Julian Trouillon

Combinatorial group testing can reduce experimental costs and turnaround time by strategically pooling samples to minimize the number of measurements needed for a given experiment. Despite broad potential utility, it remains underutilized due to its intrinsic complexity and the lack of implementation tools. Here we present PoolPy, a unified end-to-end framework and web platform to benchmark, automate, and decode combinatorial group testing strategies. PoolPy tailors pooling designs to application-specific constraints, such as time, cost, or signal dilution, across experiment types. By implementing ten different pooling algorithms, which we comprehensively benchmark in silico across >100,000 conditions, we identify key design trade-offs that define pooling applicability to specific use cases. We experimentally validate PoolPy across diverse applications, including protein-ligand interaction screening, RT-qPCR viral testing and genome-wide protein-DNA interaction profiling, achieving a 60 to 93% reduction in number of measurements needed. Overall, PoolPy provides a scalable, user-friendly ecosystem to increase throughput and reduce costs across biological applications. PoolPy is available at https://poolpy.trouillonlab.org for open use.

Identifying genomic regions bound by individual proteins such as transcription factors is essential to understanding bacterial gene regulation; however, comprehensive understanding of the effect of protein occupancy on gene regulation would be prohibitively laborious and expensive to achieve using methods such as chromatin immunoprecipitation with sequencing (ChIP-seq) and ChIP with exonuclease treatment (ChIP-exo) for every protein and condition of interest. Here we describe a protocol for performing in vivo protein occupancy display-high resolution (IPOD-HR), a powerful method for genome-wide profiling of protein-bound DNA in prokaryotic systems. Although assay for transposase-accessible chromatin with sequencing (ATAC-seq) is the method of choice for assaying general protein occupancy in eukaryotic systems, bacterial nucleoid-associated proteins can affect ATAC-seq, rendering it unsuitable for use in bacteria. In contrast, IPOD-HR can be used to identify regions of bacterial genomes that are highly bound by proteins, regardless of the identity of the proteins bound, allowing the identification of condition- and genotype-dependent changes in protein occupancy associated with changes in gene regulation. The technique is coupled to RNA polymerase ChIP, followed by sequencing of the extracted samples and downstream analysis using open-source, automated software that we provide and actively maintain. Once cross-linked samples are obtained, the core DNA selection portion of the IPOD-HR protocol takes 3 calendar days to perform. The resulting DNA extracts are subjected to high-throughput sequencing, resulting in sequencing data that are analyzed, which typically requires a few additional days, depending on the number of samples and computing resources. The IPOD-HR experimental method requires familiarity with standard molecular biology techniques suitable for preparing Illumina sequencing inputs, and the computational post-processing pipeline requires basic knowledge of the Linux command line environment.

Etude des réseaux de régulation chez Pseudomonas aeruginosa: Pseudomonas aeruginosa est un pathogène opportuniste pour l’humain et une des premières causes d’infections nosocomiales. Cette bactérie Gram-négative possède un des réseaux de régulation les plus complexes, ce qui lui permet de s’adapter à un grand nombre d’environnements différents. Durant cette thèse, différents aspects de la régulation chez P. aeruginosa ont été investigués. Un nouveau facteur de transcription (FT), ErfA, a été identifié comme impliqué dans la régulation de la virulence dépendante d’ExlBA dans le groupe phylogénétique des souches PA7-like de P. aeruginosa. L’étude de la régulation d’exlBA dans plusieurs espèces de Pseudomonas a révélé une diversité de mécanismes de régulation pour ce facteur de virulence due à des différences de séquences régulatrices dans les promoteurs, ce qui illustre un mécanisme d’évolution de la régulation entre espèce proches. La diversité de promoteurs a été étudiée à l’échelle du génome et s’est révélée être relativement commune parmi tous les gènes. De plus, la caractérisation de tous les régulateurs qui possèdent la même architecture protéique que ErfA a permis d’identifier ces FTs comme des régulateurs locaux et spécialisés dans l’inhibition de voies métaboliques. Pour aller plus loin dans l’exploration de la régulation transcriptionnelle, la détermination des régulons de tous les régulateurs de réponses qui sont des FTs a permis la définition du réseau de régulation des systèmes à deux composantes, qui comprend plus de la moitié des gènes de P. aeruginosa. Enfin, l’étude de la régulation post-transcriptionnelle au travers de la caractérisation des interactomes de Hfq dans trois phases de croissance de différentes souches a permis d’identifier de nombreuses nouvelles interactions régulatrices communes ou spécifiques.

The opportunistic pathogen Pseudomonas aeruginosa relies on a large collection of two‐component regulatory systems (TCSs) to sense and adapt to changing environments. Among them, the Roc (regulation of cup) system is a one‐of‐a‐kind network of branched TCSs, composed of two histidine kinases (HKs—RocS1 and RocS2) interacting with three response regulators (RRs—RocA1, RocR, and RocA2), which regulate virulence, antibiotic resistance, and biofilm formation. Based on extensive work on the Roc system, previous data suggested the existence of other key regulators yet to be discovered. In this work, we identified PA4080, renamed RocA3, as a fourth RR that is activated by RocS1 and RocS2 and that positively controls the expression of the cupB operon. Comparative genomic analysis of the locus identified a gene—rocR3—adjacent to rocA3 in a subpopulation of strains that encodes a protein with structural and functional similarity to the c‐di‐GMP phosphodiesterase RocR. Furthermore, we identified a fourth branch of the Roc system consisting of the PA2583 HK, renamed RocS4, and the Hpt protein HptA. Using a bacterial two‐hybrid system, we showed that RocS4 interacts with HptA, which in turn interacts with RocA1, RocA2, and RocR3. Finally, we mapped the pangenomic RRs repertoire, establishing a comprehensive view of the plasticity of such regulators among clades of the species. Overall, our work provides a comprehensive inter‐species definition of the Roc system, nearly doubling the number of proteins known to be involved in this interconnected network of TCSs controlling pathogenicity in Pseudomonas species.

The activity of bacterial transcription factors (TFs) is typically modulated through direct interactions with small molecules. However, these input signals remain unknown for most TFs, even in well-studied model bacteria. Identifying these signals typically requires tedious experiments for each TF. Here, we develop a systematic workflow for the identification of TF input signals in bacteria based on metabolomics and transcriptomics data. We inferred the activity of 173 TFs from published transcriptomics data and determined the abundance of 279 metabolites across 40 matched experimental conditions in Escherichia coli . By correlating TF activities with metabolites abundances, we successfully identified previously known TF-metabolite interactions and predicted novel TF effector metabolites for 41 TFs. To validate our predictions, we conducted in vitro assays and confirmed a predicted effector metabolite for LeuO. As a result, we established a network of 80 regulatory interactions between 71 metabolites and 41 E. coli TFs. This network includes 76 novel interactions that encompass a diverse range of chemical classes and regulatory patterns, bringing us closer to a comprehensive TF regulatory network in E. coli .

Resource competition strongly shapes microbial community dynamics and function. In polysaccharide-degrading communities, primary degraders secrete monomer-releasing enzymes and exploiters consume released products without investing in enzymes. By investigating competitive strategies between chitin degraders and N-acetylglucosamine (GlcNAc) exploiters, we uncover several interacting factors, including GlcNAc liberation rates, diffusive loss, and resource partitioning between competitors, which determine community viability and growth dynamics at early stages of degradation. Aside from cases involving antibiotic secretion or aggregation on particles to privatize degrading enzymes, exploiters also inhibit degraders by siphoning away limiting GlcNAc flux during early stages of particle degradation, when degraders must overcome diffusive GlcNAc loss to sustain chitinase production. Analogous to the Allee effect in population biology, these effects lead to sensitive dependences on initial species densities for a community to thrive. This study elucidates how metabolic competition at early stages of particle degradation shapes species interactions, resource partitioning, and ultimately community viability.

The opportunistic pathogen Pseudomonas aeruginosa counts on a large collection of two-component regulatory systems (TCSs) to sense and adapt to changing environments. Among them, the Roc ( R egulation o f c up ) system is a one-of-a-kind network of branched TCSs, composed of two histidine kinases (HKs) (RocS1 and RocS2) interacting with three response regulators (RRs) (RocA1, RocR and RocA2), which regulate virulence, antibiotic resistance and biofilm formation. Based on extensive work on the Roc system, previous data suggested the existence of other key regulators yet to be discovered. In this work, we identified PA4080, renamed RocA3, as a fourth RR that is activated by RocS1 and RocS2 and that positively controls the expression of the cupB operon. Comparative genomic analysis of the locus identified a gene - rocR3 - adjacent to rocA3 in a subpopulation of strains which encodes a protein with structural and functional similarity to the c-di-GMP phosphodiesterase RocR. Furthermore, we identified a fourth branch of the Roc system consisting of the PA2583 HK, renamed RocS4, and of the Hpt protein HptA. Using a bacterial two-hybrid system, we showed that RocS4 interacts with HptA, which in turn interacts with RocA1, RocA2 and RocR3. Finally, we mapped the pangenomic RRs repertoire establishing a comprehensive view of the plasticity of such regulators among clades of the species. Overall, our work provides a comprehensive inter-species definition of the Roc system, nearly doubling the number of proteins known to be involved in this interconnected network of TCSs controlling pathogenicity in Pseudomonas species. GRAPHICAL ABBREVIATED SUMMARY: Roc system account for a particularly interconnected yet incomplete network of two-component regulatory system involved in the virulence of Pseudomonas aeruginosa . Our work identified the missing RocA3 regulator and propose new players of the system delineating their conservation between the clade of the species.

Two‐partner secretion (TPS) systems, also known as Type Vb secretion systems, allow the translocation of effector proteins across the outer membrane of Gram‐negative bacteria. By secreting different classes of effectors, including cytolysins and adhesins, TPS systems play important roles in bacterial pathogenesis and host interactions. Here, we review the current knowledge on TPS systems regulation and highlight specific and common regulatory mechanisms across TPS functional classes. We discuss in detail the specific regulatory networks identified in various bacterial species and emphasize the importance of understanding the context‐dependent regulation of TPS systems. Several regulatory cues reflecting host environment during infection, such as temperature and iron availability, are common determinants of expression for TPS systems, even across relatively distant species. These common regulatory pathways often affect TPS systems across subfamilies with different effector functions, representing conserved global infection‐related regulatory mechanisms.

Distinct niches of the mammalian gut are populated by diverse microbiota, but the contribution of spatial variation to intestinal metabolism remains unclear. Here we present a map of the longitudinal metabolome along the gut of healthy colonized and germ-free male mice. With this map, we reveal a general shift from amino acids in the small intestine to organic acids, vitamins and nucleotides in the large intestine. We compare the metabolic landscapes in colonized versus germ-free mice to disentangle the origin of many metabolites in different niches, which in some cases allows us to infer the underlying processes or identify the producing species. Beyond the known impact of diet on the small intestinal metabolic niche, distinct spatial patterns suggest specific microbial influence on the metabolome in the small intestine. Thus, we present a map of intestinal metabolism and identify metabolite–microbe associations, which provide a basis to connect the spatial occurrence of bioactive compounds to host or microorganism metabolism.

The major RNA-binding protein Hfq interacts with mRNAs, either alone or together with regulatory small noncoding RNAs (sRNAs), affecting mRNA translation and degradation in bacteria. However, studies tend to focus on single reference strains and assume that the findings may apply to the entire species, despite the important intra-species genetic diversity known to exist. Here, we use RIP-seq to identify Hfq-interacting RNAs in three strains representing the major phylogenetic lineages ofPseudomonas aeruginosa. We find that most interactions are in fact not conserved among the different strains. We identify growth phase-specific and strain-specific Hfq targets, including previously undescribed sRNAs. Strain-specific interactions are due to different accessory gene sets, RNA abundances, or potential context- or sequence- dependent regulatory mechanisms. The accessory Hfq interactome includes most mRNAs encoding Type III Secretion System (T3SS) components and secreted toxins in two strains, as well as a cluster of CRISPR guide RNAs in one strain. Conserved Hfq targets include the global virulence regulator Vfr and metabolic pathways involved in the transition from fast to slow growth. Furthermore, we use rGRIL-seq to show that RhlS, a quorum sensing sRNA, activates Vfr translation, thus revealing a link between quorum sensing and virulence regulation. Overall, our work highlights the important intra-species diversity in post-transcriptional regulatory networks inPseudomonas aeruginosa.