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Meiosis Genes Inventory in Alveolates and Other Protist Provide Evidence for Cryptic Sex and the Prevalence of a Synaptonemal Complex - Independent Crossover Pathway

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Meiosis Genes Inventory in Alveolates and Other Protist Provide Evidence for Cryptic Sex and the Prevalence of a Synaptonemal Complex - Independent Crossover Pathway Meiosis genes inventory in alveolates and other protist provide evidence for cryptic sex and the prevalence of a synaptonemal complex - independent crossover pathway Vo m Fachbereich Biologie der Technische Universität Kaiserslautern zur Erlangung des Akademische Grades "Doktor der Naturwissenschaften" genehmigte Dissertation vorgelegt von M.Sc. Jingyun Chi Prüfungs kommission : 1. Gutachter: Prof. Dr. Micah Dunthorn 2. Gutachter : Prof. Dr. Thorsten St oe ck Vorsitzender: Prof. Dr. Matthias Hahn Kaiserslautern, 8. Dezember 2020 D386 2 Declaration I, Jingyun Chi , hereby declare that this dissertation is the presentation of my original research work. Wherever contributions of others are involved, every effort is made to indicate this clearly, with due reference to the literature, and acknowledgement of collaborative resear ch and discussions. Furthermore, I declare that this present dissertation has not been submitted complete or in parts to any other institution or university with the intent to obtain an academic degree. Also, I submitted this present dissertation not prev iously to another governmental or scientific examination board. Kaiserslautern, 8. Dezember 2020 3 Content Content 4 Content Introduction ................................ ................................ ................................ ................................ ...... 6 Chapter I ................................ ................................ ................................ ................................ ........ 3 5 Summary ................................ ................................ ................................ ................................ 36 Meiosis Gene Inventoryof Four Ciliates Reveals the Prevalence of a Synaptonemal Complex - Independent Crossover Pathway ................................ ................................ ...... 40 Chapter II ................................ ................................ ................................ ................................ ....... 74 Summary ................................ ................................ ................................ ................................ 75 Cryptic sex in Symbiodinium (Alveolata, Dinoflagellata) is supported by an inventory of meiosis genes ................................ ................................ ................................ ................... 79 Chapter III ................................ ................................ ................................ ................................ ...... 95 Summary ................................ ................................ ................................ ................................ 96 Independent Reduction of Meiotic Crossover P athway I in the Alveolates ............... 102 Chapter IV ................................ ................................ ................................ ................................ .... 123 Summary ................................ ................................ ................................ .............................. 124 Meiotic Genes in Colpodean C iliates Support Secretive Sexuality ............................ 130 Chapter V ................................ ................................ ................................ ................................ ..... 148 Summary ................................ ................................ ................................ .............................. 149 Putatively asexual chrysophy tes have meiotic genes: evidence from transcriptomic data ................................ ................................ ................................ ................................ ....... 153 Conclusion and Outlook ................................ ................................ ................................ ............ 168 Summary ................................ ................................ ................................ ................................ ...... 170 Appendix ................................ ................................ ................................ ................................ ...... 172 Acknowledgment ................................ ................................ ................................ ........................ 201 Curriculum Vitae ................................ ................................ ................................ ......................... 203 5 Introduction Introduction 1.1 Bioinformatics 1.1.1 Definition and History Just as its name implies, Bioinformatics is an interdisciplinary of biology and computer sciences, about the collection, interpretation, annotation and storage of biological data ( Abdurakhmonov 2016 ; Bayat 2002 ) . Specifically, it uses DNA sequences as a source to predict the coding protein sequences, which, on the one hand is used to predict and simulate the secondary structure of protein molecules and then according to the putative function of the protein to design drug, on the other hand is used to analyze the evolutionary relationship between differ ent species by constructing phylogenetic tree ( Eise nhaber 2000 - 2013 ; Kono and Sarai 1999 ; Morozov, et al. 2005 ; Yu, et al. 2004 ) . With the development of NGS, it is also possible to sequence the transcriptom and whole genome sequence, which provide more reliable evidence for the analysis of molecular mech anism pathway and evolution ( Behjati and Tarpey 2013 ; Serratì, et al. 2016 ) . As a conclusion, bioinformatics is defined as the discipline of applying informatics technology to understand and organize the information associate with organisms and mo lecular processes ( Le Gallo, et al. 2017 ) . As a modern interdisciplinary, biology and medicine, mathematics and computer sciences are thre e components. Biologist and medical scientist think bioinformatics aims to clarify biological or medical sense, mathematicians think that algorithms and mathematical model is its core; while, computer scientists think database and software development are the bases of bioinformatics. Thus it can be seen, bioinformatics covers a wide range of fields and its function and value are fully demonstrated in the human genome project. 6 Introduction Bioinformatics e merged with the development of the substitutions matrix by D ayhoff and the Needleman - Wunsch algorithm ( Needleman and Wunsch 1970 ; Pertsemlidis and Fondon 2001 ) . In the 80s, the establishment of biological database opened the door for people who are interested in using the data to verify their hypothesis. The corporation of three international biological databases enables the data sharing around all biology scientists. In the meantime, to manage and analyze the all the data efficiently, program tools which were represented by BLAST and FASTA raises ( Altschul, et al. 1990 ; Lipman and Pearson 1985 ) . In this period, bioinformatics had established as an independent discipline. Since 90s, with the springing up of various software tools, genome sequencing and sequence analysis plays more and more important roles in bioinformatics. Genome project, especially human genome project, is producing thousand millions of data, which fueled a computational evolution in biology. Genomic data analysis is now the most developing field in bioinformatics ( Gauthier 2018 ; Hagen 2000 ) Before the invention of next - generation sequencing, Sanger Sequencing method (dideoxy method) which was invented b y Frederick Sanger and his collaborators in 1975 and Shotgun sequencing method were widely used ( Sanger and Coulson 1975 ) . The latter one was even app lied in the Human Genome project organized by Celera Genomics cooperation. The increasing demand of low - cost sequencing pushed the development of Next Generation sequencing (NGS) technology forward. Consist of Illumina (Solexa) sequencing, Roche 454 sequen cing, Ion torrent (Proton / PGM sequencing) and SOLiD sequencing, next generation sequencing revolutionized our research on the genomics and genetics biology and take our understanding of genome to a deeper level. 1.1.2 Data and Tool Since 20 century, especially the end of 20 century, the biological technology develops with full speed. The explosion of data enriches the 7 Introduction abundance of biological resources in a large extent and meanwhile, urges people to find an extremely strong tool to m anage and store them. In 1972, the first protein database was founded by Dayoff by initiating the collection of protein sequences in her book Atlas of protein sequence structure ( Dayhoff 1965 ) . At present, many Bioinformatic centers were established to collect, organize, manage and publish biological data as well as provide search and analysis tools to researche s. Current various bioinformatic databases have become bridges to connect all the labs and promote their research. Most nucleotide sequences are stored in the currently three largest bioinformatic databases, which are DDBJ from Japan, EMBL from Europe and GenBank from the USA. Since 1982, those three databases cooperate with each other by updating and exchanging data every day. Because of the convergent evolution, classification by morphological characters has its own limitations, e.g. organisms with a lo ng distance relationship might have similarity in some aspects ( Liu, et al. 2014 ) . Meanwhile, observation of morphological characters is difficult in organisms with small size and numerous quantities, such as all kinds of micr obial . Moreover, the common character between organisms is too scanty to be studied. Currently, the development of molecular biology
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