Browsing by Subject "Biomarker"
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Item Biomarkers of rapid lung function decline in well-controlled HIV and untreated HIV: A secondary analysis of inflammation, coagulation, and pneumoproteins in the START Pulmonary Substudy(2021-06) MacDonald, DavidBackground: Chronic obstructive pulmonary disease (COPD) is among the leadingcauses of death worldwide and HIV is an independent risk factor for the development of COPD. However, the etiology of this increased risk and means to identify persons with HIV (PWH) at highest risk for COPD have remained elusive. Biomarkers may reveal etiologic pathways and allow better COPD risk stratification. Methods: We performed a matched case:control study of PWH in the Strategic Timingof Antiretoviral Treatment (START) pulmonary substudy. Cases had rapid lung function decline (> 40 mL/year FEV1 decline) and controls had stable lung function (0 +/- 20 mL/year). The analysis was performed in two distinct groups: 1) those who were virally suppressed for at least 6 months and 2) those with untreated HIV (from the START deferred treatment arm). We used linear mixed effects models to test the relationship between case:control status and blood concentrations of pneumoproteins (surfactant protein-D and club cell secretory protein), and biomarkers of inflammation (IL-6 and hsCRP) and coagulation (d-dimer and fibrinogen). We included an interaction with treatment group (untreated HIV vs viral suppression) to test if associations varied by treatment group. Results: This analysis included 77 matched case:control pairs in the virally suppressedbatch, and 42 matched case:control pairs in the untreated HIV batch (n=238 total). Median (IQR) CD4+ count was lowest in the controls with untreated HIV at 674 (580, 838). We found no significant associations between case:control status and pneumoprotein or biomarker concentrations in either virally suppressed or untreated PWH. Conclusions: Concentrations of pneumoproteins and biomarkers of inflammation andcoagulation were not associated with subsequent rapid lung function decline.Item Improving Delivery of Therapeutics to Targeted Tissues in Lysosomal Diseases(2021-12) Kim, SarahLysosomal diseases are a group of over 70 diseases with a combined incidence of approximately 1 in 7,700 live births. Most lysosomal diseases are caused by mutations in enzymes normally present in the lysosome. Lysosomal diseases are multi-systemic and progressive diseases. Currently, only 12 lysosomal diseases have treatments. A challenge in drug development is the lack of biomarkers that reflect disease progression or show response to therapy. Furthermore, current therapies have difficulty reaching certain tissues that are major contributors to morbidity and mortality, such as the central nervous system (CNS) and cardiac valves. The overall objective of this dissertation is to improve the delivery of therapeutics to targeted tissues in lysosomal diseases. To accomplish this objective, three main studies were performed. 1) Validation of Chitotriosidase as a CNS Biomarker for Gangliosidoses. Chitotriosidase was investigated as a probable surrogate endpoint for clinical trials with gene therapy. The first objective was to validate chitotriosidase levels for important clinical outcomes in patients with lysosomal diseases. The second objective was to assess chitotriosidase’s ability to detect effective gene therapy in murine models of lysosomal diseases. In patients with gangliosidoses, the most severe infantile phenotype had higher chitotriosidase levels in the cerebrospinal fluid (CSF) and a different pattern over time than the more attenuated juvenile and late-onset forms. Chitotriosidase levels were also significantly associated with neurocognitive impairment. In mice with mucopolysaccharidosis type I (MPS I), there were significant differences among the untreated, gene-therapy treated, and mice heterozygous for a mutation in the IDUA gene. These results support the use of CSF chitotriosidase levels to diagnose different disease phenotypes and to monitor disease progression in patients. As a potential biomarker of neurological improvement, CSF chitotriosidase can aid in the development of therapies that target the CNS. 2) Investigation of Iduronidase Enzymes Linked to Pepcan to Improve Delivery to Targeted Tissues. The objective of this study was to determine if pepcan-12 can increase the uptake of iduronidase into the brain of MPS I mice. Pepcan-12 is a ligand for the cannabinoid receptor type 1 (CB1), a highly expressed receptor in the CNS. The hypothesis was that a fusion iduronidase containing pepcan-12 would have higher activity levels than iduronidase in the brain. Sequences of one of two linkers, Linker S or Linker T, were inserted between pepcan-12 and IDUA to conjugate the ligand and iduronidase. MPS I mice were injected with plasmids encoding either the native iduronidase or one of four fusion iduronidase enzymes containing: pepcan-12 + Linker S, pepcan-12 + Linker T, Linker S, or Linker T. The fusion enzymes and iduronidase had similar activity levels in the brain. Unexpectedly, the fusion enzymes had higher activity levels than iduronidase in the heart and plasma, which appears to be caused by the linkers. Therefore, these fusion enzymes may improve cardiovascular outcomes in MPS I. In several MPS disorders, the cardiac valves continue to worsen despite enzyme replacement therapies (ERT) and hematopoietic cell transplants. The small size of these linkers facilitates their use as fusion enzymes encoded in gene therapy or administered directly as ERT. Therefore, these linkers may aid in therapeutic development for other lysosomal diseases. 3) Pharmacokinetic Analysis of Iduronidase and a Fusion Iduronidase Enzyme Encoded in Gene Therapy. In the previous study, an iduronidase enzyme containing Linker T, termed Linker T iduronidase, had higher activity levels than iduronidase in the plasma and heart. This study sought to investigate the mechanism of Linker T iduronidase, but a gap between the fields of lysosomal diseases/gene therapy and pharmacokinetics (PK)/ pharmacodynamics (PD) became apparent. In the field of lysosomal diseases, the activity level of an enzyme is an important measurement of efficacy, because an enzyme’s activity levels are more predictive of efficacy than its physical levels. However, pre-clinical studies in lysosomal diseases lack well-described methods to quantify changes in enzyme activity levels over time. In contrast, the pharmacokinetic field has rigorous and reproducible methods to quantify changes in a therapy over time in the body. However, traditional pharmacokinetic methods face challenges in gene therapy because of the need for uniform or convertible units. Furthermore, absorption, distribution, metabolism, and elimination processes are well-characterized for small molecule drugs but not yet adapted for biological therapies. To bridge the fields of lysosomal diseases/gene therapy and PK/PD, I aimed to develop an approach incorporating values with greater prediction of efficacy from the field of lysosomal diseases and the quantitative methods from the field of pharmacokinetics. The objective of this study was to perform a pharmacokinetic analysis of iduronidase and Linker T iduronidase administered as gene therapies. The hypothesis was that Linker T iduronidase would have a higher area under the curve (AUC) or half-life, estimated with enzyme activity levels, than iduronidase in the plasma. MPS I mice were injected with plasmids encoding either iduronidase or Linker T iduronidase. At ten time points, ranging from 0.5 to 168 hours post-injection, the enzymes’ physical levels in the liver, activity levels in the liver, and activity levels in the plasma were measured. In the liver, both the physical and activity levels over time were similar between the native iduronidase and Linker T iduronidase. In contrast, enzyme activity levels over time in the plasma showed differences between the native iduronidase and Linker T iduronidase. The time curves of activity in the plasma showed biphasic profiles for both enzymes. Iduronidase had a sharper decline between 24 and 48 hours, and both enzymes had approximately parallel slopes between 96 and 168 hours. The Linker T iduronidase had a two-fold higher AUC of activity than the normal iduronidase in the plasma. The AUC of plasma activity and other PK parameters were contextualized in gene therapy, and experimental data were used to deduce the mechanism of Linker T. These results suggest that Linker T iduronidase may have a distinct property that protects the enzyme from degradation or inactivation in the plasma. The enzymes were estimated to have a half-life of activity in the plasma under noncompartmental analysis. Future studies with compartmental analysis would better characterize half-lives of activity in biphasic profiles. This study performs a novel approach of conducting a formal pharmacokinetics analysis on enzyme activity levels, a traditionally pharmacodynamic outcome. The resulting PK parameters can be interpreted and used to gain mechanistic insight on gene therapy, by integrating concepts from pharmacokinetics and gene therapy In summary, these findings improve the therapeutic delivery in lysosomal disease through the validation of a CNS biomarker for lysosomal diseases, creation of a fusion enzyme with improved activity in the heart and plasma, and a novel approach and interpretation of pharmacokinetics to gain mechanistic insight on gene therapy.Item Informing the Oral Squamous Cell Carcinoma Biomarker Search by Exudate Proteomics(2013-04) Kooren, Joel AllanOral cancer is the sixth most common cancer worldwide ahead of Hodgkin's lymphoma, leukemia, brain, stomach, or ovarian cancers, with about 41,000 Americans being diagnosed annually. More than 90% of oral cancers are oral Squamous cell carcinomas (OSCC). While the overall 5-year survival rate is about 60%, the survival rate when diagnosed early is higher than 80%. Currently the standard for diagnosis of OSCC is early visual detection of a suspicious oral lesion followed by scalpel biopsy with histology. However, the invasiveness, expense, and required expertise involved prevents consistent application on at risk individuals. Chapter 1 discusses the methods that are being investigated for sampling and discovering biomarkers of OSCC that address some of these limitations. Protein biomarkers contained in samples collected non-invasively and directly from at-risk oral premalignant lesions (OPML) would address current needs in a uniquely targeted fashion. Chapter 2 of this thesis describes work evaluating the potential of a novel method using commercial PerioPaper absorbent strips for the collection of oral lesion exudate fluid coupled with mass spectrometry based proteomics for OSCC biomarker discovery. This research focuses on demonstrating the feasibility of using oral lesion exudates in proteomic research, exploring the proteome of exudate samples, discriminating between exudates collected from clinically different sources, with supplemental table 1 showing which proteins distinguish healthy and OPML sources. Furthermore, to ensure that the best possible marker candidates are selected given clinical sample availability, multiple methods were explored enable and improve quantitative proteomic analysis of exudates in chapter 3 (Identified proteins in supplemental files 2 and 3). Our label-free quantitative proteomics strategy analyzed paired control and OPML exudates (figure 8), identifying differentially abundant proteins between sample types. Next, we selected several [exudate] differentially abundant proteins for testing in while saliva, comparing their relative abundance levels in healthy, OPML and oral Squamous cell carcinoma (OSCC) subjects. Two proteins, CK10 and A1AT, showed differences in saliva. Our results provide a demonstration of the value of tissue exudate analysis for guiding salivary biomarker discovery in oral cancer, as well as providing promising biomarker candidates for future evaluation.Item An Investigation into the Biomarker Potential of Highly Branched Isoprenoids in Northern Minnesota Lacustrine Sediments(2022-08) Hanson, BennettHighly Branched Isoprenoids (HBIs) are isoprene-based lipids synthesized by diatoms. HBIs are commonly used as sea-ice proxies in marine environments where their presence is indicative of marginal ice zones. Historical data on Ice cover in the Laurentian Great Lakes is sparse and only spans from the mid 1960’s to present day. This study aims to expand the biomarker potential of HBIs to apply them as a proxy for ice cover on Lake Superior. Seven study sites around Lake Superior were selected and studied to determine which freshwater HBIs were present, and at what concentration they were found. Each site was sampled twice, in the summer and in the winter to determine if seasonal conditions affect HBI production of diatom communities. The HBI suite at each site is characterized by gas chromatography mass spectrometry (GC-MS) analysis of the nonpolar extractable fraction of the sediments and the genera of diatoms present at each site are characterized by visual identification. In addition, microcosm studies of Nitzschia and Fragilaria are isolated from the Two Harbors study site and are analyzed to determine the HBI suites produced by them. Overall, eight distinct HBIs were identified from the seven study sites, in concentrations ranging from 0.1-0.9 µg g-1 dry sediment. Four distinct HBIs were identified from the microcosm cultures including three distinct C30 and one distinct C25 HBIs. HBIs show a potential to be correlated to environmental conditions, though future experiments must be conducted to develop this relationship.Item Network-based learning algorithms for understanding human disease(2011-03) Hwang, Tae HyunAdvances in genomics, proteomics and molecular pathology with the use of high-throughput technologies, have produced vast datasets identifying thousands of genes whose genomic changes differ in diseased versus normal samples. Many statistical and machine learning methods have been developed to discover biomarkers with potential clinical value, but building reliable learning models for the discovery of biomarkers for prediction of clinical outcomes using high-throughput dataset is still a key challenge in genomic research. This thesis introduces network-based learning algorithms to better utilize large-scale genomic data, and to integrate data with biological prior knowledge to understand the role of genetic changes in human diseases. The first method, NetProp (Network Propagation), is a graph-based semi-supervised feature classification algorithm to identify discriminative biomarkers by learning on bipartite graphs in the analysis of high dimensional genomic data. The second method, HyperPrior, is a hypergraph-based semi-supervised learning algorithm to integrate genomic data with the known biological prior knowledge for biomarker identification and patient's outcome prediction. The third method, MINProp, is a general graph-based learning algorithm to integrate multiple genomic and network data for disease gene discovery. While the method could be applied to discover candidate biomarkers in a high-throughput genomic study, validating the candidate biomarkers is another challenging problem in genomic research. To address this, we introduce a network-based method, rcNet (rank coherence in Network), to elucidate the associations between disease and genes. We applied these methods to large and various real datasets including microarray gene expression profiles, single nucleotide polymorphisms (SNPs), and DNA copy number variations. Our methods identified novel biomarkers with clinical or biological relevance with the disease, as well as achieved competitive classification performance compared with other baseline methods. Our method also successfully validated the associations between diseases and potential disease-causing genes discovered from high-throuput studies. The results indicate that the method that explore the global topological information in the networks, and integrate data with biological prior knowledge could help to discover genetic determinants of human disease, and reveal underlying biological principles of human disease.Item The Role Of D-Serine In Normal Retinal Function And Implications For Psychiatry(2019-08) Torres Jimenez, NathaliaThere is a lack of objective measurements for assessing the progression of mental disorders such as schizophrenia. Biomarkers for schizophrenia would be an invaluable asset to identify at-risk individuals objectively, which should consequently improve the person’s prognosis and treatment. One such candidate for becoming a biomarker for schizophrenia is the flash-electroretinogram (fERG), an ophthalmological tool that assesses retinal integrity. Prior research had conflicting results, with some studies showing that people with schizophrenia have a reduced response from photoreceptors and bipolar cells. However, it has been unclear why abnormalities would occur that early in the retinal pathway when mouse studies that investigated monoamine deprivation, such as dopamine, did not reflect those deficits. An alternative reason for an altered fERG is that it may reflect reduced N-methyl-d-aspartate receptor (NMDAR) function, which has been postulated to explain some of the pathology exhibited in people with schizophrenia. However, no retinal field potentials in the outer retina had been attributed to NMDAR function. One way to induce hypofunction of the NMDAR is by reducing the availability of its co-agonist, either glycine or D-serine, since the NMDAR needs both glutamate and a co-agonist for activation. I examined how D-serine deprivation and its excess affects the outer retinal field potentials, and whether it has implications for psychiatry. We report the first fERG study in a genetic mouse model of schizophrenia characterized by NMDAR hypofunction from genetic silencing of serine racemase expression (SR-/-), an enzyme that converts L-serine to D-serine. We analyzed fERG components under mesopic-adapted conditions that reflect outer retinal function, the a-wave and the b-wave, to determine the resemblance to the human fERG from people with schizophrenia. In all the analyses, I included sex as a factor, due to thevii sex differences underlying the disease. We tested pharmacologically-induced hyperfunction of the NMDAR in WT mice by introducing D-serine. Lastly, we analyzed human fERG and pattern-electroretinogram (PERG) studies to assess outer and inner retinal function. I report that hypo- or hyper-function of the NMDAR, through changes in available D-serine, profoundly affects the temporal scale of photoreceptor and bipolar cell signaling, as well as the amplitude of bipolar cell currents. This work mirrors the deficits observed in people with schizophrenia. Including sex as a factor in analyses showed that D-serine affects male mice more profoundly regardless of genotype, suggesting that NMDAR and D-serine are involved in the retinal field potentials of the outer retina and are dependent on the animal’s sex. These studies also suggest that either there is a functional NMDAR component to the outer retinal field potentials or that D-serine has another role in the retina aside from being an endogenous co-agonist for the NMDAR. This implicates the involvement of gonadal hormones and D-serine in retinal functional integrity. Our human analyses reflect deficits in the retinal ganglion cell layer, and a trending reduction of the signal corresponding to bipolar cells. Furthermore, the human data analyses also showed an interaction between sex, with deficits affecting males with schizophrenia more profoundly. This work elevates the potential of the fERG to differentiate between healthy controls and subjects with schizophrenia, and to detect sex differences known to be present in schizophreniaItem S100A1 and nectin 4 biomarkers in ovarian cancer.(2010-05) DeRycke, Melissa SueThis doctoral thesis focuses on the use of protein biomarkers for ovarian cancer detection, diagnosis, and determination of prognosis. Ovarian cancer is frequently detected after metastasis has occurred; at this point, it is difficult to remove all the residual disease, resulting in poor survival. We hypothesize that discovering and validating novel biomarkers will allow for more sensitive, specific tests which may allow earlier detection and increase survival for women diagnosed with ovarian cancer. Work presented in this dissertation focuses on two biomarkers of ovarian cancer, S100A1 and nectin 4. S100A1 is a calcium binding protein involved in several cellular processes, including metabolism, regulation of cell cytoskeleton components, and the contractility of muscle tissue. S100A1 is differentially expressed in epithelial ovarian cancer subtypes and its expression in endometrioid ovarian cancers is an indicator of poor prognosis. Defining a subset of endometrioid ovarian cancers with a poor prognosis has useful implications for patient management and treatment. S100A1 positive patients may benefit from more aggressive chemotherapy and more frequent checkups following initial debulking surgery. Nectin 4 is also overexpressed in ovarian cancer and there is differential expression among the subtypes. A soluble form of nectin 4 is detectable in patient sera. Compared to CA125, nectin 4 is more specific but less sensitive. When combined with CA125 for the detection of ovarian cancer, both the specificity and positive predictive value (PPV) were increased. Nectin 4 expression increases proliferation in ovarian cancer cells but does not alter survival to either cisplatin or taxol. Both nectin 4 expressing and control cells are able to form spheroids equally well. Together, these results demonstrate that biomarkers can improve diagnosis and detection of ovarian cancer. Future studies may clarify how S100A1 and nectin 4 influence ovarian cancer progression. Both proteins are potential targets for therapy. Targeting S100A1 in endometrioid ovarian cancer may increase survival. Nectin 4 expression in normal tissues is restricted to the placenta. This makes it an ideal target to utilize in therapy as there should be little killing of noncancerous tissues.Item Towards affirmation of recovery of deeply embedded autobiographical memory and identification of an EEG biomarker using wearable sensors(2022-06) Das, Rupak KumarThe importance of background music in memory retrieval is irrefutable. Music can boost brain activity and jog deeply embedded memories. This is why background music is used as a popular therapy for dementia patients. Previous studies used music to recall lyrics, serial of words, and long and short-term memories. In this research, EEG and EDA data were collected from 40 healthy participants using wearable sensors during 9 music sessions (3 happy, 3 sad, and 3 neutral). A post-study survey was given to all participants after each piece of music to know if they recalled any autobiographical memories. The main objective is to find an EEG biomarker using the collected qualitative and quantitative data. The study discovered that during the memory "recall" scenario, alpha power (F3: p = 0.0066, F7: p = 0.0386, F4: p = 0.0023, and F8: p = 0288) increases significantly (on average 16.2%) compared to the "no-recall" scenario for all 4 EEG channels. We interpret increased alpha power (8–12 Hz) as a biomarker for autobiographical memory recall. In addition, for the EDA signal, there was a significant difference for the Tonic Standard Deviation (p = 0.0171), Tonic Min (p = 0.0092), Phasic Standard Deviation (p = 0.0260), Phasic Max (p = 0.0011), and Phasic Energy (p = 0.0478).