Integrative Statistical Methods for the Analysis of Transcriptomic and and Metabolomic Data.

Cancer research is embracing the multiple``-omics'' technologies available for global scale measurement of molecular events. Transcriptomics, as the global measure of gene expression, has been well developed through microarray technology. Metabolomics, an emerging omics field, involves chromatography-coupled mass spectrometry to measure the global activity of metabolites or small molecules. The aim of this dissertation is to integrate the analysis of these two data sources to enhance the ability to find molecular changes between two disease states. This work is motivated by a prostate cancer progression study in which tumor samples of varying stage and benign tissue were assessed for both gene expression and metabolomic levels. Using a pathway-directed approach, transcriptomics and metabolomics data can be mapped using publicly available metabolic pathways. Here we describe three integrative methods. In the first topic we begin with a classification method that utilizes a differential list of elements from a prior study to make prognostic or diagnostic predictions about samples in a current study. We extend the classification method by providing a testing scenario for the classifier. Though motivated by the integration of in vitro and in vivo gene expression datasets we show that it is applicable across omics platforms as well. The second topic explores the use of p-value weighting to improve the power of per-metabolite tests of differential intensity. We use gene-set enrichment testing to capture the gene expression information contained in pre-defined pathways. The results of these tests are used to devise pathway-based weights. In this way, metabolites that are involved in a pathway that is dysregulated in its gene expression are given higher importance. Finally, the third topic extends two univariate set enrichment tests to jointly search for sets of genes and metabolites that are coordinately differential. We compare these methods to their univariate counterparts and to enrichment testing on the concatenated datasets. In almost all scenarios explored, testing the datasets jointly is preferred. Each of these methods is applied to the motivating metabolomics and matched gene expression datasets and results are discussed.Ph.D.BiostatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75897/1/lpoisson_1.pd

Expression and regulatory roles of lncRNAs in G-CIMP-low vs G-CIMP-high Glioma: an in-silico analysis

BACKGROUND: Clinically relevant glioma subtypes, such as the glioma-CpG island methylator phenotype (G-CIMP), have been defined by epigenetics. In this study, the role of long non-coding RNAs in association with the poor-prognosis G-CMIP-low phenotype and the good-prognosis G-CMIP-high phenotype was investigated. Functional associations of lncRNAs with mRNAs and miRNAs were examined to hypothesize influencing factors of the aggressive phenotype. METHODS: RNA-seq data on 250 samples from TCGA\u27s Pan-Glioma study, quantified for lncRNA and mRNAs (GENCODE v28), were analyzed for differential expression between G-CIMP-low and G-CIMP-high phenotypes. Functional interpretation of the differential lncRNAs was performed by Ingenuity Pathway Analysis. Spearman rank order correlation estimates between lncRNA, miRNA, and mRNA nominated differential lncRNA with a likely miRNA sponge function. RESULTS: We identified 4371 differentially expressed features (mRNA = 3705; lncRNA = 666; FDR ≤ 5%). From these, the protein-coding gene TP53 was identified as an upstream regulator of differential lncRNAs PANDAR and PVT1 (p = 0.0237) and enrichment was detected in the development of carcinoma (p = 0.0176). Two lncRNAs (HCG11, PART1) were positively correlated with 342 mRNAs, and their correlation estimates diminish after adjusting for either of the target miRNAs: hsa-miR-490-3p, hsa-miR-129-5p. This suggests a likely sponge function for HCG11 and PART1. CONCLUSIONS: These findings identify differential lncRNAs with oncogenic features that are associated with G-CIMP phenotypes. Further investigation with controlled experiments is needed to confirm the molecular relationships

Factors Associated With Risk of Postdischarge Thrombosis in Patients With COVID-19

Importance: COVID-19 is associated with a high incidence of thrombotic events; however, the need for extended thromboprophylaxis after hospitalization remains unclear. Objective: To quantify the rate of postdischarge arterial and venous thromboembolism in patients with COVID-19, identify the factors associated with the risk of postdischarge venous thromboembolism, and evaluate the association of postdischarge anticoagulation use with venous thromboembolism incidence. Design, Setting, and Participants: This is a cohort study of adult patients hospitalized with COVID-19 confirmed by a positive SARS-CoV-2 test. Eligible patients were enrolled at 5 hospitals of the Henry Ford Health System from March 1 to November 30, 2020. Data analysis was performed from April to June 2021. Exposures: Anticoagulant therapy after discharge. Main Outcomes and Measures: New onset of symptomatic arterial and venous thromboembolic events within 90 days after discharge from the index admission for COVID-19 infection were identified using International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Results: In this cohort study of 2832 adult patients hospitalized with COVID-19, the mean (SD) age was 63.4 (16.7) years (IQR, 53-75 years), and 1347 patients (47.6%) were men. Thirty-six patients (1.3%) had postdischarge venous thromboembolic events (16 pulmonary embolism, 18 deep vein thrombosis, and 2 portal vein thrombosis). Fifteen (0.5%) postdischarge arterial thromboembolic events were observed (1 transient ischemic attack and 14 acute coronary syndrome). The risk of venous thromboembolism decreased with time (Mann-Kendall trend test, P \u3c .001), with a median (IQR) time to event of 16 (7-43) days. There was no change in the risk of arterial thromboembolism with time (Mann-Kendall trend test, P = .37), with a median (IQR) time to event of 37 (10-63) days. Patients with a history of venous thromboembolism (odds ratio [OR], 3.24; 95% CI, 1.34-7.86), peak dimerized plasmin fragment D (D-dimer) level greater than 3 μg/mL (OR, 3.76; 95% CI, 1.86-7.57), and predischarge C-reactive protein level greater than 10 mg/dL (OR, 3.02; 95% CI, 1.45-6.29) were more likely to experience venous thromboembolism after discharge. Prescriptions for therapeutic anticoagulation at discharge were associated with reduced incidence of venous thromboembolism (OR, 0.18; 95% CI, 0.04-0.75; P = .02). Conclusions and Relevance: Although extended thromboprophylaxis in unselected patients with COVID-19 is not supported, these findings suggest that postdischarge anticoagulation may be considered for high-risk patients who have a history of venous thromboembolism, peak D-dimer level greater than 3 μg/mL, and predischarge C-reactive protein level greater than 10 mg/dL, if their bleeding risk is low

Susceptibility scoring in family-based association testing

BACKGROUND: Family-based association testing is an important part of genetic epidemiology. Tests are available to include multiple siblings, unaffected offspring, and to adjust for environmental covariates. We explore a susceptibility residual method of adjustment for covariates. RESULTS: Through simulation, we show that environmental adjustments that down-weight persons who are "destined" to be affected decrease the power to detect genetic association. We used the residual adjusted method on the Framingham Heart Study offspring data, provided for Genetic Analysis Workshop 13, and got mixed results. CONCLUSION: When the genetic effect and environmental effects are independent, a susceptibility residual method of adjustment for environmental covariates reduces the power of the association test. Further study is necessary to determine if residual adjustment is appropriate in more complex disease models

Analysis of gene × environment interactions in sibships using mixed models

BACKGROUND: Gene × environment models are widely used to assess genetic and environmental risks and their association with a phenotype of interest for many complex diseases. Mixed generalized linear models were used to assess gene × environment interactions with respect to systolic blood pressure on sibships adjusting for repeated measures and hierarchical nesting structures. A data set containing 410 sibships from the Framingham Heart Study offspring cohort (part of the Genetic Analysis Workshop 13 data) was used for all analyses. Three mixed gene × environment models, all adjusting for repeated measurement and varying levels of nesting, were compared for precision of estimates: 1) all sibships with adjustment for two levels of nesting (sibs within sibships and sibs within pedigrees), 2) all sibships with adjustment for one level of nesting (sibs within sibships), and 3) 100 data sets containing random draws of one sibship per extended pedigree adjusting for one level of nesting. RESULTS: The main effects were: gender, baseline age, body mass index (BMI), hypertensive treatment, cigarettes per day, grams of alcohol per day, and marker GATA48G07A. The interaction fixed effects were: baseline age by gender, baseline age by cigarettes per day, baseline age by hypertensive treatment, baseline age by BMI, hypertensive treatment by BMI, and baseline age by marker GATA48G07A. The estimates for all three nesting techniques were not widely discrepant, but precision of estimates and determination of significant effects did change with the change in adjustment for nesting. CONCLUSION: Our results show the importance of the adjustment for all levels of hierarchical nesting of sibs in the presence of repeated measures

Proteomic Profiles of Exosomes of Septic Patients Presenting to the Emergency Department Compared to Healthy Controls

BACKGROUND: Septic Emergency Department (ED) patients provide a unique opportunity to investigate early sepsis. Recent work focuses on exosomes, nanoparticle-sized lipid vesicles (30-130 nm) that are released into the bloodstream to transfer its contents (RNA, miRNA, DNA, protein) to other cells. Little is known about how early changes related to exosomes may contribute to the dysregulated inflammatory septic response that leads to multi-organ dysfunction. We aimed to evaluate proteomic profiles of plasma derived exosomes obtained from septic ED patients and healthy controls. METHODS: This is a prospective observational pilot study evaluating a plasma proteomic exosome profile at an urban tertiary care hospital ED using a single venipuncture blood draw, collecting 40 cc Ethylenediaminetetraacetic acid (EDTA) blood. MEASUREMENTS: We recruited seven patients in the ED within 6 h of their presentation and five healthy controls. Plasma exosomes were isolated using the Invitrogen Total Exosome Isolation Kit. Exosome proteomic profiles were analyzed using fusion mass spectroscopy and Proteome Discoverer. Principal component analysis (PCA) and differential expression analysis (DEA) for sepsis versus control was performed. RESULTS: PCA of 261 proteins demonstrated septic patients and healthy controls were distributed in two groups. DEA revealed that 62 (23.8%) proteins differed between the exosomes of septic patients and healthy controls, CONCLUSION: Exosome proteomic profiles of septic ED patients differ from their healthy counterparts with regard to acute phase response and inflammation

MicroRNA and metabolomics signatures for adrenomyeloneuropathy disease severity

Adrenomyeloneuropathy (AMN), the slow progressive phenotype of adrenoleukodystrophy (ALD), has no clinical plasma biomarker for disease progression. This feasibility study aimed to determine whether metabolomics and micro-RNA in blood plasma provide a potential source of biomarkers for AMN disease severity. Metabolomics and RNA-seq were performed on AMN and healthy human blood plasma. Biomarker discovery and pathway analyses were performed using clustering, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and regression against patient\u27s clinical Expanded Disability Status Score (EDSS). Fourteen AMN and six healthy control samples were analyzed. AMN showed strong disease-severity-specific metabolic and miRNA clustering signatures. Strong, significant clinical correlations were shown for 7-alpha-hydroxy-3-oxo-4-cholestenoate (7-HOCA) (r (2) = 0.83, p \u3c 0.00001), dehydroepiandrosterone sulfate (DHEA-S; r (2) = 0.82, p \u3c 0.00001), hypoxanthine (r (2) = 0.82, p \u3c 0.00001), as well as miRNA-432-5p (r (2) = 0.68, p \u3c 0.00001). KEGG pathway comparison of mild versus severe disease identified affected downstream systems: GAREM, IGF-1, CALCRL, SMAD2&3, glutathione peroxidase, LDH, and NOS. This feasibility study demonstrates that miRNA and metabolomics are a source of potential plasma biomarkers for disease severity in AMN, providing both a disease signature and individual markers with strong clinical correlations. Network analyses of affected systems implicate differentially altered vascular, inflammatory, and oxidative stress pathways, suggesting disease-severity-specific mechanisms as a function of disease severity

SARS-COV-ATE risk assessment model for arterial thromboembolism in COVID-19

Patients with SARS-CoV-2 infection are at an increased risk of cardiovascular and thrombotic complications conferring an extremely poor prognosis. COVID-19 infection is known to be an independent risk factor for acute ischemic stroke and myocardial infarction (MI). We developed a risk assessment model (RAM) to stratify hospitalized COVID-19 patients for arterial thromboembolism (ATE). This multicenter, retrospective study included adult COVID-19 patients admitted between 3/1/2020 and 9/5/2021. Among 3531 patients from the training cohort, 15.5% developed acute in-hospital ATE, including stroke, MI, and other ATE, compared to 13.4% in the validation cohort. The 16-item final score was named SARS-COV-ATE (Sex: male = 1, Age [40-59 = 2, \u3e 60 = 4], Race: non-African American = 1, Smoking = 1 and Systolic blood pressure elevation = 1, Creatinine elevation = 1; Over the range: leukocytes/lactate dehydrogenase/interleukin-6, B-type natriuretic peptide = 1, Vascular disease (cardiovascular/cerebrovascular = 1), Aspartate aminotransferase = 1, Troponin-I [\u3e 0.04 ng/mL = 1, troponin-I \u3e 0.09 ng/mL = 3], Electrolytes derangement [magnesium/potassium = 1]). RAM had a good discrimination (training AUC 0.777, 0.756-0.797; validation AUC 0.766, 0.741-0.790). The validation cohort was stratified as low-risk (score 0-8), intermediate-risk (score 9-13), and high-risk groups (score ≥ 14), with the incidence of ATE 2.4%, 12.8%, and 33.8%, respectively. Our novel prediction model based on 16 standardized, commonly available parameters showed good performance in identifying COVID-19 patients at risk for ATE on admission

Social distancing during the COVID-19 pandemic: quantifying the practice in Michigan - a hotspot state early in the pandemic - using a volunteer-based online survey

BACKGROUND: Public Health policies related to social distancing efforts during the COVID-19 pandemic helped slow the infection rate. However, individual-level factors associated with social distancing are largely unknown. We sought to examine social distancing during the COVID-19 pandemic in Michigan, an infection hotspot state in the United States early in the pandemic. METHODS: Two surveys were distributed to Michigan residents via email lists and social media following COVID-19 related state mandates in March; 45,691 adults responded to the first survey and 8512 to the second. Staying home ≥ 3 out of 5 previous days defined having more social distancing. Logistic regression models were used to examine potential factors associated with more social distancing. RESULTS: Most respondents were women (86% in Survey 1, 87% in Survey 2). In Survey 1, 63% reported more social distancing, increasing to 78% in Survey 2. Female sex and having someone (or self) sick in the home were consistently associated with higher social distancing, while increasing age was positively associated in Survey 1 but negatively associated in Survey 2. Most respondents felt social distancing policies were important (88% in Survey 1; 91% in Survey 2). CONCLUSIONS: Michiganders responding to the surveys were both practicing and supportive of social distancing. State-level executive orders positively impacted behaviors early in the COVID-19 pandemic in Michigan. Additional supports are needed to help vulnerable populations practice social distancing, including older individuals

RTVP-1 regulates glioma cell migration and invasion via interaction with N-WASP and hnRNPK

Glioblastoma (GBM) are characterized by increased invasion into the surrounding normal brain tissue. RTVP-1 is highly expressed in GBM and regulates the migration and invasion of glioma cells. To further study RTVP-1 effects we performed a pull-down assay using His-tagged RTVP-1 followed by mass spectrometry and found that RTVP-1 was associated with the actin polymerization regulator, N-WASP. This association was further validated by co-immunoprecipitation and FRET analysis. We found that RTVP-1 increased cell spreading, migration and invasion and these effects were at least partly mediated by N-WASP. Another protein which was found by the pull-down assay to interact with RTVP-1 is hnRNPK. This protein has been recently reported to associate with and to inhibit the effect of N-WASP on cell spreading. hnRNPK decreased cell migration, spreading and invasion in glioma cells. Using co-immunoprecipitation we validated the interactions of hnRNPK with N-WASP and RTVP-1 in glioma cells. In addition, we found that overexpression of RTVP-1 decreased the association of N-WASP and hnRNPK. In summary, we report that RTVP-1 regulates glioma cell spreading, migration and invasion and that these effects are mediated via interaction with N-WASP and by interfering with the inhibitory effect of hnRNPK on the function of this protein