Les systèmes toxine-antitoxines (TA) sont abondants dans la majorité des génomes bactériens séquencés à ce jour. Ces systèmes codent une toxine stable qui inhibe soit la transcription, soit la traduction, et une antitoxine qui contrecarre l’effet de la toxine par formation d’un complexe avec celle-ci. L’antitoxine est instable suite à sa dégradation continue par les protéases ATP-dépendantes. Afin de maintenir un ratio antitoxine :toxine constant en condition normale de croissance, l’expression des systèmes TA est régulée négativement au niveau transcriptionnel par le complexe toxine-antitoxine.<p><p>Au début de notre travail, cinq systèmes TA étaient identifiés dans le chromosome d’E. coli. Il avait été montré par notre laboratoire que parmi ces systèmes, seul yefM-yoeB était activé en condition de surproduction de la protéase ATP-dépendante Lon. Ce résultat était surprenant puisque Lon était connue pour dégrader également l’antitoxine RelB du système chromosomique relBE. Un des objectifs de notre travail était de comprendre les mécanismes sous-jacents à cette spécificité. Nous avons montré que l’antitoxine YefM était dégradée à la fois par Lon et les protéases ClpAP et ClpXP. Nous avons également montré qu’en condition de surproduction de Lon, YefM était fortement instable (t1/2~ 10 min. vs 60 min en condition normale). Cette instabilité accrue permet donc l’activation du système yefM-yoeB, c’est-à-dire la libération de la toxine YoeB du complexe qu’elle forme avec YefM. Nous avons également avons montré que le t1/2 de RelB n’était pas affecté par la surproduction de Lon, ce qui explique pourquoi le système relBE n’est pas activé dans ces conditions. Notre hypothèse était qu’un cofacteur soit nécessaire à la dégradation de RelB par Lon et que celui-ci serait limitant dans nos conditions expérimentales. Le crible génétique que nous avons réalisé n’a cependant pas permis d’identifier de cofacteur de dégradation ni de régulateur transcriptionnel en trans du système relBE. <p><p>Un deuxième volet de notre travail de thèse a consisté en l’étude de la fonction des systèmes TA chromosomiques. L’hypothèse prévalente au début de notre travail était que les systèmes TA soient intégrés dans les voies adaptatives de réponses au stress. Cependant, le résultat de leur activation était controversé. L’hypothèse du groupe de Gerdes était que leur activation mène à un état bactériostatique réversible alors que le groupe d’Engelberg-Kulka montrait que le système mazEF était un système de mort programmée. Afin d’éclaircir le rôle des cinq systèmes TA dans la physiologie d’E. coli, nous avons testé l’effet de nombreux stress sur la croissance et la viabilité de souches sauvages et de souches délétées de ces systèmes. Aucune des conditions que nous avons testées n’a entraîné une diminution de la viabilité excluant de manière définitive l’hypothèse de la mort programmée. De plus, l’inhibition de croissance causée par ces différents stress s’est avérée être indépendante des cinq systèmes, de même que la phase de récupération suivant les différents stress. Enfin, nos expériences de compétition ont clairement démontré que les cinq systèmes ne procuraient aucun avantage sélectif aux bactéries dans des conditions de compétition en carence nutritive. Les systèmes TA étudiés dans ce travail ne jouent donc aucun rôle dans l’adaptation aux stress que nous avons testé puisqu’ils n’améliorent ni l’aptitude (fitness), ni la compétitivité des bactéries dans ces conditions. <p><p> / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Les éléments génétiques mobiles contribuent grandement à la diversité et à l’évolution des génomes bactériens par le biais du transfert horizontal. Parmi eux, les éléments intégratifs conjugatifs (ICE) codent leur propre excision, leur transfert par conjugaison et leur intégration. En revanche, les éléments intégratifs et mobilisables (IME) ne sont autonomes que pour leur excision et intégration et ne codent seulement que certaines des protéines/fonctions (oriT) dont ils ont besoin pour leur transfert conjugatif. Par conséquent, les IME ont besoin d’un élément conjugatif « helper » pour se transférer. Malgré leur impact sur le flux des gènes et l’évolution des génomes, la prévalence des ICE reste peu étudiée et seulement très peu d’IME avaient été identifiés au début de cette étude. De plus, bien que plusieurs méthodes de détection des ilots génomiques existent, aucune d’elles n’est dédiée aux ICE ou aux IME. Ce qui ne facilite pas l’analyse exhaustive de ces éléments. Le genre Streptococcus appartient au phylum des firmicutes. La quasi-totalité des streptocoques sont des bactéries commensales ou pathogènes de l’homme et d’autres animaux. Aussi, 2 espèces de streptocoques sont utilisées en tant que ferments lactiques lors la production de laits fermentés et divers fromages. Globalement, le genre streptocoques représente un groupe d’intérêt pour l’homme, l’étude du flux de gènes au sein de ces organismes et l’impact qu’il peut avoir sur leur mode vie est primordiale. Au cours de cette thèse, nous avons recherché les ICE et les IME dans 124 souches de streptocoques appartenant à 27 espèces en utilisant une base de données de référence comportant des protéines dites « signatures » d’IME et d’ICE (de leurs modules de conjugaison/mobilisation et d’integration/excision). Cette analyse exhaustive a permis l’identification et la délimitation de 131 ICE ou ICE légèrement dégénérés et 144 IME. Tous ces éléments ont été délimités, ce qui nous a permis de déterminer leur spécificité d’intégration dans les génomes. Au total, 17 spécificités d’intégration ont été identifiées pour les ICE dont 8 encore jamais décrites (ftsK, guaA, lysS, mutT, rpmG, rpsI, traG and ybaB/EbfC) et 18 spécificités pour les IME dont seulement 5 étaient connues chez les firmicutes. Les modules d’intégration des ICE codent soit une intégrase à tyrosine pouvant avoir une faible spécificité (1 famille d’intégrase) ou une forte spécificité (13 spécificités différentes), soit des intégrases à sérine seule ou en triplet (4 spécificités différentes), soit une transposase à DDE. Les IME codent soit des intégrases à tyrosine (10 spécificités différentes) soit des intégrases à serine seule (8 spécificités différentes). Les ICE ont été groupés en 7 familles distinctes selon les protéines codées par leur module de conjugaison. Les IME présentaient une très forte diversité au sein de leur module de mobilisation, empêchant ainsi leur regroupement en famille selon les gènes portés par ce module. Les analyses phylogénétiques des protéines signature codées par tous les ICE et les IME ont montré des échanges de modules d’intégration entre les ICE et les IME et de nombreux échanges entre les modules de mobilisation des IME. L’ensemble de ces résultats révèle la forte prévalence et l’extrême diversité des ICE et des IME au sein des génomes de streptocoques. Une meilleure connaissance et compréhension de ces éléments nous a incité à construire un outil informatique semi-automatisé de détection des ICE et des IME de Streptocoques ainsi que leurs sites d’insertion / Mobile genetic elements largely contribute to the evolution and diversity of bacterial genomes through horizontal gene transfer. Among them, the integrative and conjugative elements (ICEs) encode their own excision, conjugative transfer and integration. On the other hand, integrative mobilizable elements (IMEs) are autonomous for excision and integration but encode only some of the proteins needed for their conjugative transfer. IMEs therefore need a “helper” conjugative element to transfer. Despite their impact on gene flow and genome dynamics, the prevalence of ICEs remains largely underscored and very few IMEs were identified at the beginning of this study. Furthermore, although several in silico methods exist to detect genomic islands, none are dedicated to ICEs or IMEs, thus complicating exhaustive examination of these mobile elements. The Streptococcus genus belongs to the firmicutes’ phylum. Almost all streptococci are commensal bacteria or pathogenes to men and animals. Two species of Streptococcus are also used in the dairy industry as lactic ferments in order to produce fermented milk and different types of cheese. Studying the gene flux of the Steptococci genus and the impact it can have on the lifestyle of these organisms is essential, as it has a lot of interest for human health and activities. In this work, we searched for ICEs and IMEs in 124 strains of streptococci belonging to 27 species using a reference database of ICE and IME signature proteins (from their conjugation, mobilization and integration/excision modules). This exhaustive analysis led to the identification and delimitation of 131 ICEs or slightly decayed ICEs and 144 IMEs. All these elements were delimited, which allowed us to identify their integration specificities in the genomes. In total, 17 ICE integration specificities were identified. Among them, 8 had never been described before (ftsK, guaA, lysS, mutT, rpmG, rpsI, traG and ybaB/EbfC). 18 specificities were also identified for IMEs, among which only 5 were known for the firmicutes. ICEs encode high or low-specificity tyrosine integrases (13 different specificities), single serine intégrases (1 specificity), triplet of serine integrases (3 different specificities), or DDE transposases while IMEs encode either tyrosine integrases (10 different specificities) or single serine integrases (8 different specificities). ICE were grouped in 7 distinct families according to the proteins encoded by their conjugation module whereas the mobilization modules of IMEs were highly diverse, preventing them from grouping into families according to their mobilization modules. The phylogenetic analysis of the signature proteins encoded by all ICEs and IMEs showed integration module exchanges between ICEs and IMEs and several mobilization module exchanges between IMEs. The overall results reveal a strong prevalence and extreme diversity of these elements among Streptococci genomes. Better understanding and knowledge of ICEs and IMEs prompted us to build a semi-automated command-line tool to identify streptococcal ICEs and IMEs as well as to determine their insertion site

Promoter region is the key regulatory region, which enables the gene to be transcribed or repressed by anchoring RNA polymerase and other transcription factors, but it is difficult to determine experimentally. Hence an in silico identification of promoters is crucial in order to guide experimental work and to pin point the key region that controls the transcription initiation of a gene. Analysis of various genome sequences in the vicinity of experimentally identified transcription start sites (TSSs) in prokaryotic as well as eukaryotic genomes had earlier indicated that they have several structural features in common, such as lower stability, higher curvature and less bendability, when compared with their neighboring regions. In this thesis work, the variation observed for these DNA sequence dependent structural properties have been used to identify and delineate promoter regions from other genomic regions. Since the number of bacterial genomes being sequenced is increasing very rapidly, it is crucial to have procedures for rapid and reliable annotation of their functional elements such as promoter regions, which control the expression of each gene or each transcription unit of the genome. The thesis work addresses this requirement and presents step by step protocols followed to get a generic method for promoter prediction that can be applicable across organisms. The each paragraph below gives an overall idea about the thesis organization into chapters. An overview of prokaryotic transcriptional regulation, structural polymorphism adapted by DNA molecule and its impact on transcriptional regulation has been discussed in introduction chapter of this thesis (chapter 1). Standardization of promoter prediction methodology - Part I Based on the difference in stability between neighboring upstream and downstream regions in the vicinity of experimentally determined transcription start sites, a promoter prediction algorithm has been developed to identify prokaryotic promoter sequences in whole genomes. The average free energy (E) over known promoter sequences and the difference (D) between E and the average free energy over the random sequence generated using the downstream region of known TSS (REav) are used to search for promoters in the genomic sequences. Using these cutoff values to predict promoter regions across entire E. coli genome, a reliability of 70% has been achieved, when the predicted promoters were cross verified against the 960 transcription start sites (TSSs) listed in the Ecocyc database. Reliable promoter prediction is obtained when these genome specific threshold values were used to search for promoters in the whole E. coli genome sequence. Annotation of the whole E. coli genome for promoter region has been carried out with 49% accuracy. Reference Rangannan, V. and Bansal, M. (2007) Identification and annotation of promoter regions inmicrobial genome sequences on the basis of DNA stability. J Biosci, 32, 851-862. Standardization of promoter prediction methodology - Part II In this chapter, it has been demonstrated that while the promoter regions are in general less stable than the flanking regions, their average free energy varies depending on the GC composition of the flanking genomic sequence. Therefore, a set of free energy threshold values (TSS based threshold values), from the genomic DNA with varying GC content in the vicinity of experimentally identified TSSs have been obtained. These threshold values have been used as generic criteria for predicting promoter regions in E. coli and B. subtilis and M. tuberculosis genomes, using an in-house developed tool ‘PromPredict’. On applying it to predict promoter regions corresponding to the 1144 and 612 experimentally validated TSSs in E. coli (genome %GC : 50.8) and B. subtilis (genome %GC : 43.5) sensitivity of 99% and 95% and precision values of 58% and 60%, respectively, were achieved. For the limited data set of 81 TSSs available for M. tuberculosis (65.6% GC) a sensitivity of 100% and precision of 49% was obtained. Reference Rangannan, V. and Bansal, M. (2009) Relative stability of DNA as a generic criterion for promoter prediction: whole genome annotation of microbial genomes with varying nucleotide base composition. Mol Biosyst, 5, 1758 - 1769. Standardization of promoter prediction methodology - Part III In this chapter, the promoter prediction algorithm and the threshold values have been improved to predict promoter regions on a large scale over 913 microbial genome sequences. The average free energy (AFE) values for the promoter regions as well as their downstream regions are found to differ, depending on their GC content even with respect to translation start sites (TLSs) from 913 microbial genomes. The TSS based cut-off values derived in chapter 3 do not have cut-off values for both extremes of GC-bins at 5% interval. Hence, threshold values have been derived from a subset of translation start sites (TLSs) from all microbial genomes which were categorized based on their GC-content. Interestingly the cut-off values derived with respect to TSS data set (chapter 3) and TLS data set are very similar for the in-between GC-bins. Therefore, TSS based cut-off values derived in chapter 2 with the TLS based cut-off values have been combined (denoted as TSS-TLS based cutoff values) to predict promoters over the complete genome sequences. An average recall value of 72% (which indicates the percentage of protein and RNA coding genes with predicted promoter regions assigned to them) and precision of 56% is achieved over the 913 microbial genome dataset. These predicted promoter regions have been given a reliability level (low, medium, high, very high and highest) based on the difference in its relative average free energy, which can help the users design their experiments with more confidence by using the predictions with higher reliability levels. Reference Rangannan, V. and Bansal, M. (2010) High Quality Annotation of Promoter Regions for 913 Bacterial Genomes. Bioinformatics, 26, 3043-3050. Web applications PromBase : The predicted promoter regions for 913 microbial genomes were deposited into a public domain database called, PromBase which can serve as a valuable resource for comparative genomics study for their general genomic features and also help the experimentalist to rapidly access the annotation of the promoter regions in any given genome. This database is freely accessible for the users via the World Wide Web http://nucleix.mbu.iisc.ernet.in/prombase/. EcoProm : EcoProm is a database that can identify and display the potential promoter regions corresponding to EcoCyc annotated TSS and genes. Also displays predictions for whole genomic sequence of E. coli and EcoProm is available at http://nucleix.mbu.iisc.ernet.in/ecoprom/index.htm. PromPredict : The generic promoter prediction methodology described in previous chapters has been implemented in to an algorithm ‘PromPredict’ and available at http://nucleix.mbu.iisc.ernet.in/prompredict/prompredict.html. Analysing the DNA structural characteristic of prokaryotic promoter sequences for their predominance Sequence dependent structural properties and their variation in genomic DNA are important in controlling several crucial processes such as transcription, replication, recombination and chromatin compaction. In this chapter 6, quantitative analysis of sequences motifs as well as sequence dependent structural properties, such as curvature, bendability and stability in the upstream region of TSS and TLS from E. coli, B. subtilis and M. tuberculosis has been carried out in order to assess their predictive power for promoter regions. Also the correlation between these structural properties and GC-content has been investigated. Our results have shown that AFE values (stability) gives finer discrimination rather than %GC in identifying promoter regions and stability have shown to be the better structural property in delineating promoter regions from non-promoter regions. Analysis of these DNA structural properties has been carried out in human promoter sequences and observed to be correlating with the inactivation status of the X-linked genes in human genome. Since, it is deviating from the theme of main thesis; this chapter has been included as appendix A to the main thesis. General conclusion Stability is the ubiquitous DNA structural property seen in promoter regions. Stability shows finer discrimination for promoter prediction rather than directly using %GC-content. Based on relative stability of DNA, a generic promoter prediction algorithm has been developed and implemented to predict promoter regions on a large scale over 913 microbial genome sequences. The analysis of the predicted regions across organisms showed highly reliable predictive performance of the algorithm.

Indiana University-Purdue University Indianapolis (IUPUI) / My thesis comprises of two individual projects: 1) we have developed a database for operon prediction using high-throughput sequencing datasets for bacterial genomes. 2) Genomics and mechanistic insights of convergent transcription in bacterial genomes. In the first project we developed a database for the prediction of operons for bacterial genomes using RNA-seq datasets, we predicted operons for bacterial genomes. RNA-seq datasets with different condition for each bacterial genome were taken into account and predicted operons using Rockhopper. We took RNA-seq datasets from NCBI with distinct experimental conditions for each bacterial genome into account and analyzed using tool for operon prediction. Currently our database contains 9 bacterial organisms for which we predicted operons. User interface is simple and easy to use, in terms of visualization, downloading and querying of data. In our database user can browse through reference genome, genes present in that genome and operons predicted from different RNA-seq datasets. Further in the second project, we studied the genomic and mechanistic insights of convergent transcription in bacterial genomes. We know that convergent gene pairs with overlapping head-to-head configuration are widely spread across both eukaryotic and prokaryotic genomes. They are believed to contribute to the regulation of genes at both transcriptional and post-transcriptional levels, although factors contributing to their abundance across genomes and mechanistic basis for their prevalence are poorly understood. In this study, we explore the role of various factors contributing to convergent overlapping transcription in bacterial genomes. Our analysis shows that the proportion of convergent overlapping gene pairs (COGPs) in a genome is affected due to endospore formation, bacterial habitat, oxygen requirement, GC content and the temperature range. In particular, we show that bacterial genomes thriving in specialized habitats, such as thermophiles, exhibit a high proportion of COGPs. Our results also conclude that the density distribution of COGPs across the genomes is high for shorter overlaps with increased conservation of distances for decreasing overlaps. Our study further reveals that COGPs frequently contain stop codon overlaps with the middle base position exhibiting mismatches between complementary strands. Further, for the functional analysis using cluster of orthologous groups (COGs) annotations suggested that cell motility, cell metabolism, storage and cell signaling are enriched among COGPs, suggesting their role in processes beyond regulation. Our analysis provides genomic insights into this unappreciated regulatory phenomenon, allowing a refined understanding of their contribution to bacterial phenotypes.