DOCSLIB.ORG

Building Models of Small DNA Control Elements for Prediction Of

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Building Models of Small DNA Control Elements for Prediction Of BUILDING MODELS OF SMALL DNA CONTROL ELEMENTS FOR PREDICTION OF TRANSCRIPTION FACTOR ACTIVITY A THESIS SUBMITTED TO THE UNIVERSITY OF MANCHESTER FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN THE FACULTY OF SCIENCE AND ENGINEERING 2020 By Jose´ Luis Hernandez´ Dom´ınguez School of Computer Science Contents Abstract 11 Declaration 12 Copyright 13 Acknowledgements 14 1 Introduction 15 1.1 Motivation . 16 1.2 Contribution of the thesis . 17 1.2.1 Contributions breakdown . 20 1.3 Thesis overview . 21 2 Background information 23 2.1 Biological background . 23 2.1.1 Cells . 24 2.1.2 Molecular biology central dogma . 25 2.1.3 Control elements of protein regulation . 30 2.1.4 Experimental methods for extracting transcription factors in- teractions . 34 2.1.4.1 ChIP-chip . 34 2.1.4.2 ChIP-seq . 35 2.1.4.3 Homology and orthology . 38 2.1.5 The regulatory network of transcription factors . 39 2.2 Networks: connecting the world . 41 2 2.2.1 Topological properties of networks . 42 2.2.2 Complex networks models . 43 2.2.3 Biological networks . 47 2.3 Mathematical modelling of the TF regulatory network . 48 2.4 Transcription Factors basal regulatory network . 52 2.4.1 Historical background . 52 2.4.2 Modelling the basal regulatory network . 54 2.4.3 Interaction network and strength interaction extraction method . 60 2.4.4 Self-loops and motifs . 62 3 Methodology 66 3.1 Chapter overview . 66 3.2 Complexity reduction . 70 3.2.1 Streamline of known control processes . 71 3.2.2 Biological model simplification . 72 3.3 Building the transcription factor network . 73 3.3.1 Experimental model database . 73 3.3.2 Identifying the databases for the TFs basal regulatory network 75 3.3.3 Data extraction for the human model . 79 3.3.4 Creating the TFBRN . 80 3.3.5 CLIQUE database . 84 3.3.6 Cancer related databases . 85 3.3.7 Data extraction for the yeast model . 85 3.4 Mathematical model . 86 3.5 Impact of the network . 89 3.5.1 Standard impact . 90 3 3.5.2 Alternative impact . 92 3.5.3 Eigencentrality . 94 3.6 Impact of the links . 95 3.7 Experimental design . 96 4 Exploration of the model 101 4.1 Exploration of the RAW model . 101 4.2 Using the RAW model as measure of centrality . 107 5 Results and discussion of the human model 113 5.1 Analysis of the topology of the network . 114 5.2 Impacts of the network . 116 5.2.1 Impacts of the network based on the RAW model . 116 5.2.2 Relationship between topological characteristics, the impacts of the network and VRD . 117 5.2.3 Impacts and Self-loops . 120 5.2.4 Correlation by Self-loops . 121 5.3 Impact of the network using the Prime model . 125 5.3.1 Correlations of impacts with VRD . 126 5.3.1.1 Correlation of impacts with VRD: only self-loops . 128 5.3.1.2 Correlation of impacts with VRD: without self-loops 130 5.3.2 Correlation of impacts with VRD: positive self-loops . 131 5.3.3 Correlation of impacts with VRD: Negative self-loops . 133 5.4 Self-loops structure experiments . 135 6 Results and discussion of the yeast model 140 6.1 Analysis of the topology of the network . 143 6.2 Impacts of the network . 145 4 6.2.1 Impacts of the network based on the RAW model . 145 6.2.2 Correlation between characteristics and phenotype . 147 6.2.3 Impact and self-loops . 148 6.2.4 Correlation by self-loop . 149 6.3 Impacts of the network using the Prime model . 151 6.3.1 Impacts and Phenotype . 152 6.3.2 Impacts and Phenotype - positive self-loops . 154 6.3.3 Impacts and Phenotype - negative self-loops . 155 6.4 Self-loops analysis . 156 7 Discussion 159 7.1 Main findings . 159 7.1.1 Yeast main findings and contributions . 161 7.2 Results comparative with existing research . 163 7.3 Limitation of the thesis . 164 7.4 Implications of the study . 167 7.5 Future work . 168 Bibliography 170 A Human results 204 B Yeast results 220 5 List of Tables 2.1 Databases analysis . 59 2.1 Databases analysis . 60 3.1 Databases analysis . 76 3.1 Databases analysis . 77 5.1 Correlation: Impacts, topological characteristics, and cancer on full network — Human . 118 5.2 Correlation: Impacts, topological characteristics, and Diseases on full network — Human . 119 5.3 Correlation between impacts, topological characteristics and eigencen- tralities — Human . 120 5.4 Correlation: Impact of the network, topological characteristics, and cancer on self-loops network — Human . 122 5.5 Correlation: Impact of the network, topological characteristics, and diseases on self-loops network — Human . 123 5.6 Correlation: Impact of the network, topological characteristics, and cancer on non-self-loops network — Human . 124 5.7 Correlation: Impacts, topological characteristics, and cancer on non- self-loops network — Human . 124 6.1 Correlation: Impacts, topological characteristics, and Phenotype — Yeast147 6.2 Correlation: Impacts and topological characteristics — Yeast . 148 6.3 Correlation: Impacts and topological characteristics with self-loops — Yeast . 150 6 6.4 Correlation: Impacts and topological characteristics without self-loops — Yeast . 151 7 List of Figures 2.1 Structure of the cell . 25 2.2 Molecular biology central dogma as proposed by Crick . 26 2.3 Simplified model of the molecular biology dogma . 26 2.4 Gene structure . 27 2.5 Transcription process . 28 2.6 Translation process . 29 2.7 DNA compression due to histones modification . 31 2.8 Reference of one ChIP-seq process part . 37 2.9 Simplified example of a genome track . 38 2.10 Transcription factors regulatory network . 40 2.11 Basal regulatory network . 40 2.12 Elements of a network . 41 2.13 Random network . 45 2.14 Small-world network . 46 2.15 Scale-free network . 47 3.1 Complexity reduction of the model . 72 3.2 Transcription factor concentration control flow . 73 3.3 File output from the extraction of each database . 81 3.4 Creation of the adjacency matrix . 82 3.5 CLIQUE disease-gene matching . 84 3.6 Flow of a network . 94 4.1 All subnetworks and networks for the analysis . 102 4.2 Uniform single value model analysis . 103 8 4.3 Randomised initial condition for the model analysis . 104 4.4 Randomised initial conditions of the human subnetwork run 1,000 times 104 4.5 Randomised initial conditions of the human random network run 1,000 times . 105 4.6 Randomised initial conditions of the yeast subnetwork run 1,000 times 106 4.7 Randomised initial conditions of the yeast random network run 1,000 times . 106 4.8 Example of the impact of two TFs in the subnetwork . 108 4.9 Example of the impact difference of two TFs in the subnetwork . 109 4.10 Comparison of the different centrality measures of the human subnetwork110 4.11 Comparison of the different centrality measures of the human random network . 110 4.12 Comparison of the different centrality measures of the yeast subnetwork 111 4.13 Comparison of the different centrality measures of the yeast random network . 111 5.1 Network representation — Human . 114 5.2 In-degree distribution — Human . 115 5.3 Out-degree distribution — Human . 115 5.4 Sorted standard impact, full network — Human . 116 5.5 Sorted alternative impact, full network — Human . 117 5.6 Sorted standard impact, full network self-loops highlighted — Human 121 5.7 Sorted alternative impact, full network self-loops highlighted — Human 121 5.8 Self-loops distribution — Human . 126 5.9 Standard Impact on full network — Human . 127 5.10 Alternative Impact on full network — Human . 128 5.11 Standard Impact on TFs with self-loops — Human . 129 9 5.12 Alternative Impact on TFs with self-loops — Human . 129 5.13 Standard Impact on TFs without self-loops — Human . ..
Load more

Recommended publications
  • Supplemental Materials
    SUPPLEMENTAL MATERIALS Simple biochemical features underlie transcriptional activation domain diversity and dynamic, fuzzy binding to Mediator Adrian L. Sanborn,1,2,* Benjamin T. Yeh,2 Jordan T. Feigerle,1 Cynthia V. Hao,1 Raphael J. L. Townshend,2 Erez Lieberman Aiden,3,4 Ron O. Dror,2 Roger D. Kornberg1,*,+ 1Department of Structural Biology, Stanford University School of Medicine, Stanford, CA 94305, USA 2Department of Computer Science, Stanford University, Stanford, CA 94305, USA 3The Center for Genome Architecture, Baylor College of Medicine, Houston, USA 4Center for Theoretical Biological Physics, Rice University, Houston, USA *Correspondence: [email protected], [email protected] +Lead Contact 1 A B Empty GFP Count Count Count VP16 AD mCherry mCherry GFP C G Count 2 AD tiles 10 Random sequences 2 12 10 10 Gcn4 AD 8 1 10 1 6 10 Z-score Activation 4 Count 2 0 Activation, second library 0 10 0 10 Gal4 Gln3 Hap4 Ino2 Ino2 Msn2 Oaf1 Pip2 VP16 VP64 r = 0.952 Pho4 AD 147 104 112 1 108 234 995 944 0 1 2 Synonymously encoded control ADs 10 10 10 Activation, first library Count H 102 GFP 101 D E Empty VP16 activation 100 80% Mean positional VP16 AD 0 200 400 600 800 1000 1200 60% Position in Adr1 protein 40% By FACS GFP By sequencing I 20% 50% 0% Percent of cells 1 2 3 4 5 6 7 8 mCherry mCherry 25% 80% Gcn4 AD 60% Empty VP16 0% 40% AD at position with Percent of proteins 0.0 0.2 0.4 0.6 0.8 1.0 20% Position on protein, normalized 0% Percent of cells 100% 1 2 3 4 5 6 7 8 100% 80% 75% 75% Pho4 AD / mCherry ratio GFP 60% GFP GFP 50% 50% 40% 20% 25% 25% C-term ADs 0% ADs Percent of Percent of cells Other ADs 0% 1 2 3 4 5 6 7 8 0% 1 2 F 25 50 75 100 125 10 10 Gcn4 hydrophobic mutants (cumulative distribution) GFP bin number 1 AD length Activation J 1.0 0 0.8 0.6 0.4 −1 (log2 change vs wild-type) 0.2 Frequency of aTF expression 5 10 15 TFs without ADs falling within sorted mCh range Hydrophobic content 0.0 TFs with ADs Cumulative fraction of TFs (kcal/mol) 0.0 0.2 0.4 0.6 0.8 1.0 Fraction of genes regulated by TF that are activating Figure S1.
    [Show full text]
  • Accumulation of Rare Coding Variants in Genes Implicated in Risk of Human Cleft Lip with Or Without Cleft Palate
    HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Am J Med Manuscript Author Genet A. Author Manuscript Author manuscript; available in PMC 2020 July 01. Published in final edited form as: Am J Med Genet A. 2019 July ; 179(7): 1260–1269. doi:10.1002/ajmg.a.61183. Accumulation of Rare Coding Variants in Genes Implicated in Risk of Human Cleft Lip with or without Cleft Palate Nicholas J. Marini1,*, Kripa Asrani1, Wei Yang2, Jasper Rine1, Gary M Shaw2 1California Institute for Quantitative Biosciences, Department of Molecular and Cellular Biology, University of California, Berkeley, CA 94720, USA 2Department of Pediatrics, Stanford University School of Medicine, Stanford, CA 94305, USA Abstract Cleft lip with/without cleft palate (CLP) is a common craniofacial malformation with complex etiologies, reflecting both genetic and environmental factors. Most of the suspected genetic risk for CLP has yet to be identified. To further classify risk loci and estimate the contribution of rare variants, we sequenced the exons in 49 candidate genes in 323 CLP cases and 211 non-malformed controls. Our findings indicated that rare, protein-altering variants displayed markedly higher burdens in CLP cases at relevant loci. First, putative loss-of-function mutations (nonsense, frameshift) were significantly enriched among cases: 13 of 323 cases (~4%) harbored such alleles within these 49 genes, versus one such change in controls (p = 0.01). Second, in gene-level analyses, the burden of rare alleles showed greater case-association for several genes previously implicated in cleft risk. For example, BHMT displayed a 10-fold increase in protein-altering variants in CLP cases (p = 0.03), including multiple case occurrences of a rare frameshift mutation (K400fs).
    [Show full text]
  • Chromatin Dynamics in The
    TRANSCRIPTION AND CHROMATIN DYNAMICS IN THE NOTCH SIGNALLING RESPONSE Zoe Pillidge Churchill College Department of Physiology, Development and Neuroscience University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy September 2018 TRANSCRIPTION AND CHROMATIN DYNAMICS IN THE NOTCH SIGNALLING RESPONSE During normal development, different genes are expressed in different cell types, often directed by cell signalling pathways and the pre-existing chromatin environment. The highly-conserved Notch signalling pathway is involved in many cell fate decisions during development, activating different target genes in different contexts. Upon ligand binding, the Notch receptor itself is cleaved, allowing the intracellular domain to travel to the nucleus and activate gene expression with the transcription factor known as Suppressor of Hairless (Su(H)) in Drosophila melanogaster. It is remarkable how, with such simplicity, the pathway can have such diverse outcomes while retaining precision, speed and robustness in the transcriptional response. The primary goal of this PhD has been to gain a better understanding of this process of rapid transcriptional activation in the context of the chromatin environment. To learn about the dynamics of the Notch transcriptional response, a live imaging approach was used in Drosophila Kc167 cells to visualise the transcription of a Notch-responsive gene in real time. With this technique, it was found that Notch receptor cleavage and trafficking can take place within 15 minutes to activate target gene expression, but that a ligand-receptor interaction between neighbouring cells may take longer. These experiments provide new data about the dynamics of the Notch response which could not be obtained with static time-point experiments.
    [Show full text]
  • 4-6 Weeks Old Female C57BL/6 Mice Obtained from Jackson Labs Were Used for Cell Isolation
    Methods Mice: 4-6 weeks old female C57BL/6 mice obtained from Jackson labs were used for cell isolation. Female Foxp3-IRES-GFP reporter mice (1), backcrossed to B6/C57 background for 10 generations, were used for the isolation of naïve CD4 and naïve CD8 cells for the RNAseq experiments. The mice were housed in pathogen-free animal facility in the La Jolla Institute for Allergy and Immunology and were used according to protocols approved by the Institutional Animal Care and use Committee. Preparation of cells: Subsets of thymocytes were isolated by cell sorting as previously described (2), after cell surface staining using CD4 (GK1.5), CD8 (53-6.7), CD3ε (145- 2C11), CD24 (M1/69) (all from Biolegend). DP cells: CD4+CD8 int/hi; CD4 SP cells: CD4CD3 hi, CD24 int/lo; CD8 SP cells: CD8 int/hi CD4 CD3 hi, CD24 int/lo (Fig S2). Peripheral subsets were isolated after pooling spleen and lymph nodes. T cells were enriched by negative isolation using Dynabeads (Dynabeads untouched mouse T cells, 11413D, Invitrogen). After surface staining for CD4 (GK1.5), CD8 (53-6.7), CD62L (MEL-14), CD25 (PC61) and CD44 (IM7), naïve CD4+CD62L hiCD25-CD44lo and naïve CD8+CD62L hiCD25-CD44lo were obtained by sorting (BD FACS Aria). Additionally, for the RNAseq experiments, CD4 and CD8 naïve cells were isolated by sorting T cells from the Foxp3- IRES-GFP mice: CD4+CD62LhiCD25–CD44lo GFP(FOXP3)– and CD8+CD62LhiCD25– CD44lo GFP(FOXP3)– (antibodies were from Biolegend). In some cases, naïve CD4 cells were cultured in vitro under Th1 or Th2 polarizing conditions (3, 4).
    [Show full text]
  • Intrinsic Specificity of DNA Binding and Function of Class II Bhlh
    INTRINSIC SPECIFICITY OF BINDING AND REGULATORY FUNCTION OF CLASS II BHLH TRANSCRIPTION FACTORS APPROVED BY SUPERVISORY COMMITTEE Jane E. Johnson Ph.D. Helmut Kramer Ph.D. Genevieve Konopka Ph.D. Raymond MacDonald Ph.D. INTRINSIC SPECIFICITY OF BINDING AND REGULATORY FUNCTION OF CLASS II BHLH TRANSCRIPTION FACTORS by BRADFORD HARRIS CASEY DISSERTATION Presented to the Faculty of the Graduate School of Biomedical Sciences The University of Texas Southwestern Medical Center at Dallas In Partial Fulfillment of the Requirements For the Degree of DOCTOR OF PHILOSOPHY The University of Texas Southwestern Medical Center at Dallas Dallas, Texas December, 2016 DEDICATION This work is dedicated to my family, who have taught me pursue truth in all forms. To my grandparents for inspiring my curiosity, my parents for teaching me the value of a life in the service of others, my sisters for reminding me of the importance of patience, and to Rachel, who is both “the beautiful one”, and “the smart one”, and insists that I am clever and beautiful, too. Copyright by Bradford Harris Casey, 2016 All Rights Reserved INTRINSIC SPECIFICITY OF BINDING AND REGULATORY FUNCTION OF CLASS II BHLH TRANSCRIPTION FACTORS Publication No. Bradford Harris Casey The University of Texas Southwestern Medical Center at Dallas, 2016 Jane E. Johnson, Ph.D. PREFACE Embryonic development begins with a single cell, and gives rise to the many diverse cells which comprise the complex structures of the adult animal. Distinct cell fates require precise regulation to develop and maintain their functional characteristics. Transcription factors provide a mechanism to select tissue-specific programs of gene expression from the shared genome.
    [Show full text]
  • An Activation-Specific Role for Transcription Factor TFIIB in Vivo (Sua7͞pho4͞pho5͞transcriptional Control)
    Proc. Natl. Acad. Sci. USA Vol. 96, pp. 2764–2769, March 1999 Biochemistry An activation-specific role for transcription factor TFIIB in vivo (SUA7yPho4yPHO5ytranscriptional control) WEI-HUA WU AND MICHAEL HAMPSEY* Department of Biochemistry, Division of Nucleic Acids Enzymology, University of Medicine and Dentistry of New Jersey-Robert Wood Johnson Medical School, 675 Hoes Lane, Piscataway, NJ 08854-5635 Edited by Robert G. Roeder, The Rockefeller University, New York, NY, and approved January 20, 1999 (received for review November 9, 1998) ABSTRACT A yeast mutant was isolated encoding a sin- a study of PIC formation and transcriptional activity demon- gle amino acid substitution [serine-53 3 proline (S53P)] in strated that PIC assembly occurs by at least two steps and that transcription factor TFIIB that impairs activation of the the TATA box and TFIIB also can affect transcription subse- PHO5 gene in response to phosphate starvation. This effect is quent to PIC assembly (11). Thus, processes other than factor activation-specific because S53P did not affect the uninduced recruitment are potential activator targets. level of PHO5 expression, yet is not specific to PHO5 because TFIIB is recruited to the PIC by the acidic activator VP16 Adr1-mediated activation of the ADH2 gene also was impaired and the proline-rich activator CTF1 (3, 12). VP16 directly by S53P. Pho4, the principal activator of PHO5, directly targets TFIIB (3) and this interaction is required for activation interacted with TFIIB in vitro, and this interaction was (13, 14). Interestingly, VP16 induces a conformational change impaired by the S53P replacement. Furthermore, Pho4 in- in TFIIB that disrupts the intramolecular interaction between duced a conformational change in TFIIB, detected by en- the N- and C-terminal domains in solution (15).
    [Show full text]
  • FOXE1-Dependent Regulation of Macrophage Chemotaxis by Thyroid Cells in Vitro and in Vivo
    International Journal of Molecular Sciences Article FOXE1-Dependent Regulation of Macrophage Chemotaxis by Thyroid Cells In Vitro and In Vivo Sara C. Credendino 1,2, Marta De Menna 1,3, Irene Cantone 1 , Carmen Moccia 1 , Matteo Esposito 1, Luigi Di Guida 1, Mario De Felice 1,2 and Gabriella De Vita 1,* 1 Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, 80131 Naples, Italy; [email protected] (S.C.C.); [email protected] (M.D.M.); [email protected] (I.C.); [email protected] (C.M.); [email protected] (M.E.); [email protected] (L.D.G.); [email protected] (M.D.F.) 2 Institute of Experimental Endocrinology and Oncology “G. Salvatore”, National Research Council (CNR), 80131 Naples, Italy 3 DBMR-Department for BioMedical Research, University of Bern, 3012 Bern, Switzerland * Correspondence: [email protected]; Tel.: +39-081-746-3240 Abstract: Forkhead box E1 (FOXE1) is a lineage-restricted transcription factor involved in thyroid cancer susceptibility. Cancer-associated polymorphisms map in regulatory regions, thus affecting the extent of gene expression. We have recently shown that genetic reduction of FOXE1 dosage modifies multiple thyroid cancer phenotypes. To identify relevant effectors playing roles in thyroid cancer development, here we analyse FOXE1-induced transcriptional alterations in thyroid cells that do not express endogenous FOXE1. Expression of FOXE1 elicits cell migration, while transcriptome analysis reveals that several immune cells-related categories are highly enriched in differentially Citation: Credendino, S.C.; expressed genes, including several upregulated chemokines involved in macrophage recruitment. De Menna, M.; Cantone, I.; Moccia, C.; Accordingly, FOXE1-expressing cells induce chemotaxis of co-cultured monocytes.
    [Show full text]
  • The HLH-6 Transcription Factor Regulates C
    The HLH-6 Transcription Factor Regulates C. elegans Pharyngeal Gland Development and Function Ryan B. Smit1, Ralf Schnabel2, Jeb Gaudet1,3* 1 Genes and Development Research Group, Department of Biochemistry and Molecular Biology, University of Calgary, Calgary, Alberta, Canada, 2 Institut fu¨r Genetik, Technische Universita¨t Braunschweig, Braunschweig, Germany, 3 Department of Medical Genetics, University of Calgary, Calgary, Alberta, Canada Abstract The Caenorhabditis elegans pharynx (or foregut) functions as a pump that draws in food (bacteria) from the environment. While the ‘‘organ identity factor’’ PHA-4 is critical for formation of the C. elegans pharynx as a whole, little is known about the specification of distinct cell types within the pharynx. Here, we use a combination of bioinformatics, molecular biology, and genetics to identify a helix-loop-helix transcription factor (HLH-6) as a critical regulator of pharyngeal gland development. HLH-6 is required for expression of a number of gland-specific genes, acting through a discrete cis-regulatory element named PGM1 (Pharyngeal Gland Motif 1). hlh-6 mutants exhibit a frequent loss of a subset of glands, while the remaining glands have impaired activity, indicating a role for hlh-6 in both gland development and function. Interestingly, hlh-6 mutants are also feeding defective, ascribing a biological function for the glands. Pharyngeal pumping in hlh-6 mutants is normal, but hlh-6 mutants lack expression of a class of mucin-related proteins that are normally secreted by pharyngeal glands and line the pharyngeal cuticle. An interesting possibility is that one function of pharyngeal glands is to secrete a pharyngeal lining that ensures efficient transport of food along the pharyngeal lumen.
    [Show full text]
  • Downloaded from Refseq Database ( Duct Development (After E7.5), in Which Both Ducts Re
    Roly et al. BMC Genomics (2020) 21:688 https://doi.org/10.1186/s12864-020-07106-8 RESEARCH ARTICLE Open Access Transcriptional landscape of the embryonic chicken Müllerian duct Zahida Yesmin Roly, Rasoul Godini, Martin A. Estermann, Andrew T. Major, Roger Pocock and Craig A. Smith* Abstract Background: Müllerian ducts are paired embryonic tubes that give rise to the female reproductive tract in vertebrates. Many disorders of female reproduction can be attributed to anomalies of Müllerian duct development. However, the molecular genetics of Müllerian duct formation is poorly understood and most disorders of duct development have unknown etiology. In this study, we describe for the first time the transcriptional landscape of the embryonic Müllerian duct, using the chicken embryo as a model system. RNA sequencing was conducted at 1 day intervals during duct formation to identify developmentally-regulated genes, validated by in situ hybridization. Results: This analysis detected hundreds of genes specifically up-regulated during duct morphogenesis. Gene ontology and pathway analysis revealed enrichment for developmental pathways associated with cell adhesion, cell migration and proliferation, ERK and WNT signaling, and, interestingly, axonal guidance. The latter included factors linked to neuronal cell migration or axonal outgrowth, such as Ephrin B2, netrin receptor, SLIT1 and class A semaphorins. A number of transcriptional modules were identified that centred around key hub genes specifying matrix-associated signaling factors; SPOCK1, HTRA3 and ADGRD1. Several novel regulators of the WNT and TFG-β signaling pathway were identified in Müllerian ducts, including APCDD1 and DKK1, BMP3 and TGFBI.A number of novel transcription factors were also identified, including OSR1, FOXE1, PRICKLE1, TSHZ3 and SMARCA2.
    [Show full text]
  • Potential Targets of FOXL2, a Transcription Factor Involved in Craniofacial and Follicular Development, Identified by Transcriptomics
    Potential targets of FOXL2, a transcription factor involved in craniofacial and follicular development, identified by transcriptomics Frank Batista*, Daniel Vaiman*†, Jean Dausset‡§, Marc Fellous*¶, and Reiner A. Veitia*¶ʈ *De´partement de Ge´ne´ tique et De´veloppement, Institut Cochin, Institut National de la Sante´et de la Recherche Me´dicale U567, Centre National de la Recherche Scientifique Unite´Mixte de Recherche 8104, and Faculte´deMe´ decine Rene´Descartes, Universite´Paris V UM 3, 75014 Paris, France; †De´partement de Ge´ne´ tique Animale, Institut National de la Recherche Agronomique, 75338 Paris Cedex 07, France; ‡Fondation Jean Dausset, Centre d’Etude du Polymorphisme Humain, 75010 Paris, France; and ¶Universite´Denis Diderot/Paris VII, 75005 Paris, France Contributed by Jean Dausset, December 21, 2006 (sent for review November 16, 2006) FOXL2 is a gene encoding a forkhead transcription factor, polyAla has recently been reported in a nonsyndromic (i.e., not whose mutations are responsible for the blepharophimosis-ptosis- BPES-related) case of premature ovarian failure (9). epicanthus inversus syndrome that often involves premature ovar- In humans, FOXL2 is one of the earliest known markers of ian failure. FOXL2 is one of the earliest ovarian markers and it ovarian differentiation (3). Thus, it may play a role at an early stage offers, along with its targets, an excellent model to study ovarian of development of the ovarian somatic compartment. Because development and function in normal and pathological conditions. FOXL2 is still strongly expressed in postnatal and adult follicular We have recently shown that the aromatase gene is a target of cells, it may also play a role throughout female fertile life in FOXL2, and only three other targets have been reported so far.
    [Show full text]
  • Highlighting the Potential Utility of MBP Crystallization Chaperone For
    www.nature.com/scientificreports OPEN Highlighting the potential utility of MBP crystallization chaperone for Arabidopsis BIL1/BZR1 transcription factor‑DNA complex Shohei Nosaki1, Tohru Terada2, Akira Nakamura1, Kei Hirabayashi1, Yuqun Xu1, Thi Bao Chau Bui1, Takeshi Nakano3,4, Masaru Tanokura1* & Takuya Miyakawa1* The maltose‑binding protein (MBP) fusion tag is one of the most commonly utilized crystallization chaperones for proteins of interest. Recently, this MBP‑mediated crystallization technique was adapted to Arabidopsis thaliana (At) BRZ‑INSENSITIVE‑LONG (BIL1)/BRASSINAZOLE‑RESISTANT (BZR1), a member of the plant‑specifc BZR TFs, and revealed the frst structure of AtBIL1/BZR1 in complex with target DNA. However, it is unclear how the fused MBP afects the structural features of the AtBIL1/BZR1‑DNA complex. In the present study, we highlight the potential utility of the MBP crystallization chaperone by comparing it with the crystallization of unfused AtBIL1/BZR1 in complex with DNA. Furthermore, we assessed the validity of the MBP‑fused AtBIL1/BZR1‑DNA structure by performing detailed dissection of crystal packings and molecular dynamics (MD) simulations with the removal of the MBP chaperone. Our MD simulations defne the structural basis underlying the AtBIL1/ BZR1‑DNA assembly and DNA binding specifcity by AtBIL1/BZR1. The methodology employed in this study, the combination of MBP‑mediated crystallization and MD simulation, demonstrates promising capabilities in deciphering the protein‑DNA recognition code. Maltose-binding protein (MBP) is the most useful and successful crystallization chaperone for challeng- ing proteins1–4, as MBP maintains the solubility of fusion proteins and is used as an afnity tag for protein purifcation5–8.
    [Show full text]
  • Motif Co-Regulation and Co-Operativity Are Common Mechanisms in Transcriptional, Post-Transcriptional and Post-Translational Regulation Kim Van Roey1,2 and Norman E
    Van Roey and Davey Cell Communication and Signaling (2015) 13:45 DOI 10.1186/s12964-015-0123-9 REVIEW Open Access Motif co-regulation and co-operativity are common mechanisms in transcriptional, post-transcriptional and post-translational regulation Kim Van Roey1,2 and Norman E. Davey3* Abstract A substantial portion of the regulatory interactions in the higher eukaryotic cell are mediated by simple sequence motifs in the regulatory segments of genes and (pre-)mRNAs, and in the intrinsically disordered regions of proteins. Although these regulatory modules are physicochemically distinct, they share an evolutionary plasticity that has facilitated a rapid growth of their use and resulted in their ubiquity in complex organisms. The ease of motif acquisition simplifies access to basal housekeeping functions, facilitates the co-regulation of multiple biomolecules allowing them to respond in a coordinated manner to changes in the cell state, and supports the integration of multiple signals for combinatorial decision-making. Consequently, motifs are indispensable for temporal, spatial, conditional and basal regulation at the transcriptional, post-transcriptional and post-translational level. In this review, we highlight that many of the key regulatory pathways of the cell are recruited by motifs and that the ease of motif acquisition has resulted in large networks of co-regulated biomolecules. We discuss how co-operativity allows simple static motifs to perform the conditional regulation that underlies decision-making in higher eukaryotic biological systems. We observe that each gene and its products have a unique set of DNA, RNA or protein motifs that encode a regulatory program to define the logical circuitry that guides the life cycle of these biomolecules, from transcription to degradation.
    [Show full text]