Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of brain functions, usually preceded by memory impairments. It has been widely affecting aging Americans over 65 old and listed as 6th leading cause of death. More importantly, unlike other diseases, loss of brain function in AD progression usually leads to the significant decline in self-care abilities. And this will undoubtedly exert a lot of pressure on family members, friends, communities and the whole society due to the time-consuming daily care and high health care expenditures. In the past decade, while deaths attributed to the number one cause, heart disease, has decreased 16 percent, deaths attributed to AD has increased 68 percent. And all of these situations will continue to deteriorate as the population ages during the next several decades. To prevent such health care crisis, substantial efforts have been made to help cure, slow or stop the progression of the disease. The massive data generated through these efforts, like multimodal neuroimaging scans as well as next generation sequences, provides unprecedented opportunities for researchers to look into the deep side of the disease, with more confidence and precision. While plenty of efforts have been made to pull in those existing machine learning and statistical models, the correlated structure and high dimensionality of imaging and genetics data are generally ignored or avoided through targeted analysis. Therefore their performances on imaging genetics study are quite limited and still have plenty to be improved. The primary contribution of this work lies in the development of novel prior knowledge-guided regression and association models, and their applications in various neurobiological problems, such as identification of cognitive performance related imaging biomarkers and imaging genetics associations. In summary, this work has achieved the following research goals: (1) Explore the multimodal imaging biomarkers toward various cognitive functions using group-guided learning algorithms, (2) Development and application of novel network structure guided sparse regression model, (3) Development and application of novel network structure guided sparse multivariate association model, and (4) Promotion of the computation efficiency through parallelization strategies.

<p>Canines can detect different diseases simply by smelling different biological sample types, including  urine,  breath  and  sweat.  This  has  led  researchers  to  try  and  discovery  unique  volatile  organic compound (VOC) biomarkers. The power of VOC biomarkers lies in the fact that one day they may be able to be utilized for noninvasive, rapid and accurate diagnostics at a point of care using  miniaturized  biosensors.  However,  the  identity  of  the  specific  VOC  biomarkers  must  be  demonstrated before designing and fabricating sensing systems. Through  an  extensive  series  of  experiments,  VOCs  in  urine  are  profiled  by  solid  phase  microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS) to identify biomarkers for breast cancer using murine models. The results from these experiments indicated that  unique  classes  of  urinary  VOCs,  primarily  terpene/terpenoids  and  carbonyls,  are  potential  biomarkers  of  breast  cancer.  Through  implementing  chemometric  approaches,  unique  panels  of  VOCs  were  identified  for  breast  cancer  detection,  identifying  tumor  location,  determining  the  efficacy of dopaminergic antitumor treatments, and tracking cancer progression. Other diseases, including COVID-19 and hypoglycemia (low blood sugar) were also probed to identify volatile biomarkers present in breath samples.  VOC biomarker identification is an important step toward developing portable gas sensors, but  another  hurdle  that  exists  is  that  current  sensors  lack  selectivity  toward  specific  VOCs  of  interest.  Furthermore,  testing  sensors  for  sensitivity  and  selectivity  is  an  extensive  process  as  VOCs  must  be  tested  individually  because  the  sensors  do  not  have  modes  of  chromatographic  separation or compound identification. Another set of experiments is presented to demonstrate that SPME  fibers  can  be  coated  with  materials,  used  to  extract  standard  solutions  of  VOCs,  and  analyzed  by  GC-MS  to  determine  the  performance  of  various  gas  sensing  layers.  In  the  first  of  these  experiments,  polyetherimide  (PEI)  was  coated  onto  a  SPME  fiber  and  compared  to  commercial polyacrylate (PAA) fibers. The second experiment tuned the extraction efficiency of polyvinylidene fluoride (PVDF) - carbon black (CB) composites and showed that they had higher sensitivity  for  urinary  VOC  extraction  relative  to  a  polydimethylsiloxane  (PDMS)  SPME  fiber.  These results demonstrate SPME GC-MS can rapidly characterize and tune the VOC adsorption capabilities of gas sensing layers. </p>

<p>Urine-based biomarkers offer numerous advantages for clinical analysis, including non-invasive collection, a suitable sample source for longitudinal disease monitoring, a better screenshot of disease heterogeneity, higher sample volumes, faster processing times, and lower rejection rates and costs. They will be extremely useful in a clinical trial context, which can be applied alone or in combination with other methods as long as they demonstrate clear reproducibility across cohorts. While biofluids such as urine present enormous challenges with a wide dynamic range and extreme complex typically dominated by a few highly abundant proteins, we have demonstrated that the analytical issue can be efficiently addressed by focusing on extracellular vesicles (EVs), tiny packages released by all kinds of cells. These tiny packages contain different kinds of molecules from inside the cells. Here, we established a robust EV isolation and characterization platform to screen and validate Parkinson’s Disease (PD) and Renal Cell Carcinoma (RCC) biomarkers from urine. PD is a progressive neurological disorder affecting body movement because some brain cells stop producing dopamine. PD is often not diagnosed until it has advanced, making early detection crucial. We investigated urinary EVs from 138 individuals to enable early detection and found several proteins involved in PD development that could be biological indicators for early disease detection. Several biochemical techniques were applied to verify our findings. In the second project, we attempted to develop a novel diagnostic technique for early intervention of RCC. Here, we made our efforts to develop a quantitative method based on data-independent acquisition (DIA) mass spectrometry to analyze urinary EV phosphoproteomics for non-invasive RCC biomarker screening. Combined with our in-house EVtrap method for EV isolation and PolyMAC enrichment of phosphopeptides, we quantified 2,584 unique phosphosites. We observed unique upregulated phosphosites and pathways differentiating healthy control (HC), chronic kidney disease (CKD), low-grade, and high-grade clear cell RCC. These applications have a significant promise for early PD and RCC diagnosis and monitoring based on actual functional proteins with urine as the source. These studies might provide a viable path to developing urinary EV-based disease diagnosis.</p>

Affinity-based proteomics utilizes affinity binders to detect target proteins in a large-scale manner. This thesis describes a high-throughput method, which enables the search for biomarker candidates in human plasma and serum. A highly multiplexed antibody-based suspension bead array is created by coupling antibodies generated in the Human Protein Atlas project to color-coded beads. The beads are combined for parallel analysis of up to 384 analytes in patient and control samples. This provides data to compare protein levels from the different groups. In paper I osteoporosis patients are compared to healthy individuals to find disease-linked proteins. An untargeted discovery screening was conducted using 4608 antibodies in 16 cases and 6 controls. This revealed 72 unique proteins, which appeared differentially abundant. A validation screening of 91 cases and 89 controls confirmed that the protein autocrine motility factor receptor (AMFR) is decreased in the osteoporosis patients. Paper II investigates the risk proteome of inflammatory bowel disease (IBD). Antibodies targeting 209 proteins corresponding to 163 IBD genetic risk loci were selected. To find proteins related to IBD or its subgroups, sera from 49 patients with Crohn’s disease, 51 with ulcerative colitis and 50 matched controls were analyzed. From these targeted assays, the known inflammation-related marker serum amyloid protein A (SAA) was shown to be elevated in the IBD cases. In addition, the protein laccase (multi-copper oxidoreductase) domain containing 1 (LACC1) was found to be decreased in the IBD subjects. In conclusion, assays using affinity-based bead arrays were developed and applied to screen human plasma and serum samples in two disease contexts. Untargeted and targeted screening strategies were applied to discover disease-associated proteins. Upon further validation, these potential biomarker candidates could be valuable in future disease studies. / <p>QC 20180412</p>