DEFENSOMES, COUNTER-DEFENSOMES, AND THE REMODELING OF MICROBIAL COMMUNITIES

Horizontal gene transfer (HGT) enables bacteria to swiftly adapt to new ecological niches and challenges. This process is primarily facilitated by mobile genetic elements (MGEs), such as bacteriophages (phages), plasmids, and transposable elements, which are prevalent in most genomes, often in multiple copies. The potential for conflicts arising from the interactions between MGEs and bacteria has driven the evolution of sophisticated defense mechanisms to filter, tame, or inactivate these elements. Well studied examples of anti-MGE immunity include restriction-modification (R-M), abortive infection, and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas systems. Altogether, they revolutionized the field of genome engineering as precise cleavage / stabilization / editing tools, and further propelled the quest for additional defense mechanisms as well as MGE counter-defense strategies capable of curbing their action. The last decade has witnessed the identification and, in some cases, the mechanistic characterization of an extensive arsenal of previously unknown anti-MGE defense systems. These systems can be deployed at various stages of the MGE infection process, either by degrading invading nucleic acids, inhibiting their replication, or inducing dormancy or death of infected cells to stop the mobile element’s spread through the microbial population. With the growing number of anti-MGE families identified, so has the discovery of MGE-encoded counter-defense systems. Such counter-defensome deploys multiple mechanisms to inactivate host immune systems (beyond bacteriophage gene mutations), that include direct binding to immune proteins, post-translational modification of immune proteins, targeting of secondary messengers, and counteracting metabolite-depleting defense systems.
Many of the defense and counter-defense systems known to date have been uncovered through bioinformatic exploration of reference genome databases (e.g., NCBI RefSeq). Yet, the latter overrepresent organisms that can largely be cultivated in laboratory, and therefore provide a limited snapshot of the uncharted fraction of environmental microbial diversity that remains uncultured. To characterize this hidden diversity, we recently performed a large-scale screening of high-quality bacterial population genomes reconstructed from environmental metagenomes, highlighting the diversity of defensomes and the potential for functional cooperation and generation of novel functions between different defensive modules [1]. Findings stemming from this study raised further questions related to the nature of conflicts and alliances between defense system families, breadth of counter-defense strategies in the environmental phageome, as well as the tantalizing prospect of prioritizing core-defense genes for the development of antimicrobials capable of targeting an entire bacterial species. We propose to address such questions in the current proposal as follows:

1) The analysis of defense system co-occurrence / co-localization and synergistic immunity across bacterial species and biomes;
2) A first-of-its kind large-scale mapping of the counter-defensome of phageomes across multiple environments;
3) Analysis of the core layers of the defensome across bacterial species, with additional proof of concept that such genes (many of them now known to be essential), can be used as targets to develop antimicrobials aiming at eliminating an entire bacterial species.

DNA METHYLATION AND THE 3D GENOME ORGANIZATION OF BACTERIA

DNA methylation in bacteria has been traditionally studied in the context of antiparasitic defense and as part of the innate immune discrimination between self and non-self DNA. However, sequencing advances that allow genome-wide analysis of DNA methylation at the single-base resolution are nowadays expanding and have propelled a modern epigenomic revolution in our understanding of the extent, evolution, and physiological relevance of methylation. Typically, the first step in studying the functional impacts of bacterial DNA methylation is to compare global gene expression between wild-type (WT) and methyltransferase (MTase) mutant strains. Several studies using RNA-seq for such comparisons have shown that perturbation of a single DNA MTase often results in tens, hundreds, and sometimes thousands of differentially expressed (DE) genes. According to the local competition model, competitive binding between an MTase and other DNA-binding proteins (e.g.: transcription factors) at specific motif sites affects transcription of a nearby gene, leading to phenotypic variation within the bacterial population. However, while in some cases the regulatory effects of MTases can be conclusively traced to methylation at the promoters of target genes, the large majority (>90%) of DE genes do not have methylated sites in their promoter regions, which implies that the local competition model does not apply to most DE genes. Another possibility is that the methylation status at individual motif sites might regulate the expression of a transcription factor, causing a broad downstream shift in the expression of its target genes. Yet, the latter is also not sufficiently explanatory for such a large number of DE genes. One hypothesis relates to the effect of DNA methylation on the chromosome topology whereby methylation induces structural changes that alter the repertoire of genes exposed to the cellular transcriptional machinery. We have recently identified CamA, a core MTase of Clostridioides difficile methylating at CAAAAA, with a
role in biofilm formation, sporulation, and in-vivo transmission. Moreover, in a subsequent large-scale analysis, we found that CamA was just the tip of the iceberg, with 45% of Genbank’s bacterial species containing at least one core or quasi-core MTase, which shows that the latter are abundant and suggests that their epigenetic modifications are likely important and frequent. On top of this, S-adenosyl-l-methionine (SAM) analogues were found to successfully inhibit CamA, in what represents a substantial first step in generating potent and selective epigenetically targeted therapeutics that can be exploited as new antimicrobials.
In this PhD project proposal, the successful candidate is asked to decipher the interplay between bacterial methylation, spatial genome organization and gene expression by answering the following questions: i) does methylation alter chromosomal interaction domains? ii) are DE genes and/or target methylation motifs enriched in changeable chromosomal interaction domain boundaries? iii) Can we tinker the methylome (globally or locally) to repress certain human pathogens? He / she will use Hi-C and long-read sequencing technologies combined with microbial genetics, and comparative genomics to broadly leverage the field of microbial epigenomics.

Towards a detailed understanding of the regulation of gene expression by acetylation and lactylation of histone proteins

In eukaryotic cells, DNA is wrapped around histone proteins to form chromatin. Dynamic modification of histones by various chemical structures enables fine regulation of gene expression. Alterations in these complex regulatory mechanisms are at the root of many diseases. Histone lysine acetylation is known to induce gene expression. Other structures can be added to histones, whose effects on transcription remain largely to be elucidated. Most of them, like lactylation discovered in 2019, depend on cellular metabolism. We have begun to study lactylation in the context of murine spermatogenesis. This process of cellular differentiation is a model of choice for studying the regulation of transcription, due to the dramatic changes in chromatin composition and the gene expression program. We have generated novel epigenetic profiles consisting of the genome-wide distribution of acetylated and lactylated marks on three histone H3 lysines. The aim of this thesis is to contribute to the deciphering of the “histone code”, firstly by studying the role of lactylations on the transcriptional program. Secondly, the prediction of chromatin states will be refined by integrating our new data with existing epigenomic data at the two studied cellular stages, within neural network models.

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