Local genetic context shapes the function of a gene regulatory network

Lineage-specific microbial protein prediction enables large-scale exploration of protein ecology within the human gut

Microbes use a range of genetic codes and gene structures, yet these are often ignored during metagenomic analysis. This causes spurious protein predictions, preventing functional assignment which limits our understanding of ecosystems. To resolve this, we developed a lineage-specific gene prediction approach that uses the correct genetic code based on the taxonomic assignment of genetic fragments, removes incomplete protein predictions, and optimises prediction of small proteins. Applied to 9634 metagenomes and 3594 genomes from the human gut, this approach increased the landscape of captured expressed microbial proteins by 78.9%, including previously hidden functional groups. Optimised small protein prediction captured 3,772,658 small protein clusters, which form an improved microbial protein catalogue of the human gut (MiProGut). To enable the ecological study of a protein's prevalence and association with host parameters, we developed InvestiGUT, a tool which integrates both the protein sequences and sample metadata. Accurate prediction of proteins is critical to providing a functional understanding of microbiomes, enhancing our ability to study interactions between microbes and hosts.

2024: A "nucleoid space" odyssey featuring H-NS

The three-dimensional architecture of the bacterial chromosome is intertwined with genome processes such as transcription and replication. Conspicuously so, that the structure of the chromosome permits accurate prediction of active genome processes. Although appreciation of this interplay has developed rapidly in the past two decades, our understanding of this subject is still in its infancy, with research primarily focusing on how the process of transcription regulates and is regulated by chromosome structure. Here, we summarize the latest developments in the field with a focus on the interplay between chromosome structure and transcription in Escherichia coli (E. coli) as mediated by H-NS-a model nucleoid structuring protein. We describe how the organization of chromosomes at the global and local scales is dependent on transcription, and how transcription is regulated by chromosome structure. Finally, we take note of studies that highlight our limited knowledge of structure-function relationships in the chromosome, and we point out research tracks that will improve our insight in the topic.

Synthetic gene circuit evolution: Insights and opportunities at the mid-scale

Directed evolution focuses on optimizing single genetic components for predefined engineering goals by artificial mutagenesis and selection. In contrast, experimental evolution studies the adaptation of entire genomes in serially propagated cell populations, to provide an experimental basis for evolutionary theory. There is a relatively unexplored gap at the middle ground between these two techniques, to evolve in vivo entire synthetic gene circuits with nontrivial dynamic function instead of single parts or whole genomes. We discuss the requirements for such mid-scale evolution, with hypothetical examples for evolving synthetic gene circuits by appropriate selection and targeted shuffling of a seed set of genetic components in vivo. Implementing similar methods should aid the rapid generation, functionalization, and optimization of synthetic gene circuits in various organisms and environments, accelerating both the development of biomedical and technological applications and the understanding of principles guiding regulatory network evolution.

Advancing the scale of synthetic biology via cross-species transfer of cellular functions enabled by iModulon engraftment

Machine learning applied to large compendia of transcriptomic data has enabled the decomposition of bacterial transcriptomes to identify independently modulated sets of genes, such iModulons represent specific cellular functions. The identification of iModulons enables accurate identification of genes necessary and sufficient for cross-species transfer of cellular functions. We demonstrate cross-species transfer of: 1) the biotransformation of vanillate to protocatechuate, 2) a malonate catabolic pathway, 3) a catabolic pathway for 2,3-butanediol, and 4) an antimicrobial resistance to ampicillin found in multiple Pseudomonas species to Escherichia coli. iModulon-based engineering is a transformative strategy as it includes all genes comprising the transferred cellular function, including genes without functional annotation. Adaptive laboratory evolution was deployed to optimize the cellular function transferred, revealing mutations in the host. Combining big data analytics and laboratory evolution thus enhances the level of understanding of systems biology, and synthetic biology for strain design and development.

Inference of differential gene regulatory networks using boosted differential trees

Summary

Diseases can be caused by molecular perturbations that induce specific changes in regulatory interactions and their coordinated expression, also referred to as network rewiring. However, the detection of complex changes in regulatory connections remains a challenging task and would benefit from the development of novel nonparametric approaches. We develop a new ensemble method called BoostDiff (boosted differential regression trees) to infer a differential network discriminating between two conditions. BoostDiff builds an adaptively boosted (AdaBoost) ensemble of differential trees with respect to a target condition. To build the differential trees, we propose differential variance improvement as a novel splitting criterion. Variable importance measures derived from the resulting models are used to reflect changes in gene expression predictability and to build the output differential networks. BoostDiff outperforms existing differential network methods on simulated data evaluated in four different complexity settings. We then demonstrate the power of our approach when applied to real transcriptomics data in COVID-19, Crohn's disease, breast cancer, prostate adenocarcinoma, and stress response in Bacillus subtilis. BoostDiff identifies context-specific networks that are enriched with genes of known disease-relevant pathways and complements standard differential expression analyses.

Availability and implementation

BoostDiff is available at https://github.com/scibiome/boostdiff_inference.

Assessing in vivo the impact of gene context on transcription through DNA supercoiling

Gene context can have significant impact on gene expression but is currently not integrated in quantitative models of gene regulation despite known biophysical principles and quantitative in vitro measurements. Conceptually, the simplest gene context consists of a single gene framed by two topological barriers, known as the twin transcriptional-loop model, which illustrates the interplay between transcription and DNA supercoiling. In vivo, DNA supercoiling is additionally modulated by topoisomerases, whose modus operandi remains to be quantified. Here, we bridge the gap between theory and in vivo properties by realizing in Escherichia coli the twin transcriptional-loop model and by measuring how gene expression varies with promoters and distances to the topological barriers. We find that gene expression depends on the distance to the upstream barrier but not to the downstream barrier, with a promoter-dependent intensity. We rationalize these findings with a first-principle biophysical model of DNA transcription. Our results are explained if TopoI and gyrase both act specifically, respectively upstream and downstream of the gene, with antagonistic effects of TopoI, which can repress initiation while facilitating elongation. Altogether, our work sets the foundations for a systematic and quantitative description of the impact of gene context on gene regulation.

Precise programming of multigene expression stoichiometry in mammalian cells by a modular and programmable transcriptional system

Context-dependency of mammalian transcriptional elements has hindered the quantitative investigation of multigene expression stoichiometry and its biological functions. Here, we describe a host- and local DNA context-independent transcription system to gradually fine-tune single and multiple gene expression with predictable stoichiometries. The mammalian transcription system is composed of a library of modular and programmable promoters from bacteriophage and its cognate RNA polymerase (RNAP) fused to a capping enzyme. The relative expression of single genes is quantitatively determined by the relative binding affinity of the RNAP to the promoters, while multigene expression stoichiometry is predicted by a simple biochemical model with resource competition. We use these programmable and modular promoters to predictably tune the expression of three components of an influenza A virus-like particle (VLP). Optimized stoichiometry leads to a 2-fold yield of intact VLP complexes. The host-independent orthogonal transcription system provides a platform for dose-dependent control of multiple protein expression which may be applied for advanced vaccine engineering, cell-fate programming and other therapeutic applications.

A Maverick-like cluster in the genome of a pathogenic, moderately virulent strain of Gallibacterium anatis, ESV200, a transient biofilm producer

Introduction

Gallibacterium anatis causes gallibacteriosis in birds. These bacteria produce biofilms and secrete several fimbrial appendages as tools to cause disease in animals. G. anatis strains contain up to three types of fimbriae. Complete genome sequencing is the strategy currently used to determine variations in the gene content of G. anatis, although today only the completely circularized genome of G. anatis UMN179 is available.

Methods

The appearance of growth of various strains of G. anatis in liquid culture medium was studied. Biofilm production and how the amount of biofilm was affected by DNase, Proteinase K, and Pronase E enzymes were analyzed. Fimbrial gene expression was performed by protein analysis and qRT-PCR. In an avian model, the pathogenesis generated by the strains G. anatis ESV200 and 12656-12 was investigated. Using bioinformatic tools, the complete genome of G. anatis ESV200 was comparatively studied to search for virulence factors that would help explain the pathogenic behavior of this strain.

Results and Discussion

G. anatis ESV200 strain differs from the 12656-12 strain because it produces a biofilm at 20%. G. anatis ESV200 strain express fimbrial genes and produces biofilm but with a different structure than that observed for strain 12656-12. ESV200 and 12656-12 strains are pathogenic for chickens, although the latter is the most virulent. Here, we show that the complete genome of the ESV200 strain is similar to that of the UNM179 strain. However, these strains have evolved with many structural rearrangements; the most striking chromosomal arrangement is a Maverick-like element present in the ESV200 strain.

Molecular basis for lethal cross-talk between two unrelated bacterial transcription factors - the regulatory protein of a restriction-modification system and the repressor of a defective prophage

Bacterial gene expression depends on the efficient functioning of global transcriptional networks, however their interconnectivity and orchestration rely mainly on the action of individual DNA binding proteins called transcription factors (TFs). TFs interact not only with their specific target sites, but also with secondary (off-target) sites, and vary in their promiscuity. It is not clear yet what mechanisms govern the interactions with secondary sites, and how such rewiring affects the overall regulatory network, but this could clearly constrain horizontal gene transfer. Here, we show the molecular mechanism of one such off-target interaction between two unrelated TFs in Escherichia coli: the C regulatory protein of a Type II restriction-modification system, and the RacR repressor of a defective prophage. We reveal that the C protein interferes with RacR repressor expression, resulting in derepression of the toxic YdaT protein. These results also provide novel insights into regulation of the racR-ydaST operon. We mapped the C regulator interaction to a specific off-target site, and also visualized C protein dynamics, revealing intriguing differences in single molecule dynamics in different genetic contexts. Our results demonstrate an apparent example of horizontal gene transfer leading to adventitious TF cross-talk with negative effects on the recipient's viability. More broadly, this study represents an experimentally-accessible model of a regulatory constraint on horizontal gene transfer.

Genetic context effects can override canonical cis regulatory elements in Escherichia coli

Recent experiments have shown that in addition to control by cis regulatory elements, the local chromosomal context of a gene also has a profound impact on its transcription. Although this chromosome-position dependent expression variation has been empirically mapped at high-resolution, the underlying causes of the variation have not been elucidated. Here, we demonstrate that 1 kb of flanking, non-coding synthetic sequences with a low frequency of guanosine and cytosine (GC) can dramatically reduce reporter expression compared to neutral and high GC-content flanks in Escherichia coli. Natural and artificial genetic context can have a similarly strong effect on reporter expression, regardless of cell growth phase or medium. Despite the strong reduction in the maximal expression level from the fully-induced reporter, low GC synthetic flanks do not affect the time required to reach the maximal expression level after induction. Overall, we demonstrate key determinants of transcriptional propensity that appear to act as tunable modulators of transcription, independent of regulatory sequences such as the promoter. These findings provide insight into the regulation of naturally occurring genes and an independent control for optimizing expression of synthetic biology constructs.

Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (Nielsen, A., et al., Science, 2016, 352 (6281), DOI: 10.1126/science.aac7341). Across this set of functions, 32 of them can be improved by simply considering structural variants yielding performance gains of up to 7.9-fold, whereas 22 of them can be improved with gains up to 26-fold when selecting circuits according to the novel robustness score. We furthermore report on the synergistic combination of the two proposed improvements.