{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/83837"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/83837","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Investigation of Blood-Based Biomarkers and Epigenetic Mechanisms of Intracranial Aneurysms","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Poppenberg, Kerry; 0000-0002-1748-887X"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Meng, Hui","Biomedical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-17T19:54:36Z","date_published":"2022-06-17T19:54:36Z","updated_at":"2026-07-27T19:05:28Z","subjects":["biomedical engineering","bioinformatics","genetics"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/83837","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Meng, Hui","Biomedical Engineering"]},{"key":"dc:creator","label":"Author","values":["Poppenberg, Kerry; 0000-0002-1748-887X"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-17T19:54:36Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["biomedical engineering","bioinformatics","genetics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/83837"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Intracranial aneurysms (IAs) are lesions within cerebrovasculature with potentially fatal consequences if they rupture. The prevalence in general population is estimated at 3.2%, though it is impossible to ascertain the true percentage as most IAs are asymptomatic. Though the annual rupture rate is estimated to be only 1%, the consequences of rupture are dangerous. Rupture leads to subarachnoid hemorrhage (SAH), which has high rates of morbidity and mortality. Within the first year after SAH, 45% of patients die while 50% of those who do survive are faced with major disabilities. Detection of IAs before rupture would allow them to be appropriately monitored and treated, saving many individuals from the aforementioned complications. Currently, IAs are only detected upon rupture, incidentally by imaging modalities, such as magnetic resonance imaging (MRI), computed tomography angiography (CTA), and digital subtraction angiography (DSA), or in the uncommon event that they cause symptoms. Imaging, however, is not recommended as a screening method because of its high costs and potential risks to patients. A blood test would provide a quick and minimally invasive alternative to manage IA detection. In pursuit of this goal, our lab conducted a preliminary RNA-sequencing study using circulating neutrophils from patients with and without IA. Neutrophils have been found to alter their transcriptomes in diseases associated with inflammation, similar to IA. Therefore, we hypothesized that the contact between circulating neutrophils and the aneurysmal lesion would produce detectable changes in the neutrophils’ transcriptomes. The original study demonstrated proof of concept and resulted in an 82 transcript signature specific to IA. Subsequently, I worked with Dr. Tutino to use these differential expression profiles to create a 26 transcript biomarker for presence of unruptured IA, achieving an accuracy of 0.90 in an independent testing cohort. In this dissertation, I continued this line of inquiry, substantially expanding the neutrophil cohort to 134 samples and developing new biomarkers using more advanced machine learning techniques. The random forest model using the transcripts identified by LASSO performed the best with a testing AUC of 0.99. Furthermore, whole blood transcriptomes have also been found to be altered in patients with vascular diseases, such as atherosclerosis, thoracic aortic aneurysm, and arteriovenous malformation. As multiple cell types contribute to IA pathogenesis and we ultimately hope to have our test implemented clinically, I pursued the feasibility of using whole blood transcriptomes to develop predictive models for unruptured IAs. Using this heterogeneous cell population, I was able to develop a predictive model with training and testing accuracies of 85% in a dataset of 67 samples total. The features selected for this model reflect activation of leukocytes, activation of macrophages, and inflammatory response. These ontologies suggest the biomarker, despite being composed of a small set of genes, is capturing processes critical in IA pathogenesis. From this transcriptome profiling work, we often questioned whether the transcriptomic abnormalities we were detecting were a result of aneurysm presence or if they existed before the aneurysm and led to its formation. I examined the genetic landscape around regions with associated risk and determined if any of the genes our lab identified corresponded to those implicated by predicted risk. I found no overlap, suggesting the transcriptomic aberrations we have detected are in response to the aneurysmal lesion and not a precursor to IA formation. I also examined chromatin features in these risk associated regions in multiple cell types related to IA, including endothelial cells, monocytes, neutrophils, and peripheral blood mononuclear cells. I found that known genetic alterations associated with IA are present in endothelial cells to a greater degree than immune cells, implying that genetic risk factors for IA are more likely affecting the vascular wall than circulating inflammatory cells. Ultimately in this dissertation, I demonstrate the feasibility of developing blood-based biomarkers to predict presence of unruptured IAs. I achieved success with models built with both neutrophil and whole blood transcriptomes. Further, I investigated networks and ontologies associated with differentially expressed genes and genes selected for biomarkers, finding a common theme of inflammation and cell activation in IA samples. Lastly, from my epigenetic study I reported that these transcriptional differences are more likely a response to the disease as genetic risk associates with endothelial cells. In the future, blood-based IA biomarkers such as those developed in this dissertation will permit screening in greater populations, ultimately shifting IA management from reactive to proactive.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Investigation of Blood-Based Biomarkers and Epigenetic Mechanisms of Intracranial Aneurysms"]}]}],"canonical_facts":{"dc:contributor":["Meng, Hui","Biomedical Engineering"],"dc:creator":["Poppenberg, Kerry; 0000-0002-1748-887X"],"dc:date":["2022-06-17T19:54:36Z","2020"],"dc:description":["Ph.D.","Intracranial aneurysms (IAs) are lesions within cerebrovasculature with potentially fatal consequences if they rupture. The prevalence in general population is estimated at 3.2%, though it is impossible to ascertain the true percentage as most IAs are asymptomatic. Though the annual rupture rate is estimated to be only 1%, the consequences of rupture are dangerous. Rupture leads to subarachnoid hemorrhage (SAH), which has high rates of morbidity and mortality. Within the first year after SAH, 45% of patients die while 50% of those who do survive are faced with major disabilities. Detection of IAs before rupture would allow them to be appropriately monitored and treated, saving many individuals from the aforementioned complications. Currently, IAs are only detected upon rupture, incidentally by imaging modalities, such as magnetic resonance imaging (MRI), computed tomography angiography (CTA), and digital subtraction angiography (DSA), or in the uncommon event that they cause symptoms. Imaging, however, is not recommended as a screening method because of its high costs and potential risks to patients. A blood test would provide a quick and minimally invasive alternative to manage IA detection. In pursuit of this goal, our lab conducted a preliminary RNA-sequencing study using circulating neutrophils from patients with and without IA. Neutrophils have been found to alter their transcriptomes in diseases associated with inflammation, similar to IA. Therefore, we hypothesized that the contact between circulating neutrophils and the aneurysmal lesion would produce detectable changes in the neutrophils’ transcriptomes. The original study demonstrated proof of concept and resulted in an 82 transcript signature specific to IA. Subsequently, I worked with Dr. Tutino to use these differential expression profiles to create a 26 transcript biomarker for presence of unruptured IA, achieving an accuracy of 0.90 in an independent testing cohort. In this dissertation, I continued this line of inquiry, substantially expanding the neutrophil cohort to 134 samples and developing new biomarkers using more advanced machine learning techniques. The random forest model using the transcripts identified by LASSO performed the best with a testing AUC of 0.99. Furthermore, whole blood transcriptomes have also been found to be altered in patients with vascular diseases, such as atherosclerosis, thoracic aortic aneurysm, and arteriovenous malformation. As multiple cell types contribute to IA pathogenesis and we ultimately hope to have our test implemented clinically, I pursued the feasibility of using whole blood transcriptomes to develop predictive models for unruptured IAs. Using this heterogeneous cell population, I was able to develop a predictive model with training and testing accuracies of 85% in a dataset of 67 samples total. The features selected for this model reflect activation of leukocytes, activation of macrophages, and inflammatory response. These ontologies suggest the biomarker, despite being composed of a small set of genes, is capturing processes critical in IA pathogenesis. From this transcriptome profiling work, we often questioned whether the transcriptomic abnormalities we were detecting were a result of aneurysm presence or if they existed before the aneurysm and led to its formation. I examined the genetic landscape around regions with associated risk and determined if any of the genes our lab identified corresponded to those implicated by predicted risk. I found no overlap, suggesting the transcriptomic aberrations we have detected are in response to the aneurysmal lesion and not a precursor to IA formation. I also examined chromatin features in these risk associated regions in multiple cell types related to IA, including endothelial cells, monocytes, neutrophils, and peripheral blood mononuclear cells. I found that known genetic alterations associated with IA are present in endothelial cells to a greater degree than immune cells, implying that genetic risk factors for IA are more likely affecting the vascular wall than circulating inflammatory cells. Ultimately in this dissertation, I demonstrate the feasibility of developing blood-based biomarkers to predict presence of unruptured IAs. I achieved success with models built with both neutrophil and whole blood transcriptomes. Further, I investigated networks and ontologies associated with differentially expressed genes and genes selected for biomarkers, finding a common theme of inflammation and cell activation in IA samples. Lastly, from my epigenetic study I reported that these transcriptional differences are more likely a response to the disease as genetic risk associates with endothelial cells. In the future, blood-based IA biomarkers such as those developed in this dissertation will permit screening in greater populations, ultimately shifting IA management from reactive to proactive.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/83837"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["biomedical engineering","bioinformatics","genetics"],"dc:title":["Investigation of Blood-Based Biomarkers and Epigenetic Mechanisms of Intracranial Aneurysms"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:28Z"}