Modeling the Pseudomonas Sulfur Regulome by Quantifying the Storage and Communication of Information

Metaphors as design tools for microbial consortia: An analysis of recent peer-reviewed literature

Single engineered microbial species cannot always conduct complex transformations, while complex, incompletely defined microbial consortia have heretofore been suited to a limited range of tasks. As biodesigners bridge this gap with intentionally designed microbial communities, they will, intentionally or otherwise, build communities that embody particular ideas about what microbial communities can and should be. Here, we suggest that metaphors-ideas about what microbial communities are like-are therefore important tools for designing synthetic consortia-based bioreactors. We identify a range of metaphors currently employed in peer-reviewed microbiome research articles, characterizing each through its potential structural implications and distinctive imagery. We present this metaphor catalogue in the interest of, first, making metaphors visible as design choices, second, enabling deliberate experimentation with them towards expanding the potential design space of the field, and third, encouraging reflection on the goals and values they embed.

Precision targeting of food biofilm-forming genes by microbial scissors: CRISPR-Cas as an effective modulator

The abrupt emergence of antimicrobial resistant (AMR) bacterial strains has been recognized as one of the biggest public health threats affecting the human race and food processing industries. One of the causes for the emergence of AMR is the ability of the microorganisms to form biofilm as a defense strategy that restricts the penetration of antimicrobial agents into bacterial cells. About 80% of human diseases are caused by biofilm-associated sessile microbes. Bacterial biofilm formation involves a cascade of genes that are regulated via the mechanism of quorum sensing (QS) and signaling pathways that control the production of the extracellular polymeric matrix (EPS), responsible for the three-dimensional architecture of the biofilm. Another defense strategy utilized commonly by various bacteria includes clustered regularly interspaced short palindromic repeats interference (CRISPRi) system that prevents the bacterial cell from viral invasion. Since multigenic signaling pathways and controlling systems are involved in each and every step of biofilm formation, the CRISPRi system can be adopted as an effective strategy to target the genomic system involved in biofilm formation. Overall, this technology enables site-specific integration of genes into the host enabling the development of paratransgenic control strategies to interfere with pathogenic bacterial strains. CRISPR-RNA-guided Cas9 endonuclease, being a promising genome editing tool, can be effectively programmed to re-sensitize the bacteria by targeting AMR-encoding plasmid genes involved in biofilm formation and virulence to revert bacterial resistance to antibiotics. CRISPRi-facilitated silencing of genes encoding regulatory proteins associated with biofilm production is considered by researchers as a dependable approach for editing gene networks in various biofilm-forming bacteria either by inactivating biofilm-forming genes or by integrating genes corresponding to antibiotic resistance or fluorescent markers into the host genome for better analysis of its functions both in vitro and in vivo or by editing genes to stop the secretion of toxins as harmful metabolites in food industries, thereby upgrading the human health status.

CRISPR interference to interrogate genes that control biofilm formation in Pseudomonas fluorescens

Bacterial biofilm formation involves signaling and regulatory pathways that control the transition from motile to sessile lifestyle, production of extracellular polymeric matrix, and maturation of the biofilm 3D structure. Biofilms are extensively studied because of their importance in biomedical, ecological and industrial settings. Gene inactivation is a powerful approach for functional studies but it is often labor intensive, limiting systematic gene surveys to the most tractable bacterial hosts. Here, we adapted the CRISPR interference (CRISPRi) system for use in diverse strain isolates of P. fluorescens, SBW25, WH6 and Pf0-1. We found that CRISPRi is applicable to study complex phenotypes such as cell morphology, motility and biofilm formation over extended periods of time. In SBW25, CRISPRi-mediated silencing of genes encoding the GacA/S two-component system and regulatory proteins associated with the cylic di-GMP signaling messenger produced swarming and biofilm phenotypes similar to those obtained after gene inactivation. Combined with detailed confocal microscopy of biofilms, our study also revealed novel phenotypes associated with extracellular matrix biosynthesis as well as the potent inhibition of SBW25 biofilm formation mediated by the PFLU1114 operon. We conclude that CRISPRi is a reliable and scalable approach to investigate gene networks in the diverse P. fluorescens group.

The Cognitive Lens: a primer on conceptual tools for analysing information processing in developmental and regenerative morphogenesis

Brains exhibit plasticity, multi-scale integration of information, computation and memory, having evolved by specialization of non-neural cells that already possessed many of the same molecular components and functions. The emerging field of basal cognition provides many examples of decision-making throughout a wide range of non-neural systems. How can biological information processing across scales of size and complexity be quantitatively characterized and exploited in biomedical settings? We use pattern regulation as a context in which to introduce the Cognitive Lens-a strategy using well-established concepts from cognitive and computer science to complement mechanistic investigation in biology. To facilitate the assimilation and application of these approaches across biology, we review tools from various quantitative disciplines, including dynamical systems, information theory and least-action principles. We propose that these tools can be extended beyond neural settings to predict and control systems-level outcomes, and to understand biological patterning as a form of primitive cognition. We hypothesize that a cognitive-level information-processing view of the functions of living systems can complement reductive perspectives, improving efficient top-down control of organism-level outcomes. Exploration of the deep parallels across diverse quantitative paradigms will drive integrative advances in evolutionary biology, regenerative medicine, synthetic bioengineering, cognitive neuroscience and artificial intelligence. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Approaches to Computational Strain Design in the Multiomics Era

Modern omics analyses are able to effectively characterize the genetic, regulatory, and metabolic phenotypes of engineered microbes, yet designing genetic interventions to achieve a desired phenotype remains challenging. With recent developments in genetic engineering techniques, timelines associated with building and testing strain designs have been greatly reduced, allowing for the first time an efficient closed loop iteration between experiment and analysis. However, the scale and complexity associated with multi-omics datasets complicates manual biological reasoning about the mechanisms driving phenotypic changes. Computational techniques therefore form a critical part of the Design-Build-Test-Learn (DBTL) cycle in metabolic engineering. Traditional statistical approaches can reduce the dimensionality of these datasets and identify common motifs among high-performing strains. While successful in many studies, these methods do not take full advantage of known connections between genes, proteins, and metabolic networks. There is therefore a growing interest in model-aided design, in which modeling frameworks from systems biology are used to integrate experimental data and generate effective and non-intuitive design predictions. In this mini-review, we discuss recent progress and challenges in this field. In particular, we compare methods augmenting flux balance analysis with additional constraints from fluxomic, genomic, and metabolomic datasets and methods employing kinetic representations of individual metabolic reactions, and machine learning. We conclude with a discussion of potential future directions for improving strain design predictions in the omics era and remaining experimental and computational hurdles.