48670 research outputs found
Sort by
George Corbett. \u27Dante’s Christian Ethics. Purgatory and its moral contexts.\u27 Cambridge: Cambridge University Press, 2020.
Get PDFFrom \u27-Omics\u27 to Biomarkers and Mechanisms in Parkinson\u27s Disease: Growth Hormone Receptor and GPNMB
No full textParkinson’s disease (PD) is a devastating neurodegenerative disorder that affects an estimated ~5 million people worldwide and is associated with intracellular α-synuclein protein inclusions and progressive neuronal cell loss. There are currently no disease-modifying therapies for PD due in part to lack of clinically useful biomarkers and clearly intervenable therapeutic targets. Despite recent advances made with genome-wide association studies (GWAS), there is an unmet need in the PD field for (1) generation of diagnostic and prognostic biomarkers that can distinguish PD from non-PD and parse considerable clinical heterogeneity, and (2) mechanistic follow-up of GWAS-identified leads to elucidate their role in neurodegeneration. This dissertation addresses these two gaps in therapeutic development in PD. First, we perform an unbiased, ~1000-protein screen in plasma samples from over 500 PD patients and neurologically normal controls across three different clinical sites, identifying novel proteins associated with PD. We show that our top PD-associated biomarkers—particularly, plasma levels of growth hormone receptor (GHR)—replicate their associations with disease status across multiple cohorts, are robust to common sources of variability in a clinical setting (specifically, sample handling and dopaminergic medication), and predict future cognitive decline across cohorts, cognitive measures, and statistical test used. Second, to “widen the net” for potential pathophysiological mechanisms and therapeutic targets in PD, we dissect the mechanism behind the non-coding chromosome 7 locus first associated with PD by GWAS. We identify the glycoprotein non-metastatic melanoma protein B gene (GPNMB) as the target of this locus by mining existing “-omic” datasets and analyzing the probability of a shared causal variant behind both the association to PD risk and expression levels of potential target genes. We then confirm these in silico analyses with allele-specific expression experiments using patient-derived brain samples. We explore interactions between GPNMB and α-synuclein by immunofluorescence, co-immunoprecipitation, immunoblotting, and RNA-seq-based transcriptomic profiling in immortalized cell lines and iPSC-derived neurons with normal levels, partial loss, or full loss of GPNMB. We find that GPNMB interacts with α-synuclein and that loss of GPNMB leads to decreased synaptic α-synuclein and broad transcriptomic changes among synaptic genes. Lastly, we test the clinical utility of GPNMB as a biomarker in plasma and CSF samples from \u3e 800 PD patients and controls, finding that plasma GPNMB is elevated in PD and higher levels are found in PD patients with more severe disease. These results suggest a model whereby the rs199347 risk allele results in increased GPNMB expression which, through interactions with α-synuclein, leads to synaptic changes, thereby impacting risk for neurodegeneration. More broadly, this dissertation exemplifies an investigative approach combining computational analyses of large datasets with targeted bench-based experiments. Through this work, we nominate GHR as a promising PD biomarker and GPNMB as both a biomarker and potential therapeutic target in PD
Machine Learning Methods for the Analysis of Single-Cell and Spatially Resolved Transcriptomics Data
No full textThe advent of high-throughput next-generation sequencing technologies has transformed our understanding of cell biology and human disease. It is now common for investigators to study human cell populations by profiling the transcriptomes for thousands of single cells using single-cell RNA sequencing (scRNA-seq) technologies. In addition, recent advances in spatially resolved transcriptomics (SRT) technologies have enabled gene expression profiling with spatial information in tissues. Knowledge of the relative locations of different cells in a tissue is critical for understanding disease pathology because spatial information helps in understanding how the gene expression of a cell is influenced by its surrounding environment and how neighboring regions interact at the gene expression level. In order to take full advantage of the multi-modality information when analyzing scRNA-seq and SRT data, new methods are demanded for the following challenges: (1) how to identify cell types for scRNA-seq data with closely related cell types or low sequencing depths? (2) how to jointly model gene expression, spatial location, and histology in SRT data analysis? (3) how to increase gene expression resolution in SRT to study detailed tissue structure? In this dissertation, I seek to address these various challenges and difficulties associated with scRNA-seq and SRT data analyses. To address challenge (1), I developed ItClust, a supervised machine learning method that takes advantage of cell-type-specific gene expression information learned from a well-labeled source dataset, to help cluster and classify cell types on newly generated target data. To address challenge (2), I developed SpaGCN, a graph convolutional network approach that integrates gene expression, spatial location and histology to identify spatial domains and spatially variable genes in SRT data analysis. Lastly, to address challenge (3), I developed TESLA, a machine learning framework that enhances gene expression resolution in SRT and further performs multi-level tissue annotation with pixel-level resolution. I validated the utility of each of these approaches using experimentally validated cell type labels and independent pathologists’ annotation. I also demonstrated real use cases for these methods in deciphering tumor microenvironment in various cancer types
Prevotella Phylogeny: Genomic and Molecular Insights into the Role of the Human Commensal Prevotella in Cystic Fibrosis
No full textThe genus Prevotella comprises of a diverse set of gram-negative anaerobes that are implicated in both health and disease. Prevotella is a common human commensal of various anatomic sites but can also be associated with the dysbiotic microbiomes of various chronic inflammatory diseases. Due to it’s association with both commensalism and disease, the role of Prevotella in disease progression is unclear. However, Prevotella has shown immunomodulatory potential, the ability to change the metabolic microenvironment and other cytotoxic phenotypes in both in vitro and in vivo studies. Despite this, Prevotella remains understudied both at the genomic and phenotypic levels. In this thesis, we explore the role of Prevotella in the context of the cystic fibrosis lung microbiome. In this thesis we characterize the ecological composition of a longitudinal pediatric CF cohort called EcoCF and show that the prevalence and relative abundance of Prevotella remains relatively stable from mild to severe lung disease. Prevotella is often the dominant species in samples of higher bacterial diversity, which is associated with higher lung function but is also differentially enriched in samples of lower lung function, where bacterial diversity is low. We attempt to explain this contradictory result by exploring the genomic diversity in the genus Prevotella, with a focus on P. melaninogenica and its closely related species that are often associated with the CF lung. We show that P. melaninogenica is a complex of species with varied potential for horizontal gene transfer. Prevotella species have high recombination rates but also complex restriction-modification systems. Our work highlights the incredible genomic diversity within some of the oropharyngeal commensal Prevotella, indicating that the key to drawing meaningful associations of Prevotella with health and disease is by studying the genus Prevotella resolved at the strain-level
Capturing Complex Patterns of Association in Genetic Data: A Rule Based Machine Learning Approach to Survival Analysis
No full textGenetic heterogeneity, epistasis, and other complex genetic architectures underlie disease risk, survival, and other outcomes. Traditional statistical methods rely heavily on assumptions and struggle to detect these complex patterns. This dissertation applied unfamiliar approaches to familiar problems in genetic epidemiology to demonstrate the advantages of machine learning. In Aim 1, we applied Relief-based algorithms as a ranking scheme for enrichment analysis to generate hypotheses about the role of epistasis in conotruncal heart defects. We identified key pathways in the secondary heart field and cardiac neural crest cells that play a role in the development of the cardiac outflow tract. For Aim 2, we developed a method to specifically address genetic heterogeneity in survival data, avoiding the constraints of popular methods such as the Cox proportional hazards model. Our novel survival-Learning classifier system (LCS) fully accounts for right-censored observations, handles multiple feature types and missing data, and makes no assumptions about baseline hazard or survival distributions. LCSs are a type of rule-based machine learning algorithms that are uniquely suited to heterogeneous problem domains, but to date, have not been adapted for survival analysis. While most methods seek to develop a single model that represents all the data; LCSs evolve a generalizable and interpretable population of rules that flexibly models underlying interactions and heterogeneity. As proof of concept, we evaluated the survival-LCS on simulated genetic survival datasets of increasing complexity. The four genetic models included main effect, epistatic, additive, and heterogeneous models, simulated across a range of censoring proportions, minor allele frequencies, and number of features. The results of this sensitivity analysis demonstrated the ability of survival-LCS to identify complex patterns of association in survival data. Using integrated Brier scores as a performance metric, we showed that survival-LCS can also reliably predict survival times and distributions, potentially useful for clinical applications such as informing self-controls in single-arm clinical trials. Finally, in Aim 3, we applied the survival-LCS to GWAS data from a neuroblastoma cohort. This work introduces new approaches that are accessible to epidemiologists and others and provides a path forward for future methods developmen
Mechanical Stimuli-Defined TNFα Endocytosis Governs Mesenchymal Stem Cell Homeostasis
No full textTumor necrosis factor alpha (TNFα) is a pro-inflammatory cytokine responsible for immune regulation and is considered to execute its function mainly through its receptor- mediated canonical signal pathway. Mesenchymal stem cells (MSCs) are the heterogeneous primitive cells initially discovered residing in the adult bone marrow stroma, possessing self-renewal and multiple differentiation potential and critically maintaining multiple tissue/organ homeostasis. The interplay between MSCs and immune cytokines via the receptors on the MSC surface has increasingly been recognized; increasing evidence has shown that MSCs produce a certain amount of cytokines by themselves with little understanding of the role of MSC-derived cytokines.
In this study, we, for the first time, reveal a non-inflammatory, non-canonical role of MSC-derived TNFα by showing that TNFα-deficient MSCs exhibit impaired self-renewal and differentiation due to upregulated mTOR phosphorylation. Mature TNFα is internalized into the cytoplasm via endocytosis after being cleaved by the TNFα- converting enzyme and shedded into the extracellular microenvironment. We further find that cytoplasmic TNFα binds to Rictor, a component of mTOR complex 2, to restrain mTOR activation.
A complex regulatory network and signaling pathways are involved in governing MSC fate commitment. Mechanical stimuli, including physical cues from the matrix and applied forces, account for one critical extrinsic factor controlling MSC fate determinations. Microgravity conditions, such as astronauts in spaceflight missions and bedridden patients, are reported to result in progressive bone loss, but the therapeutics have yet to be established. In our study, we use hindlimb unloading (HU) mice to mimic the microgravity condition and find that HU mice resulted in reduced TNFα endocytosis and impaired cell function in MSCs as well as osteopenia phenotype. Rapamycin therapy rescues MSCs impairment and osteopenia in HU mice by blocking mTOR activation.
Collectively, our findings identify a previously unrecognized role of TNFα in maintaining MSC homeostasis via receptor-independent endocytosis to finetune mTOR signaling homeostasis. A mechanical stimuli-dependent and receptor-independent endocytosis of TNFα is required to maintain mTOR equilibrium and therefore safeguard MSC homeostasis. Rapamycin may be a promising therapy for hypodynamia-induced osteoporosis in astronauts and bedridden patients
Collation Model for Ms. Codex 320: Le epistole.
Get PDFThe letters attributed to Phalaris, translated from Greek into Latin by the humanist Francesco Aretino (also known as Francesco Griffolini), and from Latin into Italian by an unnamed translator (perhaps Giovanni Andrea Ferabos, or Bartholomeo Phontio [or Fontio]?). Includes a dedication of Francesco Aretino to Novello Malatesta.https://repository.upenn.edu/sims_models/1030/thumbnail.jp
Collation Model for Ms. Codex 273: Liber ethicorum Aristotelis.
Get PDFCompendium of Aristotle\u27s Ethics (both the Nicomachean and the Eudemian, or Great Ethics), followed by a list of the electors of the Holy Roman Empire (f. 66r).https://repository.upenn.edu/sims_models/1029/thumbnail.jp
Collation Model for Ms. Codex 98: Statuta Ecclesiae Tullensis.
Get PDFStatutes of the cathedral chapter in Toul. Also contains an inspeximus by the chapter, 14 January 1650 (f. 76r-77r); an excerpt from the registers of the chapter, 18 February 1695 (f. 77v-78v); Memoire des divers fondations de messes, 1635 (f. 82r-87r); and a table of contents for the statutes (f. 88v). Folios 79-81 are ruled but blank. A notarial note at the end is dated 2 May 1772 (f. 88v).https://repository.upenn.edu/sims_models/1026/thumbnail.jp
Changes in Retirement Savings During the COVID Pandemic
Get PDFThis paper documents changes in retirement saving patterns at the onset of the COVID-19 pandemic. We construct a large panel of US tax data, including tens of millions of person-year observations, and measure retirement savings contributions and withdrawals. We use these data to document several important changes in retirement savings patterns during the pandemic relative to prior years, and we compare these results to changes in savings patterns during the Great Recession. We find that, unlike during the Great Recession, contributions by individuals to retirement savings vehicles did not meaningfully decline. Additionally, driven by the suspension of required minimum distribution rules, IRA withdrawals substantially declined in 2020 for those older than age 72. Finally, likely due to the partial suspension of the early withdrawal penalty, employer-plan withdrawals increased for those under age 60