood samples from volunteers with the goal of optimizing the protocol necessary to conduct the final analyses on samples obtained from patients that are scheduled to start treatment. We anticipate that our observations will provide insights into the mechanisms by which response manifests, potentially allowing us to refine existing treatments and pave the way for novel therapeutic interventions.
Text: Femke Mol
The AMC has been awarded funding for the rapid targeted methylation assay development and partial funding for the clinical validation trial through a Horizon Europe grant award. Therefore, AMC also seeked co-funding from Helmsley Charitable Trust to close the gap in funding for the clinical validation trial. In 2023 funding was granted by Helmsley to support the following clinical validation trial activities:
There is an urgent need to develop biomarkers that are predictive of a Crohn’s Disease patient’s response to biologic therapies. If successful, this project will clinically validate a rapid targeted methylation marker blood test for the selection of the biologics adalimumab, vedolizumab and ustekinumab that has the highest likelihood of providing effective treatment for an individual Crohn’s Disease patient, decreasing the potential for delays in effective treatment and improving outcomes for Crohn’s Disease patients. Furthermore, the proposed study is designed to meet the criteria for future submission of the validated predictive assay for regulatory agency approval and therefore paves the way for the critical future steps that will be required to bring this potentially valuable tool into clinical practice.
Epigenetics is a broad term for the control panel for our genes. It involves changes to our DNA that do not alter the genetic code itself but can influence how DNA will be used in the cell. These instructions can be influenced by various factors, such as our environment, lifestyle, and even experiences. Epigenetics does not only affect DNA, but also structures wrapped around our DNA, so-called histones. It is a very complex process. Epigenetic changes can impact our health by affecting how our genes function, not only influencing our risk for certain diseases but also influencing the effect of different drugs on our bodies. In the OmiCrohn trial we will use patients’ individual epigenetic “blueprint” in DNA to predict the effect of different therapeutic options.
I believe that in most case it is unclear how epigenetics exactly affects immune diseases. Most studies to date have associative in nature, meaning that epigenetic differences are correlated with the occurrence, or particular phenotypes, of immune mediated inflammatory disorders (IMIDs). That being said, at the level of histone modifications, there is some evidence that epigenetics may actually modulate the inflammatory phenotype through particular epigenetic readers and erasers. One of the biggest problems in understanding how epigenetics affects the pathogenesis and/or etiology is due to the fact that epigenetics is a cell-type specific feature with most studies interrogating heterogeneous populations: any “epigenetic” difference could be the result of population differences and/or actual differences at the level of the epigenome. This is exactly the question we try to elucidate in work package 5 where we seek to disentangle the DNA methylome from cellular heterogeneity.
The current research is novel in that the use of DNA methylation as biomarker for predicting response to therapy has not been implemented outside the field of oncology. Beyond the DNA methylation analyses on peripheral blood, the characterization of DNA methylation on single cells is not an experiment that is done routinely. If successful, our data would not only show that the implementation is possible in peripheral blood, but also provide a resource for other researchers to interrogate the DNA methylome at single cell level.
An AI algorithm can be a very broad term. The way we are implementing our algorithm is essentially a set of rules that define, based on the DNA methylome, whether the provided DNA methylation data from a particular sample resembles more a responder or non-responder. The AI algorithm is in our case a (set of) decision tree(s), which essentially boils down the same principle used by daycares in the Netherlands during COVID-19 when deciding whether a could go to the daycare. Importantly, the actual AI algorithm does not need to be the most complicated (although it can certainly be!) but rather the process by which one arrives at such an algorithm. This process is, just like the rest of science, an iterative process by which the most optimal algorithm is decided upon through multiple rounds of training and validation.