The Expression of Irx7 in the Inner Nuclear Layer of Zebrafish Retina Is Essential for a Proper Retinal Development and Lamination
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THE ROLE of HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 in CARDIAC CONTRACTILITY and HYPERTROPHIC RESPONSE by © COPYRIGHT by KYOUNG H
THE ROLE OF HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 IN CARDIAC CONTRACTILITY AND HYPERTROPHIC RESPONSE By KYOUNG HAN KIM A THESIS SUBMITTED IN CONFORMITY WITH THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY GRADUATE DEPARTMENT OF PHYSIOLOGY UNIVERSITY OF TORONTO © COPYRIGHT BY KYOUNG HAN KIM (2011) THE ROLE OF HOMEODOMAIN TRANSCRIPTION FACTOR IRX5 IN CARDIAC CONTRACTILITY AND HYPERTROPHIC RESPONSE KYOUNG HAN KIM DOCTOR OF PHILOSOPHY GRADUATE DEPARTMENT OF PHYSIOLOGY UNIVERSITY OF TORONTO 2011 ABSTRACT Irx5 is a homeodomain transcription factor that negatively regulates cardiac fast transient + outward K currents (Ito,f) via the KV4.2 gene and is thereby a major determinant of the transmural repolarization gradient. While Ito,f is invariably reduced in heart disease and changes in Ito,f can modulate both cardiac contractility and hypertrophy, less is known about a functional role of Irx5, and its relationship with Ito,f, in the normal and diseased heart. Here I show that Irx5 plays crucial roles in the regulation of cardiac contractility and proper adaptive hypertrophy. Specifically, Irx5-deficient (Irx5-/-) hearts had reduced cardiac contractility and lacked the normal regional difference in excitation-contraction with decreased action potential duration, Ca2+ transients and myocyte shortening in sub-endocardial, but not sub-epicardial, myocytes. In addition, Irx5-/- mice showed less cardiac hypertrophy, but increased interstitial fibrosis and greater contractility impairment following pressure overload. A defect in hypertrophic responses in Irx5-/- myocardium was confirmed in cultured neonatal mouse ventricular myocytes, exposed to norepinephrine while being restored with Irx5 replacement. Interestingly, studies using mice ii -/- virtually lacking Ito,f (i.e. KV4.2-deficient) showed that reduced contractility in Irx5 mice was completely restored by loss of KV4.2, whereas hypertrophic responses to pressure-overload in hearts remained impaired when both Irx5 and Ito,f were absent. -
Table S1 the Four Gene Sets Derived from Gene Expression Profiles of Escs and Differentiated Cells
Table S1 The four gene sets derived from gene expression profiles of ESCs and differentiated cells Uniform High Uniform Low ES Up ES Down EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol EntrezID GeneSymbol 269261 Rpl12 11354 Abpa 68239 Krt42 15132 Hbb-bh1 67891 Rpl4 11537 Cfd 26380 Esrrb 15126 Hba-x 55949 Eef1b2 11698 Ambn 73703 Dppa2 15111 Hand2 18148 Npm1 11730 Ang3 67374 Jam2 65255 Asb4 67427 Rps20 11731 Ang2 22702 Zfp42 17292 Mesp1 15481 Hspa8 11807 Apoa2 58865 Tdh 19737 Rgs5 100041686 LOC100041686 11814 Apoc3 26388 Ifi202b 225518 Prdm6 11983 Atpif1 11945 Atp4b 11614 Nr0b1 20378 Frzb 19241 Tmsb4x 12007 Azgp1 76815 Calcoco2 12767 Cxcr4 20116 Rps8 12044 Bcl2a1a 219132 D14Ertd668e 103889 Hoxb2 20103 Rps5 12047 Bcl2a1d 381411 Gm1967 17701 Msx1 14694 Gnb2l1 12049 Bcl2l10 20899 Stra8 23796 Aplnr 19941 Rpl26 12096 Bglap1 78625 1700061G19Rik 12627 Cfc1 12070 Ngfrap1 12097 Bglap2 21816 Tgm1 12622 Cer1 19989 Rpl7 12267 C3ar1 67405 Nts 21385 Tbx2 19896 Rpl10a 12279 C9 435337 EG435337 56720 Tdo2 20044 Rps14 12391 Cav3 545913 Zscan4d 16869 Lhx1 19175 Psmb6 12409 Cbr2 244448 Triml1 22253 Unc5c 22627 Ywhae 12477 Ctla4 69134 2200001I15Rik 14174 Fgf3 19951 Rpl32 12523 Cd84 66065 Hsd17b14 16542 Kdr 66152 1110020P15Rik 12524 Cd86 81879 Tcfcp2l1 15122 Hba-a1 66489 Rpl35 12640 Cga 17907 Mylpf 15414 Hoxb6 15519 Hsp90aa1 12642 Ch25h 26424 Nr5a2 210530 Leprel1 66483 Rpl36al 12655 Chi3l3 83560 Tex14 12338 Capn6 27370 Rps26 12796 Camp 17450 Morc1 20671 Sox17 66576 Uqcrh 12869 Cox8b 79455 Pdcl2 20613 Snai1 22154 Tubb5 12959 Cryba4 231821 Centa1 17897 -
Understanding Chronic Kidney Disease: Genetic and Epigenetic Approaches
University of Pennsylvania ScholarlyCommons Publicly Accessible Penn Dissertations 2017 Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Yi-An Ko Ko University of Pennsylvania, [email protected] Follow this and additional works at: https://repository.upenn.edu/edissertations Part of the Bioinformatics Commons, Genetics Commons, and the Systems Biology Commons Recommended Citation Ko, Yi-An Ko, "Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches" (2017). Publicly Accessible Penn Dissertations. 2404. https://repository.upenn.edu/edissertations/2404 This paper is posted at ScholarlyCommons. https://repository.upenn.edu/edissertations/2404 For more information, please contact [email protected]. Understanding Chronic Kidney Disease: Genetic And Epigenetic Approaches Abstract The work described in this dissertation aimed to better understand the genetic and epigenetic factors influencing chronic kidney disease (CKD) development. Genome-wide association studies (GWAS) have identified single nucleotide polymorphisms (SNPs) significantly associated with chronic kidney disease. However, these studies have not effectively identified target genes for the CKD variants. Most of the identified variants are localized to non-coding genomic regions, and how they associate with CKD development is not well-understood. As GWAS studies only explain a small fraction of heritability, we hypothesized that epigenetic changes could explain part of this missing heritability. To identify potential gene targets of the genetic variants, we performed expression quantitative loci (eQTL) analysis, using genotyping arrays and RNA sequencing from human kidney samples. To identify the target genes of CKD-associated SNPs, we integrated the GWAS-identified SNPs with the eQTL results using a Bayesian colocalization method, coloc. This resulted in a short list of target genes, including PGAP3 and CASP9, two genes that have been shown to present with kidney phenotypes in knockout mice. -
Integrative Analysis of Single-Cell Genomics Data by Coupled Nonnegative Matrix Factorizations
Integrative analysis of single-cell genomics data by coupled nonnegative matrix factorizations Zhana Durena,b,1, Xi Chena,b,1, Mahdi Zamanighomia,b,c,1, Wanwen Zenga,b,d, Ansuman T. Satpathyc, Howard Y. Changc, Yong Wange,f, and Wing Hung Wonga,b,c,2 aDepartment of Statistics, Stanford University, Stanford, CA 94305; bDepartment of Biomedical Data Science, Stanford University, Stanford, CA 94305; cCenter for Personal Dynamic Regulomes, Stanford University, Stanford, CA 94305; dMinistry of Education Key Laboratory of Bioinformatics, Bioinformatics Division and Center for Synthetic & Systems Biology, Department of Automation, Tsinghua University, 100084 Beijing, China; eAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, 100080 Beijing, China; and fCenter for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, 650223 Kunming, China Contributed by Wing Hung Wong, June 14, 2018 (sent for review April 4, 2018; reviewed by Andrew D. Smith and Nancy R. Zhang) When different types of functional genomics data are generated on Approach single cells from different samples of cells from the same hetero- Coupled NMF. We first introduce our approach in general terms. geneous population, the clustering of cells in the different samples Let O be a p1 by n1 matrix representing data on p1 features for n1 should be coupled. We formulate this “coupled clustering” problem units in the first sample; then a “soft” clustering of the units in as an optimization problem and propose the method of coupled this sample can be obtained from a nonnegative factorization nonnegative matrix factorizations (coupled NMF) for its solution. O = W1H1 as follows: The ith column of W1 gives the mean The method is illustrated by the integrative analysis of single-cell vector for the ith cluster of units, while the jth column of H1 gives RNA-sequencing (RNA-seq) and single-cell ATAC-sequencing (ATAC- the assignment weights of the jth unit to the different clusters. -
Fig1-13Tab1-5.Pdf
Supplementary Information Promoter hypomethylation of EpCAM-regulated bone morphogenetic protein genes in advanced endometrial cancer Ya-Ting Hsu, Fei Gu, Yi-Wen Huang, Joseph Liu, Jianhua Ruan, Rui-Lan Huang, Chiou-Miin Wang, Chun-Liang Chen, Rohit R. Jadhav, Hung-Cheng Lai, David G. Mutch, Paul J. Goodfellow, Ian M. Thompson, Nameer B. Kirma, and Tim Hui-Ming Huang Tables of contents Page Table of contents 2 Supplementary Methods 4 Supplementary Figure S1. Summarized sequencing reads and coverage of MBDCap-seq 8 Supplementary Figure S2. Reproducibility test of MBDCap-seq 10 Supplementary Figure S3. Validation of MBDCap-seq by MassARRAY analysis 11 Supplementary Figure S4. Distribution of differentially methylated regions (DMRs) in endometrial tumors relative to normal control 12 Supplementary Figure S5. Network analysis of differential methylation loci by using Steiner-tree analysis 13 Supplementary Figure S6. DNA methylation distribution in early and late stage of the TCGA endometrial cancer cohort 14 Supplementary Figure S7. Relative expression of BMP genes with EGF treatment in the presence or absence of PI3K/AKT and Raf (MAPK) inhibitors in endometrial cancer cells 15 Supplementary Figure S8. Induction of invasion by EGF in AN3CA and HEC1A cell lines 16 Supplementary Figure S9. Knockdown expression of BMP4 and BMP7 in RL95-2 cells 17 Supplementary Figure S10. Relative expression of BMPs and BMPRs in normal endometrial cell and endometrial cancer cell lines 18 Supplementary Figure S11. Microfluidics-based PCR analysis of EMT gene panel in RL95-2 cells with or without EGF treatment 19 Supplementary Figure S12. Knockdown expression of EpCAM by different shRNA sequences in RL95-2 cells 20 Supplementary Figure S13. -
In Vitro Targeting of Transcription Factors to Control the Cytokine Release Syndrome in 2 COVID-19 3
bioRxiv preprint doi: https://doi.org/10.1101/2020.12.29.424728; this version posted December 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 1 In vitro Targeting of Transcription Factors to Control the Cytokine Release Syndrome in 2 COVID-19 3 4 Clarissa S. Santoso1, Zhaorong Li2, Jaice T. Rottenberg1, Xing Liu1, Vivian X. Shen1, Juan I. 5 Fuxman Bass1,2 6 7 1Department of Biology, Boston University, Boston, MA 02215, USA; 2Bioinformatics Program, 8 Boston University, Boston, MA 02215, USA 9 10 Corresponding author: 11 Juan I. Fuxman Bass 12 Boston University 13 5 Cummington Mall 14 Boston, MA 02215 15 Email: [email protected] 16 Phone: 617-353-2448 17 18 Classification: Biological Sciences 19 20 Keywords: COVID-19, cytokine release syndrome, cytokine storm, drug repurposing, 21 transcriptional regulators 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.12.29.424728; this version posted December 30, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license. 22 Abstract 23 Treatment of the cytokine release syndrome (CRS) has become an important part of rescuing 24 hospitalized COVID-19 patients. Here, we systematically explored the transcriptional regulators 25 of inflammatory cytokines involved in the COVID-19 CRS to identify candidate transcription 26 factors (TFs) for therapeutic targeting using approved drugs. -
CENTOGENE's Severe and Early Onset Disorder Gene List
CENTOGENE’s severe and early onset disorder gene list USED IN PRENATAL WES ANALYSIS AND IDENTIFICATION OF “PATHOGENIC” AND “LIKELY PATHOGENIC” CENTOMD® VARIANTS IN NGS PRODUCTS The following gene list shows all genes assessed in prenatal WES tests or analysed for P/LP CentoMD® variants in NGS products after April 1st, 2020. For searching a single gene coverage, just use the search on www.centoportal.com AAAS, AARS1, AARS2, ABAT, ABCA12, ABCA3, ABCB11, ABCB4, ABCB7, ABCC6, ABCC8, ABCC9, ABCD1, ABCD4, ABHD12, ABHD5, ACACA, ACAD9, ACADM, ACADS, ACADVL, ACAN, ACAT1, ACE, ACO2, ACOX1, ACP5, ACSL4, ACTA1, ACTA2, ACTB, ACTG1, ACTL6B, ACTN2, ACVR2B, ACVRL1, ACY1, ADA, ADAM17, ADAMTS2, ADAMTSL2, ADAR, ADARB1, ADAT3, ADCY5, ADGRG1, ADGRG6, ADGRV1, ADK, ADNP, ADPRHL2, ADSL, AFF2, AFG3L2, AGA, AGK, AGL, AGPAT2, AGPS, AGRN, AGT, AGTPBP1, AGTR1, AGXT, AHCY, AHDC1, AHI1, AIFM1, AIMP1, AIPL1, AIRE, AK2, AKR1D1, AKT1, AKT2, AKT3, ALAD, ALDH18A1, ALDH1A3, ALDH3A2, ALDH4A1, ALDH5A1, ALDH6A1, ALDH7A1, ALDOA, ALDOB, ALG1, ALG11, ALG12, ALG13, ALG14, ALG2, ALG3, ALG6, ALG8, ALG9, ALMS1, ALOX12B, ALPL, ALS2, ALX3, ALX4, AMACR, AMER1, AMN, AMPD1, AMPD2, AMT, ANK2, ANK3, ANKH, ANKRD11, ANKS6, ANO10, ANO5, ANOS1, ANTXR1, ANTXR2, AP1B1, AP1S1, AP1S2, AP3B1, AP3B2, AP4B1, AP4E1, AP4M1, AP4S1, APC2, APTX, AR, ARCN1, ARFGEF2, ARG1, ARHGAP31, ARHGDIA, ARHGEF9, ARID1A, ARID1B, ARID2, ARL13B, ARL3, ARL6, ARL6IP1, ARMC4, ARMC9, ARSA, ARSB, ARSL, ARV1, ARX, ASAH1, ASCC1, ASH1L, ASL, ASNS, ASPA, ASPH, ASPM, ASS1, ASXL1, ASXL2, ASXL3, ATAD3A, ATCAY, ATIC, ATL1, ATM, ATOH7, -
Engineered Type 1 Regulatory T Cells Designed for Clinical Use Kill Primary
ARTICLE Acute Myeloid Leukemia Engineered type 1 regulatory T cells designed Ferrata Storti Foundation for clinical use kill primary pediatric acute myeloid leukemia cells Brandon Cieniewicz,1* Molly Javier Uyeda,1,2* Ping (Pauline) Chen,1 Ece Canan Sayitoglu,1 Jeffrey Mao-Hwa Liu,1 Grazia Andolfi,3 Katharine Greenthal,1 Alice Bertaina,1,4 Silvia Gregori,3 Rosa Bacchetta,1,4 Norman James Lacayo,1 Alma-Martina Cepika1,4# and Maria Grazia Roncarolo1,2,4# Haematologica 2021 Volume 106(10):2588-2597 1Department of Pediatrics, Division of Stem Cell Transplantation and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 2Stanford Institute for Stem Cell Biology and Regenerative Medicine, Stanford School of Medicine, Stanford, CA, USA; 3San Raffaele Telethon Institute for Gene Therapy, Milan, Italy and 4Center for Definitive and Curative Medicine, Stanford School of Medicine, Stanford, CA, USA *BC and MJU contributed equally as co-first authors #AMC and MGR contributed equally as co-senior authors ABSTRACT ype 1 regulatory (Tr1) T cells induced by enforced expression of interleukin-10 (LV-10) are being developed as a novel treatment for Tchemotherapy-resistant myeloid leukemias. In vivo, LV-10 cells do not cause graft-versus-host disease while mediating graft-versus-leukemia effect against adult acute myeloid leukemia (AML). Since pediatric AML (pAML) and adult AML are different on a genetic and epigenetic level, we investigate herein whether LV-10 cells also efficiently kill pAML cells. We show that the majority of primary pAML are killed by LV-10 cells, with different levels of sensitivity to killing. Transcriptionally, pAML sensitive to LV-10 killing expressed a myeloid maturation signature. -
Transcriptional and Epigenetic Control of Brown and Beige Adipose Cell Fate and Function
REVIEWS Transcriptional and epigenetic control of brown and beige adipose cell fate and function Takeshi Inagaki1,2, Juro Sakai1,2 and Shingo Kajimura3 Abstract | White adipocytes store excess energy in the form of triglycerides, whereas brown and beige adipocytes dissipate energy in the form of heat. This thermogenic function relies on the activation of brown and beige adipocyte-specific gene programmes that are coordinately regulated by adipose-selective chromatin architectures and by a set of unique transcriptional and epigenetic regulators. A number of transcriptional and epigenetic regulators are also required for promoting beige adipocyte biogenesis in response to various environmental stimuli. A better understanding of the molecular mechanisms governing the generation and function of brown and beige adipocytes is necessary to allow us to control adipose cell fate and stimulate thermogenesis. This may provide a therapeutic approach for the treatment of obesity and obesity-associated diseases, such as type 2 diabetes. Interscapular BAT Adipose tissue has a central role in whole-body energy subjects who had previously lacked detectable BAT Brown adipose tissue (BAT) is a homeostasis. White adipose tissue (WAT) is the major depots before cold exposure, presumably owing to the specialized organ that adipose organ in mammals. It represents 10% or more emergence of new thermogenic adipocytes. This, then, produces heat. BAT is localized of the body weight of healthy adult humans and is leads to an increase in non-shivering thermogenesis in the interscapular and 6–9 perirenal regions of rodents specialized for the storage of excess energy. Humans and/or an improvement in insulin sensitivity . These and infants. -
POGLUT1, the Putative Effector Gene Driven by Rs2293370 in Primary
www.nature.com/scientificreports OPEN POGLUT1, the putative efector gene driven by rs2293370 in primary biliary cholangitis susceptibility Received: 6 June 2018 Accepted: 13 November 2018 locus chromosome 3q13.33 Published: xx xx xxxx Yuki Hitomi 1, Kazuko Ueno2,3, Yosuke Kawai1, Nao Nishida4, Kaname Kojima2,3, Minae Kawashima5, Yoshihiro Aiba6, Hitomi Nakamura6, Hiroshi Kouno7, Hirotaka Kouno7, Hajime Ohta7, Kazuhiro Sugi7, Toshiki Nikami7, Tsutomu Yamashita7, Shinji Katsushima 7, Toshiki Komeda7, Keisuke Ario7, Atsushi Naganuma7, Masaaki Shimada7, Noboru Hirashima7, Kaname Yoshizawa7, Fujio Makita7, Kiyoshi Furuta7, Masahiro Kikuchi7, Noriaki Naeshiro7, Hironao Takahashi7, Yutaka Mano7, Haruhiro Yamashita7, Kouki Matsushita7, Seiji Tsunematsu7, Iwao Yabuuchi7, Hideo Nishimura7, Yusuke Shimada7, Kazuhiko Yamauchi7, Tatsuji Komatsu7, Rie Sugimoto7, Hironori Sakai7, Eiji Mita7, Masaharu Koda7, Yoko Nakamura7, Hiroshi Kamitsukasa7, Takeaki Sato7, Makoto Nakamuta7, Naohiko Masaki 7, Hajime Takikawa8, Atsushi Tanaka 8, Hiromasa Ohira9, Mikio Zeniya10, Masanori Abe11, Shuichi Kaneko12, Masao Honda12, Kuniaki Arai12, Teruko Arinaga-Hino13, Etsuko Hashimoto14, Makiko Taniai14, Takeji Umemura 15, Satoru Joshita 15, Kazuhiko Nakao16, Tatsuki Ichikawa16, Hidetaka Shibata16, Akinobu Takaki17, Satoshi Yamagiwa18, Masataka Seike19, Shotaro Sakisaka20, Yasuaki Takeyama 20, Masaru Harada21, Michio Senju21, Osamu Yokosuka22, Tatsuo Kanda 22, Yoshiyuki Ueno 23, Hirotoshi Ebinuma24, Takashi Himoto25, Kazumoto Murata4, Shinji Shimoda26, Shinya Nagaoka6, Seigo Abiru6, Atsumasa Komori6,27, Kiyoshi Migita6,27, Masahiro Ito6,27, Hiroshi Yatsuhashi6,27, Yoshihiko Maehara28, Shinji Uemoto29, Norihiro Kokudo30, Masao Nagasaki2,3,31, Katsushi Tokunaga1 & Minoru Nakamura6,7,27,32 Primary biliary cholangitis (PBC) is a chronic and cholestatic autoimmune liver disease caused by the destruction of intrahepatic small bile ducts. Our previous genome-wide association study (GWAS) identifed six susceptibility loci for PBC. -
Analysis of Gene Expression in Wild Type and Notch1 Mutant Retinal Cells by Single Cell Profiling Karolina Mizeracka1, Jeffrey M
Research Article Developmental Dynamics DOI 10.1002/dvdy.24006 Analysis of gene expression in wild type and Notch1 mutant retinal cells by single cell profiling Karolina Mizeracka1, Jeffrey M. Trimarchi1,2, Michael B. Stadler3, Constance L. Cepko1,4 1 Department of Genetics, Department of Ophthalmology, Harvard Medical School, Boston, MA 02115 2 Current Address: Department of Genetics, Development and Cell Biology, Iowa State University, Ames, IA 50014 3 Friedrich Miescher Institute for Biomedical Research, 4058 Basel, Switzerland 4 Howard Hughes Medical Institute, Department of Genetics, Department of Ophthalmology, Harvard Medical School, Boston, MA 02115 Correspondence: Constance Cepko 77 Avenue Louis Pasteur, Boston, MA 02115 Phone: (617) 432-7618 Fax: (617) 432-7595 Running title: Single cell profiling of Notch1 mutant retinal cells Key words: retina, progenitor, microarray, cell fate Summary: • Profiling of individual Notch1 deficient and wild type postnatal retinal cells on microarrays reveals changes in gene expression obscured by whole tissue analysis • Notch1 deficient cells downregulate progenitor and cell cycle markers with a concomitant upregulation in early rod photoreceptor markers • Based on classification, single Notch1 deficient and wild type cells represent Developmental Dynamics transition from progenitor to postmitotic cell • Individual wild type retinal cells express cell type markers of both photoreceptors and interneurons Grant sponsor and number: National Institutes of Health Grant R01EY09676 Accepted Articles are accepted, unedited articles for future issues, temporarily published online in advance of the final edited version. © 2013 Wiley Periodicals, Inc. Received: Mar 04, 2013; Revised: May 02, 2013; Accepted: May 13, 2013 Developmental Dynamics Page 2 of 66 Abstract Background: The vertebrate retina comprises sensory neurons, the photoreceptors, as well as many other types of neurons and one type of glial cell. -
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! ii! Acknowledgements Lots of people have helped me get to where I am today and I apologize if I have left anyone out. First, I would like to thank my mother and father for always pushing me to try harder and encouraging me to persist in my scientific career. Both of my parents encouraged me to think like a scientist from a young age. I don’t think I would be here today without that early upbringing. Joining the Pollard lab was the best decision I made in graduate school. I can’t emphasize the importance of finding a good mentor in school. My advisor, Katherine Pollard, provided me with a model of leadership that I will carry with me for the rest of my career. Additionally, I would like to emphasize that the lab is full of driven individuals that have supported me in all my scientific endeavors. Fellow graduate students, Aram Avila-Herrera and Genevieve Erwin Haliburton, guided much of my direction during the early years. In addition, postdoctoral scholars Nandita Garud, Hassan Samee, Patrick Bradley, and Geoffrey Fudenberg were key in helping me prepare for my future steps in my career. I want to thank my committee members for sitting through long meetings and giving me the feedback I needed. Nadav Ahituv, Benoit Bruneau, and Jeff Wall have given me valuable advice on whether to pursue certain directions in my research. I also want to acknowledge all the individuals that supported me personally in school. In particular, I have always valued Sara Calhoun’s well-thought-out advice and Rose ! iii! Citron’s insight when making important decisions.