Medical Sciences Commons^™

Protocol To Identify The Core Gene Supported By An Essential Gene In E. Coli Bacteria Using A Genome-Wide Suppressor Screen, Isao Masuda, Ya-Ming Hou

Department of Biochemistry and Molecular Biology Faculty Papers

We describe here a genome-wide screening approach to identify the most critical core reaction among a network of many that are supported by an essential gene to establish cell viability. We describe steps for maintenance plasmid construction, knockout cell construction, and phenotype validation. We then detail isolation of suppressors, whole-genome sequencing analysis, and reconstruction of CRISPR mutants. We focus on E. coli trmD, which encodes an essential methyl transferase that synthesizes m1G37 on the 3'-side of the tRNA anticodon. For complete details on the use and execution of this protocol, please refer to Masuda et al. (2022).

Multi-Ancestry Genome-Wide Association Analyses Improve Resolution Of Genes And Pathways Influencing Lung Function And Chronic Obstructive Pulmonary Disease Risk, Nick Shrine, Abril G. Izquierdo, Jing Chen, Richard Packer, Robert J. Hall, Anna L. Guyatt, Chiara Batini, Rebecca J. Thompson, Chandan Puvuluri, Vidhi Malik, Brian D. Hobbs, Matthew Moll, Wonji Kim, Ruth Tal-Singer, Per Bakke, Katherine A. Fawcett, Catherine John, Kayesha Coley, Noemi Nicole Piga, Sinjini Sikdar, Martin D. Tobin, Et Al.

Mathematics & Statistics Faculty Publications

Lung-function impairment underlies chronic obstructive pulmonary disease (COPD) and predicts mortality. In the largest multi-ancestry genome-wide association meta-analysis of lung function to date, comprising 580,869 participants, we identified 1,020 independent association signals implicating 559 genes supported by ≥2 criteria from a systematic variant-to-gene mapping framework. These genes were enriched in 29 pathways. Individual variants showed heterogeneity across ancestries, age and smoking groups, and collectively as a genetic risk score showed strong association with COPD across ancestry groups. We undertook phenome-wide association studies for selected associated variants as well as trait and pathway-specific genetic risk scores to infer possible consequences of …

Genetics And Genomics Education Among Physician Assistants, Wesley Patterson

All Dissertations

This dissertation comprises five chapters to describe genetics and genomics education among physician assistant/associate (PA) students and practicing PAs. Chapter I introduces the gap in supply and demand of genetic services, the need for non-genetics healthcare providers to fill the gap, and the PA profession as a solution.

Chapter II is a rapid literature review that summarizes the available literature regarding genetics and genomics education for PAs. A paucity of literature exists to describe the current state of PA genetics-genomics education. The few studies retrieved describe content being taught in PA programs, the number of genetics-genomics contact hours PA students …

Sequence Analysis Methods For The Design Of Cancer Vaccines That Target Tumor-Specific Mutant Antigens (Neoantigens), Jasreet Hundal

Arts & Sciences Electronic Theses and Dissertations

The human adaptive immune system is programmed to distinguish between self and non-self proteins and if trained to recognize markers unique to a cancer, it may be possible to stimulate the selective destruction of cancer cells. Therapeutic cancer vaccines aim to boost the immune system by selectively increasing the population of T cells specifically targeted to the tumor-unique antigens, thereby initiating cancer cell death.. In the past, this approach has primarily focused on targeted selection of ‘shared’ tumor antigens, found across many patients. The advent of massively parallel sequencing and specialized analytical approaches has enabled more efficient characterization of tumor-specific …

Integrative Cancer Immunogenomic Analysis Of Serial Melanoma Biopsies Reveals Correlates Of Response And Resistance To Sequential Ctla-4 And Pd-1 Blockade Treatment, Whijae Roh

Dissertations & Theses (Open Access)

Melanoma is the most malignant form of skin cancer. The five-year survival rate for metastatic melanoma is 19.9%. Although targeted therapy of BRAF and MEK inhibitors were developed for melanoma, resistance to therapy is inevitable. Immune checkpoint blockade, which reverses the suppression of the immune system, on the other hand, has shown a durable response in 20-30% of patients with metastatic melanoma. However, more predictive and robust biomarkers of response to this therapy are still needed, and resistance mechanisms remain incompletely understood. To address this, we examined a cohort of metastatic melanoma patients treated with sequential checkpoint blockade against cytotoxic …

Integrative Bayesian Analysis Of High-Dimensional Multi-Platform Genomics Data, Wenting Wang, Veerabhadran Baladandayuthapani, Jeffrey S. Morris, Bradley M. Broom, Ganiraju C. Manyam, Kim-Anh Do

Jeffrey S. Morris

Motivation: Analyzing data from multi-platform genomics experiments combined with patients’ clinical outcomes helps us understand the complex biological processes that characterize a disease, as well as how these processes relate to the development of the disease. Current integration approaches that treat the data are limited in that they do not consider the fundamental biological relationships that exist among the data from platforms.

Statistical Model: We propose an integrative Bayesian analysis of genomics data (iBAG) framework for identifying important genes/biomarkers that are associated with clinical outcome. This framework uses a hierarchical modeling technique to combine the data obtained from multiple platforms …

Members’ Discoveries: Fatal Flaws In Cancer Research, Jeffrey S. Morris

Jeffrey S. Morris

A recent article published in The Annals of Applied Statistics (AOAS) by two MD Anderson researchers—Keith Baggerly and Kevin Coombes—dissects results from a highly-influential series of medical papers involving genomics-driven personalized cancer therapy, and outlines a series of simple yet fatal flaws that raises serious questions about the veracity of the original results. Having immediate and strong impact, this paper, along with related work, is providing the impetus for new standards of reproducibility in scientific research.

Statistical Contributions To Proteomic Research, Jeffrey S. Morris, Keith A. Baggerly, Howard B. Gutstein, Kevin R. Coombes

Jeffrey S. Morris

Proteomic profiling has the potential to impact the diagnosis, prognosis, and treatment of various diseases. A number of different proteomic technologies are available that allow us to look at many proteins at once, and all of them yield complex data that raise significant quantitative challenges. Inadequate attention to these quantitative issues can prevent these studies from achieving their desired goals, and can even lead to invalid results. In this chapter, we describe various ways the involvement of statisticians or other quantitative scientists in the study team can contribute to the success of proteomic research, and we outline some of the …

Bayesian Random Segmentationmodels To Identify Shared Copy Number Aberrations For Array Cgh Data, Veerabhadran Baladandayuthapani, Yuan Ji, Rajesh Talluri, Luis E. Nieto-Barajas, Jeffrey S. Morris

Jeffrey S. Morris

Array-based comparative genomic hybridization (aCGH) is a high-resolution high-throughput technique for studying the genetic basis of cancer. The resulting data consists of log fluorescence ratios as a function of the genomic DNA location and provides a cytogenetic representation of the relative DNA copy number variation. Analysis of such data typically involves estimation of the underlying copy number state at each location and segmenting regions of DNA with similar copy number states. Most current methods proceed by modeling a single sample/array at a time, and thus fail to borrow strength across multiple samples to infer shared regions of copy number aberrations. …

Detecting Outlier Genes From High-Dimensional Data: A Fuzzy Approach, Debashis Ghosh

Debashis Ghosh

A recent nding in cancer research has been the characterization of previously undis- covered chromosomal abnormalities in several types of solid tumors. This was found based on analyses of high-throughput data from gene expression microarrays and motivated the development of so-called `outlier' tests for dierential expression. One statistical issue was the potential discreteness of the test statistics. Using ideas from fuzzy set theory, we develop fuzzy outlier detection algorithms that have links to ideas in multiple comparisons. Two- and K-sample extensions are considered. The methodology is illustrated by application to two microarray studies.