Bases of motifs for generating repeated patterns with wild cards

Motif inference represents one of the most important areas of research in computational biology, and one of its oldest ones. Despite this, the problem remains very much open in the sense that no existing definition is fully satisfying, either in formal terms, or in relation to the biological questions that involve finding such motifs. Two main types of motifs have been considered in the literature: matrices (of letter frequency per position in the motif) and patterns. There is no conclusive evidence in favor of either, and recent work has attempted to integrate the two types into a single model. In this paper, we address the formal issue in relation to motifs as patterns. This is essential to get at a better understanding of motifs in general. In particular, we consider a promising idea that was recently proposed, which attempted to avoid the combinatorial explosion in the number of motifs by means of a generator set for the motifs. Instead of exhibiting a complete list of motifs satisfying some input constraints, what is produced is a basis of such motifs from which all the other ones can be generated. We study the computational cost of determining such a basis of repeated motifs with wild cards in a sequence. We give new upper and lower bounds on such a cost, introducing a notion of basis that is provably contained in (and, thus, smaller) than previously defined ones. Our basis can be computed in less time and space, and is still able to generate the same set of motifs. We also prove that the number of motifs in all bases defined so far grows exponentially with the quorum, that is, with the minimal number of times a motif must appear in a sequence, something unnoticed in previous work. We show that there is no hope to efficiently compute such bases unless the quorum is fixed

Pattern Masking for Dictionary Matching:Theory and Practice

Data masking is a common technique for sanitizing sensitive data maintained in database systems which is becoming increasingly important in various application areas, such as in record linkage of personal data. This work formalizes the Pattern Masking for Dictionary Matching (PMDM) problem: given a dictionary D of d strings, each of length ℓ, a query string q of length ℓ, and a positive integer z, we are asked to compute a smallest set K⊆{1, …, ℓ}, so that if q[i] is replaced by a wildcard for all i∈K, then q matches at least z strings from D. Solving PMDM allows providing data utility guarantees as opposed to existing approaches. We first show, through a reduction from the well-known k-Clique problem, that a decision version of the PMDM problem is NP-complete, even for binary strings. We thus approach the problem from a more practical perspective. We show a combinatorial O((dℓ)|K|/3+dℓ)-time and O(dℓ)-space algorithm for PMDM for |K|=O(1). In fact, we show that we cannot hope for a faster combinatorial algorithm, unless the combinatorial k-Clique hypothesis fails (Abboud et al. in SIAM J Comput 47:2527–2555, 2018; Lincoln et al., in: 29th ACM-SIAM Symposium on Discrete Algorithms (SODA), 2018). Our combinatorial algorithm, executed with small |K|, is the backbone of a greedy heuristic that we propose. Our experiments on real-world and synthetic datasets show that our heuristic finds nearly-optimal solutions in practice and is also very efficient. We also generalize this algorithm for the problem of masking multiple query strings simultaneously so that every string has at least z matches in D. PMDM can be viewed as a generalization of the decision version of the dictionary matching with mismatches problem: by querying a PMDM data structure with string q and z=1, one obtains the minimal number of mismatches of q with any string from D. The query time or space of all known data structures for the more restricted problem of dictionary matching with at most k mismatches incurs some exponential factor with respect to k. A simple exact algorithm for PMDM runs in time O(2ℓd). We present a data structure for PMDM that answers queries over D in time O(2ℓ/2(2ℓ/2+τ)ℓ) and requires space O(2ℓd2/τ2+2ℓ/2d), for any parameter τ∈[1, d]. We complement our results by showing a two-way polynomial-time reduction between PMDM and the Minimum Union problem [Chlamtáč et al., ACM-SIAM Symposium on Discrete Algorithms (SODA) 2017]. This gives a polynomial-time O(d1/4+ϵ)-approximation algorithm for PMDM, which is tight under a plausible complexity conjecture. This is an extended version of a paper that was presented at International Symposium on Algorithms and Computation (ISAAC) 2021

A search for small noncoding RNAs in Staphylococcus aureus reveals a conserved sequence motif for regulation

Bioinformatic analysis of the intergenic regions of Staphylococcus aureus predicted multiple regulatory regions. From this analysis, we characterized 11 novel noncoding RNAs (RsaA‐K) that are expressed in several S. aureus strains under different experimental conditions. Many of them accumulate in the late-exponential phase of growth. All ncRNAs are stable and their expression is Hfq-independent. The transcription of several of them is regulated by the alternative sigma B factor (RsaA, D and F) while the expression of RsaE is agrA-dependent. Six of these ncRNAs are specific to S. aureus, four are conserved in other Staphylococci, and RsaE is also present in Bacillaceae. Transcriptomic and proteomic analysis indicated that RsaE regulates the synthesis of proteins involved in various metabolic pathways. Phylogenetic analysis combined with RNA structure probing, searches for RsaE‐mRNA base pairing, and toeprinting assays indicate that a conserved and unpaired UCCC sequence motif of RsaE binds to target mRNAs and prevents the formation of the ribosomal initiation complex. This study unexpectedly shows that most of the novel ncRNAs carry the conserved C−rich motif, suggesting that they are members of a class of ncRNAs that target mRNAs by a shared mechanism

LOCAL AND GLOBAL GENE REGULATION ANALYSIS OF THE AUTOINDUCER-2 MEDIATED QUORUM SENSING MECHANISM IN ESCHERICHIA COLI

The term `quorum sensing' (QS) is used to define a population density based communication mechanism which uses chemical signal molecules called autoinducers to trigger unique and varied changes in gene expression. Although several communication methods have been identified in bacteria that are unique to a particular species, one type of signal molecule, autoinducer-2 (AI-2) is linked to interspecies communication, indicating its potential as a universal signal for cueing a QS response among multiple bacterial types. In E. coli, AI-2 acts as an effector by binding to the QS repressor LsrR. As a result, LsrR unbinds and relieves repression of the lsr regulon, stimulating a subsequent QS gene expression cascade. In this dissertation, LsrR structure and in vitro binding activity are examined. Genomic binding and DNA microarray analyses are conducted and three novel sites putatively regulated by LsrR, yegE-udk, mppA and yihF, are revealed. Two cAMP receptor protein (CRP) binding locations in intergenic region of the lsr regulon are also confirmed. The role of each CRP site in divergent expression is qualified, indicating the lsr intergenic region to be a class III CRP-dependent promoter. Also, four specific DNA binding sites for LsrR in the lsr intergenic region are proposed, and reliance upon simultaneous binding to these various sites and the resulting effects on LsrR repression is presented. Finally, a complex model for regulation of the lsr regulon is depicted incorporating LsrR, CRP, DNA looping, and a predicted secondary layer of repression by an integration host factor (IHF)-like protein. Further understanding of this QS genetic mechanism may potentially be used for inhibiting bacterial proliferation and infection, modifying the natural genetic system to elicit alternate desired responses, or extracted and applied to a highly customizable and sensitive in vitro biosensor

Doctor of Philosophy

dissertationThere are many bacteria that associate with insects in a mutualistic manner and offer their hosts distinct fitness advantages, and thus have likely played an important role in shaping the ecology and evolution of insects. Therefore, there is much interest in understanding how these relationships are initiated and maintained and the molecular mechanisms involved in this process, as well as interest in developing symbionts as platforms for paratransgenesis to combat disease transmission by insect hosts. However, this research has been hampered by having only a limited number of systems to work with, due to the difficulties in isolating and modifying bacterial symbionts in the lab. In this dissertation, I present my work in developing a recently described insect-bacterial symbiosis, that of the louse fly, Pseudolynchia canariensis, and its bacterial symbiont, Candidatus Arsenophonus arthropodicus, into a new model system with which to investigate the mechanisms and evolution of symbiosis. This included generating and analyzing the complete genome sequence of Ca. A. arthropodicus, which provided some evidence that Ca. A. arthropodicus has become recently associated with insects and may have evolved from an ancestor that was an insect pathogen. Additionally, I describe the development of methods for genetic modification of this bacterial symbiont and for introducing recombinant symbionts into louse fly hosts, as well as a new microinjection technique that enables the complete replacement of native symbionts with recombinant symbionts. With the generation of the symbiont genome sequence along with strategies for engineering recombinant symbionts and establishing them in an insect host, this work provides an interesting new system with which to investigate the function of specific genes in symbiosis as well as a promising new avenue of research involving paratransgenesis

Genome-wide characterization of the Complex Trancriptome Architecture of S.cerevisiae with tiling arrays

Recent genome-wide transcriptome analysis in humans, Drosophila, Arabidopsis and yeast challenged the old notion of fundamental aspects of gene regulation, providing evidence that protein-encoding genes are not the only agents controlling cellular processes. Non-coding RNAs comprising untranslated regions of protein coding genes, antisense transcripts of annotated genes, micro RNAs and small interfering RNAs present another tier in gene regulation, enabling integration and networking of complex suites of gene activity. Sophisticated RNA signaling networks operate in higher eukaryotes, enabling gene to gene communication and regulation of chromatin structure, DNA methylation, transcription, translation, RNA silencing and stability, and coordinate multiple tasks of the whole cellular system. Fundamental mechanisms and structure of such control architecture remained largely obscure due to limitations of available approaches, such as noise in the data, strand–unspecific transcription analysis and difficulties in functional follow-up opportunities in higher eukaryotes. To address the complexity of transcriptome architecture we undertook the genome-wide transcriptome study in a simpler genome of S.cerevisiae with the help of a new tiling array. We have shown that 85% of the genome is expressed in rich media. Apart from expected transcripts, we found operon-like transcripts, transcripts from neighboring genes not separated by intergenic regions, and genes with complex transcriptional architecture where different parts of the same gene are expressed at different levels. We mapped the positions of 3' and 5' UTRs of coding genes and identified hundreds of RNA transcripts distinct from annotated genes. These non-annotated transcripts, on average, have lower sequence conservation and lower rates of deletion phenotype than protein coding genes. Many other transcripts overlap known genes in antisense orientation, and for these pairs global correlations were discovered: UTR lengths correlated with gene function, localization, and requirements for regulation; antisense transcripts overlapped 3' UTRs more than 5' UTRs; UTRs with overlapping antisense tended to be longer; and the presence of antisense associated with gene function. Overall our study revealed complexity of yeast transcriptional architecture calling for additional annotation of the genome and putting forward an important role for RNA-mediated regulation. An attractive model for the study of the genome-wide RNA-mediated regulation of gene activity in yeast is mitotic cell cycle, which has been extensively studied over past decade and is therefore a well characterized system. Mitosis is associated with important physiological changes in the cell and diverse biological events depend on this periodicity. To ensure the proper functioning of the mechanisms that maintain order during cell division about 800 genes of diverse GO categories are coordinately regulated in a periodic manner coincident with the cell cycle. This includes genes involved in DNA replication, budding, glycosylation, nuclear division, control of mRNA transcription, responsiveness to external stimuli and subcellular localization of proteins. Several genome-wide studies have been done to catalogue cell cycle-regulated genes with the help of early expression arrays. Given the high resolution of our technique, profiling genome-wide periodic expression with the tiling arrays allowed taking a step forward to prove the existence of RNA-mediated regulation of transcription. Using two methods of synchronization, I have monitored cell-cycle dependent transcription for more than 3 complete cell cycles. I have identified about ~600 periodic ORFs. In consent with previous studies on transcriptional regulation during specific mitotic phases, I have shown prevalence of periodic expression of annotated genes in three distinct periods of cell cycle progression: late G1/S transition, G2/M transition and exit of M phase of mitosis. Moreover, I have shown antisense transcription throughout the cell cycle phases. Out of ~260 antisense transcripts that we discovered, 37 display periodic patterns; half of them are expressed coincidentally with peak expression intensity of cell cycle-regulated ORFs, whereas the other half peaks at the periods of relaxation of the transcriptional machinery, which drives phase transition. Cycling antisense has been registered opposite several important cell cycle regulators. Additionally, periodic novel isolated transcripts were detected in the dataset, which may represent non-annotated ncRNAs involved in regulation of mitosis or regulated by cell cycle controlling genes

Expanding the repertoire of bacterial (non-)coding RNAs

The detection of non-protein-coding RNA (ncRNA) genes in bacteria and their diverse regulatory mode of action moved the experimental and bio-computational analysis of ncRNAs into the focus of attention. Regulatory ncRNA transcripts are not translated to proteins but function directly on the RNA level. These typically small RNAs have been found to be involved in diverse processes such as (post-)transcriptional regulation and modification, translation, protein translocation, protein degradation and sequestration. Bacterial ncRNAs either arise from independent primary transcripts or their mature sequence is generated via processing from a precursor. Besides these autonomous transcripts, RNA regulators (e.g. riboswitches and RNA thermometers) also form chimera with protein-coding sequences. These structured regulatory elements are encoded within the messenger RNA and directly regulate the expression of their “host” gene. The quality and completeness of genome annotation is essential for all subsequent analyses. In contrast to protein-coding genes ncRNAs lack clear statistical signals on the sequence level. Thus, sophisticated tools have been developed to automatically identify ncRNA genes. Unfortunately, these tools are not part of generic genome annotation pipelines and therefore computational searches for known ncRNA genes are the starting point of each study. Moreover, prokaryotic genome annotation lacks essential features of protein-coding genes. Many known ncRNAs regulate translation via base-pairing to the 5’ UTR (untranslated region) of mRNA transcripts. Eukaryotic 5’ UTRs have been routinely annotated by sequencing of ESTs (expressed sequence tags) for more than a decade. Only recently, experimental setups have been developed to systematically identify these elements on a genome-wide scale in prokaryotes. The first part of this thesis, describes three experimental surveys of exploratory field studies to analyze transcript organization in pathogenic bacteria. To identify ncRNAs in Pseudomonas aeruginosa we used a combination of an experimental RNomics approach and ncRNA prediction. Besides already known ncRNAs we identified and validated the expression of six novel RNA genes. Global detection of transcripts by next generation RNA sequencing techniques unraveled an unexpectedly complex transcript organization in many bacteria. These ultra high-throughput methods give us the appealing opportunity to analyze the complete RNA output of any species at once. The development of the differential RNA sequencing (dRNA-seq) approach enabled us to analyze the primary transcriptome of Helicobacter pylori and Xanthomonas campestris. For the first time we generated a comprehensive and precise transcription start site (TSS) map for both species and provide a general framework for the analysis of dRNA-seq data. Focusing on computer-aided analysis we developed new tools to annotate TSS, detect small protein-coding genes and to infer homology of newly detected transcripts. We discovered hundreds of TSS in intergenic regions, upstream of protein-coding genes, within operons and antisense to annotated genes. Analysis of 5’ UTRs (spanning from the TSS to the start codon of the adjacent protein-coding gene) revealed an unexpected size diversity ranging from zero to several hundred nucleotides. We identified and validated the expression of about 60 and about 20 ncRNA candidates in Helicobacter and Xanthomonas, respectively. Among these ncRNA candidates we found several small protein-coding genes that have previously evaded annotation in both species. We showed that the combination of dRNA-seq and computational analysis is a powerful method to examine prokaryotic transcriptomes. Experimental setups are time consuming and often combined with huge costs. Another limitation of experimental approaches is that genes which are expressed in specific developmental stages or stress conditions are likely to be missed. Bioinformatic tools build an alternative to overcome such restraints. General approaches usually depend on comparative genomic data and evolutionary signatures are used to analyze the (non-)coding potential of multiple sequence alignments. In the second part of my thesis we present our major update of the widely used ncRNA gene finder RNAz and introduce RNAcode, an efficient tool to asses local protein-coding potential of genomic regions. RNAz has been successfully used to identify structured RNA elements in all domains of life. However, our own experience and the user feedback not only demonstrated the applicability of the RNAz approach, but also helped us to identify limitations of the current implementation. Using a much larger training set and a new classification model we significantly improved the prediction accuracy of RNAz. During transcriptome analysis we repeatedly identified small protein-coding genes that have not been annotated so far. Only a few of those genes are known to date and standard proteincoding gene finding tools suffer from the lack of training data. To avoid an excess of false positive predictions, gene finding software is usually run with an arbitrary cutoff of 40-50 amino acids and therefore misses the small sized protein-coding genes. We have implemented RNAcode which is optimized for emerging applications not covered by standard protein-coding gene annotation software. In addition to complementing classical protein gene annotation, a major field of application of RNAcode is the functional classification of transcribed regions. RNA sequencing analyses are likely to falsely report transcript fragments (e.g. mRNA degradation products) as non-coding. Hence, an evaluation of the protein-coding potential of these fragments is an essential task. RNAcode reports local regions of high coding potential instead of complete protein-coding genes. A training on known protein-coding sequences is not necessary and RNAcode can therefore be applied to any species. We showed this with our analysis of the Escherichia coli genome where the current annotation could be accurately reproduced. We furthermore identified novel small protein-coding genes with RNAcode in this extensively studied genome. Using transcriptome and proteome data we found compelling evidence that several of the identified candidates are bona fide proteins. In summary, this thesis clearly demonstrates that bioinformatic methods are mandatory to analyze the huge amount of transcriptome data and to identify novel (non-)coding RNA genes. With the major update of RNAz and the implementation of RNAcode we contributed to complete the repertoire of gene finding software which will help to unearth hidden treasures of the RNA World

The sporulation-specific small regulatory RNAs of Bacillus subtilis

Constantly changing environments in nature have led to bacteria evolving regulatory strategies that result in differential gene expression. A novel and understudied aspect of these networks are regulatory RNAs. The Gram-positive model organism Bacillus subtilis not only modulates gene expression to survive a variety of stresses, but also can form endospores to ensure its survival. Sporulation is an essential survival mechanism for many species, allowing them to enter a state of dormancy with resistance to various harsh conditions. This, in turn, ensures survival of not only the population, but also the species. The process of sporulation requires the controlled expression of approximately a quarter of the genes encoded by B. subtilis. Previous large-scale studies have identified that many transcripts do not encode proteins, but exhibited expression profiles similar to genes already known to be part of the sporulation network. Many of these transcripts were selected to likely function as small regulatory RNAs (sRNAs). This study has shown that many putative sRNAs are active during sporulation, three of which show specific phenotypes that alter germination capabilities in the presence of specific germinants. Cells lacking the necessary components to reverse this process are at a strong disadvantage. Detection of favorable growth conditions is key, but how is this conveyed during metabolic inactivity? Initial selection of putative sRNAs was done by in silico characterization. Prediction of transcriptional control and regulatory regions combined with tiling array profiling was used to select putative sRNAs for confirmation in vivo. Transcriptional fusion constructs were generated to confirm compartmental specific expression during sporulation. Spore specific sRNAs were further characterized with phenotypic studies, which suggested a role in endospore formation. This study explored some of the global analysis methods to identify sRNA targets. Whilst no targets for the four chosen sRNAs could be identified, this study produced the most comprehensive data set of proteins to be identified from a B. subtilis endospore

차세대 시퀀싱 기반 다중 오믹스 분석을 이용한 방선균 유전체의 분석 및 이차대사 생산의 이해

학위논문 (박사)-- 서울대학교 대학원 : 화학생물공학부, 2015. 8. 김병기.In this thesis, applications using next-generation sequencing (NGS) technology were employed to obtain genome-wide data, elucidating diverse cellular events of Streptomyces genome. First, comparative genomic analysis using 17 completely sequenced genome of Streptomyces revealed that 2018 gene families constitute core genome of this genus, including 15 ortholog clusters of sigma factors, 22 ortholog clusters involved in cell division category and secondary metabolite genes related to stress protection. Next, genome-wide binding of NdgR, a common transcriptional regulator involved in the biosynthesis of amino acids in S. coelicolor, was discovered by using chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP- seq). The study showed that NdgR binds 19 genomic loci including upstream regions of most genes involved in branched-chain and sulfur-containing amino acids biosynthesis. For this experiment, tandem epitope tagging systems for Streptomyces genome engineering was developed, which can be applied to other transcription factors in Streptomyces. Further study revealed that NdgR maintains homeostasis of sulfur assimilation under thiol oxidative stress conditions. In addition, genome architecture and dynamic expressions of mRNA and protein were uncovered by using multiple NGS tools, including TSS-seq, RNA-seq and ribosome profiling. Total 3926 transcription start sites were identified, indicating the length of 5 untranslated region of mRNA. This revealed that abundant existence of leaderless genes (~20%) and many of them were involved in transcription category. In particular, dynamic change of RNA and ribosome protected mRNA fragment (RPF) level showed disparity between transcription and translation, indicating the existence of translational control. With the integration of multiple NGS data, the single-based resolution map of genome architecture and expression profiles of each secondary metabolite clusters were examined, which provides valuable information for manipulating secondary metabolite production. The enormous data generated in this thesis and methodologies can be applied to engineering of genetic circuits for the antibiotics synthesis in S. coelicolor.Abstract i LIST OF TABLES vii LIST OF FIGURES viii 1 Introduction 1 1.1 Genomic basis for secondary metabolite biosynthesis in Streptomyces 2 1.1.1 Genome sequencing of Streptomyces 2 1.1.2 Toward a systems level understanding of Streptomyces biology 6 1.2 Next-generation sequencing technology 10 1.2.1 Emergence of next-generation sequencing 10 1.2.2 Next-generationsequencingmethods 12 1.3 Applications of Next-generation sequencing technologies used in this thesis 19 1.3.1 Chromatin immunoprecipitation coupled with high-throughput sequencing (ChIP-seq) 19 1.3.2 Strand-specific RNA sequencing (ssRNA-seq) 23 1.3.3 DifferentialRNAsequencing(dRNA-seq) 24 1.4 The scope of thesis 28 2 Materials and methods 30 2.1 Bacterial strains and culture conditions 31 2.2 DNA manipulations 32 2.2.1 Construction of template plasmids for tandem myc tagging 32 2.2.2 Tandem epitope tagging to Streptomyces coelicolor transcription factors 32 2.3 Chromatin immunoprecipitation 33 2.4 RNA extraction 35 2.5 Directional RNA sequencing 35 2.6 Strand-specific RNA sequencing 38 2.7 NGS sequencing 40 2.8 Western blot analysis 40 2.9 Bioinformatic analysis 40 2.9.1 Pan-genome analysis 40 2.9.2 ChIP-seq data analysis 41 2.9.3 TSS identification and data analysis 41 2.9.4 RNA sequencing and ribosome profiling data processing 42 3 Comparative genomics reveals the core and accessary genome of Streptomyces species 45 3.1 Pan-genome of 17 Streptomyces 46 3.2 Functional distribution of ortholog clusters 50 3.3 Core genome of 17 Streptomyces genome 53 3.4 Conclusion 61 4 Genome-wide analysis of transcriptional regulatory network of NdgR in Streptomyces coelicolor using ChIP-seq 62 4.1 Construction of PCR-based tandem epitope tagging system for Streptomyces genome 63 4.2 Verification of tagging system using chromatin immunoprecipitation 65 4.3 Identification of in vivo NdgR binding regions by ChIP-seq 70 4.4 Sequence analysis of NdgR binding region 76 4.5 Functional classification of the NdgR regulon 77 4.6 Role of NdgR under thiol oxidative stress 81 4.7 Elucidation of NdgR regulatory logic 85 4.8 Conclusions 88 5 Transcriptional and translational landscape of Streptomyces coelicolor genome 90 5.1 Integration of genome-wide data generated by Next-generation sequencing technology 91 5.2 High-resolution map of genetic organizational elements 98 5.3 Discrepancy in mRNA and protein expression 107 5.4 Genomic landscape of secondary metabolite genes in S. coelicolor 111 5.5 Conclusion 118 6 Conclusion & Further Suggestions 118 Conclusion & Further Suggestions 119 REFERENCES 122 APPENDIX 141 Appendix I The list of leaderless genes in S. coelicolor 142 Appendix II Transcriptional start sites in the secondary metabolite gene clusters of S. coelicolor 151 Appendix III RNA and RPF abundance of secondary metabolite genes in S. coelicolor 153 ABSTRACT IN KOREAN 159Docto