Multi-omics Analysis of CRISPRi-Knockdowns Identifies Mechanisms that Buffer Decreases of Enzymes in E. coli Metabolism

Technological advancement spurs Komagataella phaffii as a next-generation platform for sustainable biomanufacturing

Biomanufacturing stands as a cornerstone of sustainable industrial development, necessitating a shift toward non-food carbon feedstocks to alleviate agricultural resource competition and advance a circular bioeconomy. Methanol, a renewable one‑carbon substrate, has emerged as a pivotal candidate due to its abundance, cost-effectiveness, and high reduction potential, further bolstered by breakthroughs in CO₂ hydrogenation-based synthesis. Capitalizing on this momentum, the methylotrophic yeast Komagataella phaffii has undergone transformative technological upgrades, evolving from a conventional protein expression workhorse into an intelligent bioproduction chassis. This paradigm shift is fundamentally driven by converging innovations across CRISPR-empowered advancement in genome editing and AI-powered metabolic pathway design in K. phaffii. The integration of CRISPR systems with droplet microfluidics high-throughput screening has redefined strain engineering efficiency, achieving much higher editing precision than traditional homologous recombination while compressing the "design-build-test-learn" cycle. Concurrently, machine learning-enhanced genome-scale metabolic models facilitate dynamic flux balancing, enabling simultaneous improvements in product titers, carbon yields, and volumetric productivity. Finally, technological advancement promotes the application of K. phaffii, including directing more efficiently metabolic flux toward nutrient products, and strengthening efficient synthesis of excreted proteins. As DNA synthesis automation and robotic experimentation platforms mature, next-generation breakthroughs in genome modification, cofactor engineering, and AI-guided autonomous evolution will further cement K. phaffii as a next-generation platform for decarbonizing global manufacturing paradigms. This technological trajectory positions methanol-based biomanufacturing as a cornerstone of the low-carbon circular economy.

Systems-level design principles of metabolic rewiring in an animal

The regulation of metabolism is vital to any organism and can be achieved by transcriptionally activating or repressing metabolic genes1-3. Although many examples of transcriptional metabolic rewiring have been reported4, a systems-level study of how metabolism is rewired in response to metabolic perturbations is lacking in any animal. Here we apply Worm Perturb-Seq (WPS)-a high-throughput method combining whole-animal RNA-interference and RNA-sequencing5-to around 900 metabolic genes in the nematode Caenorhabditis elegans. We derive a metabolic gene regulatory network (mGRN) in which 385 perturbations are connected to 9,414 genes by more than 110,000 interactions. The mGRN has a highly modular structure in which 22 perturbation clusters connect to 44 gene expression programs. The mGRN reveals different modes of transcriptional rewiring from simple reaction and pathway compensation to rerouting and more complex network coordination. Using metabolic network modelling, we identify a design principle of transcriptional rewiring that we name the compensation-repression (CR) model. The CR model explains most transcriptional responses in metabolic genes and reveals a high level of compensation and repression in five core metabolic functions related to energy and biomass. We provide preliminary evidence that the CR model may also explain transcriptional metabolic rewiring in human cells.

The SPFH complex HflK-HflC regulates aerobic respiration in bacteria

The bacterial HflK-HflC membrane complex is a member of the highly conserved family of SPFH proteins, which are present in all domains of life and include eukaryotic stomatins, flotillins, and prohibitins. These proteins organize cell membranes and are involved in various processes. However, the exact physiological functions of most bacterial SPFH proteins remain unclear. Here, we report that the HflK-HflC complex in Escherichia coli is required for growth under high aeration. The absence of this complex causes a growth defect at high oxygen levels due to a reduced abundance of IspG, an essential iron-sulfur cluster enzyme in the isoprenoid biosynthetic pathway. This reduction might be related to lower stability of IspG and several other proteins, including the iron siderophore transporter TonB, in the absence of the HflK-HflC complex. Our results suggest that decreased IspG activity leads to lower levels of ubiquinone and misregulated expression of multiple respiratory enzymes, including cytochrome oxidases, and consequently reduced respiration and lower ATP levels. This impact of the hflK hflC deletion on aerobic respiration resembles the mitochondrial respiratory defects caused by the inactivation of prohibitins in mammalian and yeast cells, indicating functional parallels between these bacterial and eukaryotic SPFH proteins.

Arrayed CRISPRi library to suppress genes required for Schizosaccharomyces pombe viability

The fission yeast, Schizosaccharomyces pombe, is an excellent eukaryote model organism for studying essential biological processes. Its genome contains ∼1,200 genes essential for cell viability, most of which are evolutionarily conserved. To study these essential genes, resources enabling conditional perturbation of target genes are required. Here, we constructed comprehensive arrayed libraries of plasmids and strains to knock down essential genes in S. pombe using dCas9-mediated CRISPRi. These libraries cover ∼98% of all essential genes in fission yeast. We estimate that in ∼60% of these strains, transcription of a target gene was repressed so efficiently that cell proliferation was significantly inhibited. To demonstrate the usefulness of these libraries, we performed metabolic analyses with knockdown strains and revealed flexible interaction among metabolic pathways. Libraries established in this study enable comprehensive functional analyses of essential genes in S. pombe and will facilitate the understanding of essential biological processes in eukaryotes.

GLiDe: a web-based genome-scale CRISPRi sgRNA design tool for prokaryotes

BACKGROUND

CRISPRi screening has become a powerful approach for functional genomic research. However, the off-target effects resulting from the mismatch tolerance between sgRNAs and their intended targets is a primary concern in CRISPRi applications.

RESULTS

We introduce Guide Library Designer (GLiDe), a web-based tool specifically created for the genome-scale design of sgRNA libraries tailored for CRISPRi screening in prokaryotic organisms. GLiDe incorporates a robust quality control framework, rooted in prior experimental knowledge, ensuring the accurate identification of off-target hits. It boasts an extensive built-in database, encompassing 1,397 common prokaryotic species as a comprehensive design resource. It also provides the capability to design sgRNAs for newly discovered organisms by accepting uploaded design resource. We further demonstrated that GLiDe exhibits enhanced precision in identifying off-target binding sites for the CRISPRi system.

CONCLUSIONS

We present a web server that allows the construction of genome-scale CRISPRi sgRNA libraries for prokaryotes. It mitigates off-target effects through a robust quality control framework, leveraging prior experimental knowledge within an end-to-end, user-friendly pipeline.

Tailoring microbial fitness through computational steering and CRISPRi-driven robustness regulation

The widespread application of genetically modified microorganisms (GMMs) across diverse sectors underscores the pressing need for robust strategies to mitigate the risks associated with their potential uncontrolled escape. This study merges computational modeling with CRISPR interference (CRISPRi) to refine GMM metabolic robustness. Utilizing ensemble modeling, we achieved high-throughput in silico screening for enzymatic targets susceptible to expression alterations. Translating these insights, we developed functional CRISPRi, boosting fitness control via multiplexed gene knockdown. Our method, enhanced by an insulator-improved gRNA structure and an off-switch circuit controlling a compact Cas12m, resulted in rationally engineered strains with escape frequencies below National Institutes of Health standards. The effectiveness of this approach was confirmed under various conditions, showcasing its ability for secure GMM management. This research underscores the resilience of microbial metabolism, strategically modifying key nodes to halt growth without provoking significant resistance, thereby enabling more reliable and precise GMM control. A record of this paper's transparent peer review process is included in the supplemental information.

Intact protein barcoding enables one-shot identification of CRISPRi strains and their metabolic state

Detecting strain-specific barcodes with mass spectrometry can facilitate the screening of genetically engineered bacterial libraries. Here, we introduce intact protein barcoding, a method to measure protein-based library barcodes and metabolites using flow injection mass spectrometry (FI-MS). Protein barcodes are based on ubiquitin with N-terminal tags of six amino acids. We demonstrate that FI-MS detects intact ubiquitin proteins and identifies the mass of N-terminal barcodes. In the same analysis, we measured relative concentrations of primary metabolites. We constructed six ubiquitin-barcoded CRISPR interference (CRISPRi) strains targeting metabolic enzymes and analyzed their metabolic profiles and ubiquitin barcodes. FI-MS detected barcodes and distinct metabolome changes in CRISPRi-targeted pathways. We demonstrate the scalability of intact protein barcoding by measuring 132 ubiquitin barcodes in microtiter plates. These results show that intact protein barcoding enables fast and simultaneous detection of library barcodes and intracellular metabolites, opening up new possibilities for mass spectrometry-based barcoding.

AI-empowered perturbation proteomics for complex biological systems

The insufficient availability of comprehensive protein-level perturbation data is impeding the widespread adoption of systems biology. In this perspective, we introduce the rationale, essentiality, and practicality of perturbation proteomics. Biological systems are perturbed with diverse biological, chemical, and/or physical factors, followed by proteomic measurements at various levels, including changes in protein expression and turnover, post-translational modifications, protein interactions, transport, and localization, along with phenotypic data. Computational models, employing traditional machine learning or deep learning, identify or predict perturbation responses, mechanisms of action, and protein functions, aiding in therapy selection, compound design, and efficient experiment design. We propose to outline a generic PMMP (perturbation, measurement, modeling to prediction) pipeline and build foundation models or other suitable mathematical models based on large-scale perturbation proteomic data. Finally, we contrast modeling between artificially and naturally perturbed systems and highlight the importance of perturbation proteomics for advancing our understanding and predictive modeling of biological systems.

Whole genome CRISPRi screening identifies druggable vulnerabilities in an isoniazid resistant strain of Mycobacterium tuberculosis

Drug-resistant strains of Mycobacterium tuberculosis are a major global health problem. Resistance to the front-line antibiotic isoniazid is often associated with mutations in the katG-encoded bifunctional catalase-peroxidase. We hypothesise that perturbed KatG activity would generate collateral vulnerabilities in isoniazid-resistant katG mutants, providing potential pathway targets to combat isoniazid resistance. Whole genome CRISPRi screens, transcriptomics, and metabolomics were used to generate a genome-wide map of cellular vulnerabilities in an isoniazid-resistant katG mutant strain of M. tuberculosis. Here, we show that metabolic and transcriptional remodelling compensates for the loss of KatG but in doing so generates vulnerabilities in respiration, ribosome biogenesis, and nucleotide and amino acid metabolism. Importantly, these vulnerabilities are more sensitive to inhibition in an isoniazid-resistant katG mutant and translated to clinical isolates. This work highlights how changes in the physiology of drug-resistant strains generates druggable vulnerabilities that can be exploited to improve clinical outcomes.

In vitro transcription-based biosensing of glycolate for prototyping of a complex enzyme cascade

In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing, and metabolic engineering. Yet, the analysis of the activity of such in vitro networks is very often restricted by time- and cost-intensive methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that is compatible with the complex conditions of in vitro metabolism, such as the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a 27-component in vitro metabolic system that converts CO2 into glycolate. As proof of concept, we constructed a novel glycolate sensor module that is based on the transcriptional repressor GlcR from Paracoccus denitrificans and established an IVT biosensing workflow that allows us to quantify glycolate from CETCH samples in the micromolar to millimolar range. We investigate the influence of 13 (shared) cofactors between the two in vitro systems to show that Mg2+, adenosine triphosphate , and other phosphorylated metabolites are critical for robust signal output. Our optimized IVT biosensor correlates well with liquid chromatography-mass spectrometry-based glycolate quantification of CETCH samples, with one or multiple components varying (linear correlation 0.94-0.98), but notably at ∼10-fold lowered cost and ∼10 times faster turnover time. Our results demonstrate the potential and challenges of IVT-based systems to quantify and prototype the activity of complex reaction cascades and in vitro metabolic networks.

An outer membrane porin-lipoprotein complex modulates elongasome movement to establish cell curvature in Rhodospirillum rubrum

Curved cell shapes are widespread among bacteria and important for cellular motility, virulence and fitness. However, the underlying morphogenetic mechanisms are still incompletely understood. Here, we identify an outer-membrane protein complex that promotes cell curvature in the photosynthetic species Rhodospirillum rubrum. We show that the R. rubrum porins Por39 and Por41 form a helical ribbon-like structure at the outer curve of the cell that recruits the peptidoglycan-binding lipoprotein PapS, with PapS inactivation, porin delocalization or disruption of the porin-PapS interface resulting in cell straightening. We further demonstrate that porin-PapS assemblies act as molecular cages that entrap the cell elongation machinery, thus biasing cell growth towards the outer curve. These findings reveal a mechanistically distinct morphogenetic module mediating bacterial cell shape. Moreover, they uncover an unprecedented role of outer-membrane protein patterning in the spatial control of intracellular processes, adding an important facet to the repertoire of regulatory mechanisms in bacterial cell biology.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Protein overabundance is driven by growth robustness

Protein expression levels optimize cell fitness: Too low an expression level of essential proteins will slow growth by compromising essential processes; whereas overexpression slows growth by increasing the metabolic load. This trade-off naïvely predicts that cells maximize their fitness by sufficiency, expressing just enough of each essential protein for function. We test this prediction in the naturally-competent bacterium Acinetobacter baylyi by characterizing the proliferation dynamics of essential-gene knockouts at a single-cell scale (by imaging) as well as at a genome-wide scale (by TFNseq). In these experiments, cells proliferate for multiple generations as target protein levels are diluted from their endogenous levels. This approach facilitates a proteome-scale analysis of protein overabundance. As predicted by the Robustness-Load Trade-Off (RLTO) model, we find that roughly 70% of essential proteins are overabundant and that overabundance increases as the expression level decreases, the signature prediction of the model. These results reveal that robustness plays a fundamental role in determining the expression levels of essential genes and that overabundance is a key mechanism for ensuring robust growth.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Metabolomics and Microbial Metabolism: Toward a Systematic Understanding

Over the past decades, our understanding of microbial metabolism has increased dramatically. Metabolomics, a family of techniques that are used to measure the quantities of small molecules in biological samples, has been central to these efforts. Advances in analytical chemistry have made it possible to measure the relative and absolute concentrations of more and more compounds with increasing levels of certainty. In this review, we highlight how metabolomics has contributed to understanding microbial metabolism and in what ways it can still be deployed to expand our systematic understanding of metabolism. To that end, we explain how metabolomics was used to (a) characterize network topologies of metabolism and its regulation networks, (b) elucidate the control of metabolic function, and (c) understand the molecular basis of higher-order phenomena. We also discuss areas of inquiry where technological advances should continue to increase the impact of metabolomics, as well as areas where our understanding is bottlenecked by other factors such as the availability of statistical and modeling frameworks that can extract biological meaning from metabolomics data.

Connecting metabolome and phenotype: recent advances in functional metabolomics tools for the identification of bioactive natural products

Covering: 1995 to 2023Advances in bioanalytical methods, particularly mass spectrometry, have provided valuable molecular insights into the mechanisms of life. Non-targeted metabolomics aims to detect and (relatively) quantify all observable small molecules present in a biological system. By comparing small molecule abundances between different conditions or timepoints in a biological system, researchers can generate new hypotheses and begin to understand causes of observed phenotypes. Functional metabolomics aims to investigate the functional roles of metabolites at the scale of the metabolome. However, most functional metabolomics studies rely on indirect measurements and correlation analyses, which leads to ambiguity in the precise definition of functional metabolomics. In contrast, the field of natural products has a history of identifying the structures and bioactivities of primary and specialized metabolites. Here, we propose to expand and reframe functional metabolomics by integrating concepts from the fields of natural products and chemical biology. We highlight emerging functional metabolomics approaches that shift the focus from correlation to physical interactions, and we discuss how this allows researchers to uncover causal relationships between molecules and phenotypes.

CRISPRi: a way to integrate iPSC-derived neuronal models

The genetic landscape of neurodegenerative diseases encompasses genes affecting multiple cellular pathways which exert effects in an array of neuronal and glial cell-types. Deconvolution of the roles of genes implicated in disease and the effects of disease-associated variants remains a vital step in the understanding of neurodegeneration and the development of therapeutics. Disease modelling using patient induced pluripotent stem cells (iPSCs) has enabled the generation of key cell-types associated with disease whilst maintaining the genomic variants that predispose to neurodegeneration. The use of CRISPR interference (CRISPRi), alongside other CRISPR-perturbations, allows the modelling of the effects of these disease-associated variants or identifying genes which modify disease phenotypes. This review summarises the current applications of CRISPRi in iPSC-derived neuronal models, such as fluorescence-activated cell sorting (FACS)-based screens, and discusses the future opportunities for disease modelling, identification of disease risk modifiers and target/drug discovery in neurodegeneration.

Autotrophic growth of Escherichia coli is achieved by a small number of genetic changes

Synthetic autotrophy is a promising avenue to sustainable bioproduction from CO2. Here, we use iterative laboratory evolution to generate several distinct autotrophic strains. Utilising this genetic diversity, we identify that just three mutations are sufficient for Escherichia coli to grow autotrophically, when introduced alongside non-native energy (formate dehydrogenase) and carbon-fixing (RuBisCO, phosphoribulokinase, carbonic anhydrase) modules. The mutated genes are involved in glycolysis (pgi), central-carbon regulation (crp), and RNA transcription (rpoB). The pgi mutation reduces the enzyme's activity, thereby stabilising the carbon-fixing cycle by capping a major branching flux. For the other two mutations, we observe down-regulation of several metabolic pathways and increased expression of native genes associated with the carbon-fixing module (rpiB) and the energy module (fdoGH), as well as an increased ratio of NADH/NAD+ - the cycle's electron-donor. This study demonstrates the malleability of metabolism and its capacity to switch trophic modes using only a small number of genetic changes and could facilitate transforming other heterotrophic organisms into autotrophs.

Integrative Metabolomics and Proteomics Allow the Global Intracellular Characterization of Bacillus subtilis Cells and Spores

Reliable and comprehensive multi-omics analysis is essential for researchers to understand and explore complex biological systems more completely. Bacillus subtilis (B. subtilis) is a model organism for Gram-positive spore-forming bacteria, and in-depth insight into the physiology and molecular basis of spore formation and germination in this organism requires advanced multilayer molecular data sets generated from the same sample. In this study, we evaluated two monophasic methods for polar and nonpolar compound extraction (acetonitrile/methanol/water; isopropanol/water, and 60% ethanol) and two biphasic methods (chloroform/methanol/water, and methyl tert-butyl ether/methanol/water) on coefficients of variation of analytes, identified metabolite composition, and the quality of proteomics profiles. The 60% EtOH protocol proved to be the easiest in sample processing and was more amenable to automation. Collectively, we annotated 505 and 484 metabolites and identified 1665 and 1562 proteins in B. subtilis vegetative cells and spores, respectively. We also show differences between vegetative cells and spores from a multi-omics perspective and demonstrate that an integrative multi-omics analysis can be implemented from one sample using the 60% EtOH protocol. The results obtained by the 60% EtOH protocol provide comprehensive insight into differences in the metabolic and protein makeup of B. subtilis vegetative cells and spores.

Mobile CRISPRi moves through the complexity of bacterial genetics

In this issue of Cell Reports Methods, Rachwalski et al. describe a high-throughput method to screen genetic interactions in bacteria using a conjugative CRISPR interference (CRISPRi) plasmid. The method enables systematic studies of gene essentiality in diverse genomic and environmental contexts and is applicable to Escherichia coli as well as other bacteria.

A mobile CRISPRi collection enables genetic interaction studies for the essential genes of Escherichia coli

Advances in gene editing, in particular CRISPR interference (CRISPRi), have enabled depletion of essential cellular machinery to study the downstream effects on bacterial physiology. Here, we describe the construction of an ordered E. coli CRISPRi collection, designed to knock down the expression of 356 essential genes with the induction of a catalytically inactive Cas9, harbored on the conjugative plasmid pFD152. This mobile CRISPRi library can be conjugated into other ordered genetic libraries to assess combined effects of essential gene knockdowns with non-essential gene deletions. As proof of concept, we probed cell envelope synthesis with two complementary crosses: (1) an Lpp deletion into every CRISPRi knockdown strain and (2) the lolA knockdown plasmid into the Keio collection. These experiments revealed a number of notable genetic interactions for the essential phenotype probed and, in particular, showed suppressing interactions for the loci in question.

Synergistic investigation of natural and synthetic C1-trophic microorganisms to foster a circular carbon economy

A true circular carbon economy must upgrade waste greenhouse gases. C1-based biomanufacturing is an attractive solution, in which one carbon (C1) molecules (e.g. CO2, formate, methanol, etc.) are converted by microbial cell factories into value-added goods (i.e. food, feed, and chemicals). To render C1-based biomanufacturing cost-competitive, we must adapt microbial metabolism to perform chemical conversions at high rates and yields. To this end, the biotechnology community has undertaken two (seemingly opposing) paths: optimizing natural C1-trophic microorganisms versus engineering synthetic C1-assimilation de novo in model microorganisms. Here, we pose how these approaches can instead create synergies for strengthening the competitiveness of C1-based biomanufacturing as a whole.

Mapping temperature-sensitive mutations at a genome scale to engineer growth switches in Escherichia coli

Temperature-sensitive (TS) mutants are a unique tool to perturb and engineer cellular systems. Here, we constructed a CRISPR library with 15,120 Escherichia coli mutants, each with a single amino acid change in one of 346 essential proteins. 1,269 of these mutants showed temperature-sensitive growth in a time-resolved competition assay. We reconstructed 94 TS mutants and measured their metabolism under growth arrest at 42°C using metabolomics. Metabolome changes were strong and mutant-specific, showing that metabolism of nongrowing E. coli is perturbation-dependent. For example, 24 TS mutants of metabolic enzymes overproduced the direct substrate metabolite due to a bottleneck in their associated pathway. A strain with TS homoserine kinase (ThrBF267D ) produced homoserine for 24 h, and production was tunable by temperature. Finally, we used a TS subunit of DNA polymerase III (DnaXL289Q ) to decouple growth from arginine overproduction in engineered E. coli. These results provide a strategy to identify TS mutants en masse and demonstrate their large potential to produce bacterial metabolites with nongrowing cells.

Inhibition of Plasmid Conjugation in Escherichia coli by Targeting rbsB Gene Using CRISPRi System

Bacterial conjugation constitutes a major horizontal gene transfer mechanism for the dissemination of antibiotic-resistant genes (ARGs) among human pathogens. The spread of ARGs can be halted or diminished by interfering with the conjugation process. In this study, we explored the possibility of using an rbsB gene as a single target to inhibit plasmid-mediated horizontal gene transfer in Escherichia coli by CRISPR interference (CRISPRi) system. Three single-guide RNAs (sgRNAs) were designed to target the rbsB gene. The transcriptional levels of the rbsB gene, the conjugation-related genes, and the conjugation efficiency in the CRISPRi strain were tested. We further explored the effect of the repressed expression of the rbsB gene on the quorum sensing (QS) system and biofilm formation. The results showed that the constructed CRISPRi system was effective in repressing the transcriptional level of the rbsB gene at a rate of 66.4%. The repressed expression of the rbsB gene resulted in the reduced conjugation rate of RP4 plasmid by 88.7%, which significantly inhibited the expression of the conjugation-related genes (trbBp, trfAp, traF and traJ) and increased the global regulator genes (korA, korB and trbA). The repressed rbsB gene expression reduced the depletion of autoinducer 2 signals (AI-2) by 12.8% and biofilm formation by a rate of 68.2%. The results of this study indicated the rbsB gene could be used as a universal target for the inhibition of conjugation. The constructed conjugative CRISPRi system has the potential to be used in ARG high-risk areas.

CRISPRi-Mediated Silencing of Burkholderia O-Linked Glycosylation Systems Enables the Depletion of Glycosylation Yet Results in Modest Proteome Impacts

The process of O-linked protein glycosylation is highly conserved across the Burkholderia genus and mediated by the oligosaccharyltransferase PglL. While our understanding of Burkholderia glycoproteomes has increased in recent years, little is known about how Burkholderia species respond to modulations in glycosylation. Utilizing CRISPR interference (CRISPRi), we explored the impact of silencing of O-linked glycosylation across four species of Burkholderia; Burkholderia cenocepacia K56-2, Burkholderia diffusa MSMB375, Burkholderia multivorans ATCC17616, and Burkholderia thailandensis E264. Proteomic and glycoproteomic analyses revealed that while CRISPRi enabled inducible silencing of PglL, this did not abolish glycosylation, nor recapitulate phenotypes such as proteome changes or alterations in motility that are associated with glycosylation null strains, despite inhibition of glycosylation by nearly 90%. Importantly, this work also demonstrated that CRISPRi induction with high levels of rhamnose leads to extensive impacts on the Burkholderia proteomes, which without appropriate controls mask the impacts specifically driven by CRISPRi guides. Combined, this work revealed that while CRISPRi allows the modulation of O-linked glycosylation with reductions up to 90% at a phenotypic and proteome levels, Burkholderia appears to demonstrate a robust tolerance to fluctuations in glycosylation capacity.

An automated workflow for multi-omics screening of microbial model organisms

Multi-omics datasets are becoming of key importance to drive discovery in fundamental research as much as generating knowledge for applied biotechnology. However, the construction of such large datasets is usually time-consuming and expensive. Automation might enable to overcome these issues by streamlining workflows from sample generation to data analysis. Here, we describe the construction of a complex workflow for the generation of high-throughput microbial multi-omics datasets. The workflow comprises a custom-built platform for automated cultivation and sampling of microbes, sample preparation protocols, analytical methods for sample analysis and automated scripts for raw data processing. We demonstrate possibilities and limitations of such workflow in generating data for three biotechnologically relevant model organisms, namely Escherichia coli, Saccharomyces cerevisiae, and Pseudomonas putida.

Deep mutational scanning of essential bacterial proteins can guide antibiotic development

Deep mutational scanning is a powerful approach to investigate a wide variety of research questions including protein function and stability. Here, we perform deep mutational scanning on three essential E. coli proteins (FabZ, LpxC and MurA) involved in cell envelope synthesis using high-throughput CRISPR genome editing, and study the effect of the mutations in their original genomic context. We use more than 17,000 variants of the proteins to interrogate protein function and the importance of individual amino acids in supporting viability. Additionally, we exploit these libraries to study resistance development against antimicrobial compounds that target the selected proteins. Among the three proteins studied, MurA seems to be the superior antimicrobial target due to its low mutational flexibility, which decreases the chance of acquiring resistance-conferring mutations that simultaneously preserve MurA function. Additionally, we rank anti-LpxC lead compounds for further development, guided by the number of resistance-conferring mutations against each compound. Our results show that deep mutational scanning studies can be used to guide drug development, which we hope will contribute towards the development of novel antimicrobial therapies.

Large-Scale CRISPRi and Transcriptomics of Staphylococcus epidermidis Identify Genetic Factors Implicated in Lifestyle Versatility

Staphylococcus epidermidis is a ubiquitous human commensal skin bacterium that is also one of the most prevalent nosocomial pathogens. The genetic factors underlying this remarkable lifestyle plasticity are incompletely understood, mainly due to the difficulties of genetic manipulation, precluding high-throughput functional profiling of this species. To probe the versatility of S. epidermidis to survive across a diversity of environmental conditions, we developed a large-scale CRISPR interference (CRISPRi) screen complemented by transcriptional profiling (RNA sequencing) across 24 diverse conditions and piloted a droplet-based CRISPRi approach to enhance throughput and sensitivity. We identified putative essential genes, importantly revealing amino acid metabolism as crucial to survival across diverse environments, and demonstrated the importance of trace metal uptake for survival under multiple stress conditions. We identified pathways significantly enriched and repressed across our range of stress and nutrient-limited conditions, demonstrating the considerable plasticity of S. epidermidis in responding to environmental stressors. Additionally, we postulate a mechanism by which nitrogen metabolism is linked to lifestyle versatility in response to hyperosmotic challenges, such as those encountered on human skin. Finally, we examined the survival of S. epidermidis under acid stress and hypothesize a role for cell wall modification as a vital component of the survival response under acidic conditions. Taken together, this study integrates large-scale CRISPRi and transcriptomics data across multiple environments to provide insights into a keystone member of the human skin microbiome. Our results additionally provide a valuable benchmarking analysis for CRISPRi screens and are a rich resource for other staphylococcal researchers. IMPORTANCE Staphylococcus epidermidis is a bacteria that broadly inhabits healthy human skin, yet it is also a common cause of skin infections and bloodstream infections associated with implanted medical devices. Because human skin has many different types of S. epidermidis, each containing different genes, our goal is to determine how these different genes allow S. epidermidis to switch from healthy growth in the skin to being an infectious pathogen. Understanding this switch is critical to developing new strategies to prevent and treat S. epidermidis infections.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Interrogating the effect of enzyme kinetics on metabolism using differentiable constraint-based models

Metabolic models are typically characterized by a large number of parameters. Traditionally, metabolic control analysis is applied to differential equation-based models to investigate the sensitivity of predictions to parameters. A corresponding theory for constraint-based models is lacking, due to their formulation as optimization problems. Here, we show that optimal solutions of optimization problems can be efficiently differentiated using constrained optimization duality and implicit differentiation. We use this to calculate the sensitivities of predicted reaction fluxes and enzyme concentrations to turnover numbers in an enzyme-constrained metabolic model of Escherichia coli. The sensitivities quantitatively identify rate limiting enzymes and are mathematically precise, unlike current finite difference based approaches used for sensitivity analysis. Further, efficient differentiation of constraint-based models unlocks the ability to use gradient information for parameter estimation. We demonstrate this by improving, genome-wide, the state-of-the-art turnover number estimates for E. coli. Finally, we show that this technique can be generalized to arbitrarily complex models. By differentiating the optimal solution of a model incorporating both thermodynamic and kinetic rate equations, the effect of metabolite concentrations on biomass growth can be elucidated. We benchmark these metabolite sensitivities against a large experimental gene knockdown study, and find good alignment between the predicted sensitivities and in vivo metabolome changes. In sum, we demonstrate several applications of differentiating optimal solutions of constraint-based metabolic models, and show how it connects to classic metabolic control analysis.

Uncovering interactions between mycobacterial respiratory complexes to target drug-resistant Mycobacterium tuberculosis

Mycobacterium tuberculosis remains a leading cause of infectious disease morbidity and mortality for which new drug combination therapies are needed. Mycobacterial bioenergetics has emerged as a promising space for the development of novel therapeutics. Further to this, unique combinations of respiratory inhibitors have been shown to have synergistic or synthetic lethal interactions, suggesting that combinations of bioenergetic inhibitors could drastically shorten treatment times. Realizing the full potential of this unique target space requires an understanding of which combinations of respiratory complexes, when inhibited, have the strongest interactions and potential in a clinical setting. In this review, we discuss (i) chemical-interaction, (ii) genetic-interaction and (iii) chemical-genetic interaction studies to explore the consequences of inhibiting multiple mycobacterial respiratory components. We provide potential mechanisms to describe the basis for the strongest interactions. Finally, whilst we place an emphasis on interactions that occur with existing bioenergetic inhibitors, by highlighting interactions that occur with alternative respiratory components we envision that this information will provide a rational to further explore alternative proteins as potential drug targets and as part of unique drug combinations.

Thermodynamic limitations of PHB production from formate and fructose in Cupriavidus necator

The chemolithotroph Cupriavidus necator H16 is known as a natural producer of the bioplastic-polymer PHB, as well as for its metabolic versatility to utilize different substrates, including formate as the sole carbon and energy source. Depending on the entry point of the substrate, this versatility requires adjustment of the thermodynamic landscape to maintain sufficiently high driving forces for biological processes. Here we employed a model of the core metabolism of C. necator H16 to analyze the thermodynamic driving forces and PHB yields from formate for different metabolic engineering strategies. For this, we enumerated elementary flux modes (EFMs) of the network and evaluated their PHB yields as well as thermodynamics via Max-min driving force (MDF) analysis and random sampling of driving forces. A heterologous ATP:citrate lyase reaction was predicted to increase driving force for producing acetyl-CoA. A heterologous phosphoketolase reaction was predicted to increase maximal PHB yields as well as driving forces. These enzymes were then verified experimentally to enhance PHB titers between 60 and 300% in select conditions. The EFM analysis also revealed that PHB production from formate may be limited by low driving forces through citrate lyase and aconitase, as well as cofactor balancing, and identified additional reactions associated with low and high PHB yield. Proteomics analysis of the engineered strains confirmed an increased abundance of aconitase and cofactor balancing. The findings of this study aid in understanding metabolic adaptation. Furthermore, the outlined approach will be useful in designing metabolic engineering strategies in other non-model bacteria.

Engineering a CRISPR interference system targeting AcrAB-TolC efflux pump to prevent multidrug resistance development in Escherichia coli

OBJECTIVES

We engineered a CRISPR interference (CRISPRi) system targeting the AcrAB-TolC efflux pump to prevent MDR development in Escherichia coli.

METHODS

Nine specific single-guide RNAs (sgRNAs) were designed to target the components of the AcrAB-TolC efflux pump, namely AcrA, AcrB and TolC. A total of thirteen CRISPRi recombinant plasmids were constructed with single or clustered sgRNAs. The transcriptional levels of the target genes, MICs of multiple antibiotics and biofilm formation in each CRISPRi strain were tested.

RESULTS

The CRISPRi system expressing sgRNA clusters targeting acrB and tolC simultaneously exhibited the highest inhibitory effect on AcrAB-TolC efflux pump activity in E. coli HB101, with 78.3%, 90.0% and 65.4% inhibition rates on the transcriptional levels of acrA, acrB and tolC, respectively. The CRISPRi system resulted in ∼2-, ∼8- and 16-fold increased susceptibility to rifampicin, erythromycin and tetracycline, respectively. In addition, the constructed CRISPRi system reduced biofilm formation with inhibition rates in the range of 11.2% to 58.2%.

CONCLUSIONS

To the best of our knowledge, this is the first report on the construction of an inducible CRISPRi system targeting the AcrAB-TolC efflux pump to prevent MDR development in E. coli. This study provides insights for future regulation and manipulation of AcrAB-TolC activity and bacterial MDR by a CRISPRi system.

Expanding the search for small-molecule antibacterials by multidimensional profiling

New techniques for systematic profiling of small-molecule effects can enhance traditional growth inhibition screens for antibiotic discovery and change how we search for new antibacterial agents. Computational models that integrate physicochemical compound properties with their phenotypic and molecular downstream effects can not only predict efficacy of molecules yet to be tested, but also reveal unprecedented insights on compound modes of action (MoAs). The unbiased characterization of compounds that themselves are not growth inhibitory but exhibit diverse MoAs, can expand antibacterial strategies beyond direct inhibition of core essential functions. Early and systematic functional annotation of compound libraries thus paves the way to new models in the selection of lead antimicrobial compounds. In this Review, we discuss how multidimensional small-molecule profiling and the ever-increasing computing power are accelerating the discovery of unconventional antibacterials capable of bypassing resistance and exploiting synergies with established antibacterial treatments and with protective host mechanisms.

Gradients in gene essentiality reshape antibacterial research

Essential genes encode the processes that are necessary for life. Until recently, commonly applied binary classifications left no space between essential and non-essential genes. In this review, we frame bacterial gene essentiality in the context of genetic networks. We explore how the quantitative properties of gene essentiality are influenced by the nature of the encoded process, environmental conditions and genetic background, including a strain's distinct evolutionary history. The covered topics have important consequences for antibacterials, which inhibit essential processes. We argue that the quantitative properties of essentiality can thus be used to prioritize antibacterial cellular targets and desired spectrum of activity in specific infection settings. We summarize our points with a case study on the core essential genome of the cystic fibrosis pathobiome and highlight avenues for targeted antibacterial development.

New synthetic biology tools for metabolic control

In industrial bioprocesses, microbial metabolism dictates the product yields, and therefore, our capacity to control it has an enormous potential to help us move towards a bio-based economy. The rapid development of multiomics data has accelerated our systematic understanding of complex metabolic regulatory mechanisms, which allow us to develop tools to manipulate them. In the last few years, machine learning-based metabolic modeling, Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) derived synthetic biology tools, and synthetic genetic circuits have been widely used to control the metabolism of microorganisms, manipulate gene expression, and build synthetic pathways for bioproduction. This review describes the latest developments for metabolic control, and focuses on the trends and challenges of metabolic engineering strategies.

Combining CRISPRi and metabolomics for functional annotation of compound libraries

Molecular profiling of small-molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small molecule direct targets and secondary effects. However, current profiling methods are either limited in the number of measurable parameters or throughput. Here, we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from (CRISPR) interference with 352 genes in all major essential biological processes. Next, based on the comparison of genetic with 1342 drug-induced metabolic changes we made de novo predictions of compound functionality and revealed antibacterials with unconventional Modes of Action. We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality, from bacteria to human cell lines.

CRISPRi-seq for genome-wide fitness quantification in bacteria

CRISPR interference (CRISPRi) is a powerful tool to link essential and nonessential genes to specific phenotypes and to explore their functions. Here we describe a protocol for CRISPRi screenings to assess genome-wide gene fitness in a single sequencing step (CRISPRi-seq). We demonstrate the use of the protocol in Streptococcus pneumoniae, an important human pathogen; however, the protocol can easily be adapted for use in other organisms. The protocol includes a pipeline for single-guide RNA library design, workflows for pooled CRISPRi library construction, growth assays and sequencing steps, a read analysis tool (2FAST2Q) and instructions for fitness quantification. We describe how to make an IPTG-inducible system with small libraries that are easy to handle and cost-effective and overcome bottleneck issues, which can be a problem when using similar, transposon mutagenesis-based methods. Ultimately, the procedure yields a fitness score per single-guide RNA target for any given growth condition. A genome-wide screening can be finished in 1 week with a constructed library. Data analysis and follow-up confirmation experiments can be completed in another 2-3 weeks.

Multiplexed transcriptional repression identifies a network of bactericidal interactions between mycobacterial respiratory complexes

Mycobacterium tuberculosis remains a leading cause of infectious disease morbidity and mortality for which new drug combination therapies are needed. Combinations of respiratory inhibitors can have synergistic or synthetic lethal interactions with sterilizing activity, suggesting that regimens with multiple bioenergetic inhibitors could shorten treatment times. However, realizing this potential requires an understanding of which combinations of respiratory complexes, when inhibited, have the strongest consequences on bacterial growth and viability. Here we have used multiplex CRISPR interference (CRISPRi) and Mycobacterium smegmatis as a physiological and molecular model for mycobacterial respiration to identify interactions between respiratory complexes. In this study, we identified synthetic lethal and synergistic interactions between respiratory complexes and demonstrated how the engineering of CRISPRi-guide sequences can be used to further explore networks of interacting gene pairs. These results provide fundamental insights into the functions of and interactions between bioenergetic complexes and the utility of CRISPRi in designing drug combinations.

Deciphering the physiological response of Escherichia coli under high ATP demand

One long-standing question in microbiology is how microbes buffer perturbations in energy metabolism. In this study, we systematically analyzed the impact of different levels of ATP demand in Escherichia coli under various conditions (aerobic and anaerobic, with and without cell growth). One key finding is that, under all conditions tested, the glucose uptake increases with rising ATP demand, but only to a critical level beyond which it drops markedly, even below wild-type levels. Focusing on anaerobic growth and using metabolomics and proteomics data in combination with a kinetic model, we show that this biphasic behavior is induced by the dual dependency of the phosphofructokinase on ATP (substrate) and ADP (allosteric activator). This mechanism buffers increased ATP demands by a higher glycolytic flux but, as shown herein, it collapses under very low ATP concentrations. Model analysis also revealed two major rate-controlling steps in the glycolysis under high ATP demand, which could be confirmed experimentally. Our results provide new insights on fundamental mechanisms of bacterial energy metabolism and guide the rational engineering of highly productive cell factories.

Applications of CRISPR-Cas Technologies to Proteomics

CRISPR-Cas-based genome editing is a revolutionary approach that has provided an unprecedented investigational power for the life sciences. Rapid and efficient, CRISPR-Cas technologies facilitate the generation of complex biological models and at the same time provide the necessary methods required to study these models in depth. The field of proteomics has already significantly benefited from leveraging the power of CRISPR-Cas technologies, however, many potential applications of these technologies in the context of proteomics remain unexplored. In this review, we intend to provide an introduction to the CRISPR-Cas technologies and demonstrate how they can be applied to solving proteome-centric questions. To achieve this goal, we begin with the description of the modern suite of CRISPR-Cas-based tools, focusing on the more mature CRISPR-Cas9 system. In the second part of this review, we highlight both established and potential applications of the CRISPR-Cas technologies to proteomics.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Morphological and Transcriptional Responses to CRISPRi Knockdown of Essential Genes in Escherichia coli

CRISPR interference (CRISPRi) has facilitated the study of essential genes in diverse organisms using both high-throughput and targeted approaches. Despite the promise of this technique, no comprehensive arrayed CRISPRi library targeting essential genes exists for the model bacterium Escherichia coli, or for any Gram-negative species. Here, we built and characterized such a library. Each of the ∼500 strains in our E. coli library contains an inducible, chromosomally integrated single guide RNA (sgRNA) targeting an essential (or selected nonessential) gene and can be mated with a pseudo-Hfr donor strain carrying a dcas9 cassette to create a CRISPRi knockdown strain. Using this system, we built an arrayed library of CRISPRi strains and performed population and single-cell growth and morphology measurements as well as targeted follow-up experiments. These studies found that inhibiting translation causes an extended lag phase, identified new modulators of cell morphology, and revealed that the morphogene mreB is subject to transcriptional feedback regulation, which is critical for the maintenance of morphology. Our findings highlight canonical and noncanonical roles for essential genes in numerous aspects of cellular homeostasis. IMPORTANCE Essential genes make up only ∼5 to 10% of the genetic complement in most organisms but occupy much of their protein synthesis and account for almost all antibiotic targets. Despite the importance of essential genes, their intractability has, until recently, hampered efforts to study them. CRISPRi has facilitated the study of essential genes by allowing inducible and titratable depletion. However, all large-scale CRISPRi studies in Gram-negative bacteria thus far have used plasmids to express CRISPRi components and have been constructed in pools, limiting their utility for targeted assays and complicating the determination of antibiotic effects. Here, we use a modular method to construct an arrayed library of chromosomally integrated CRISPRi strains targeting the essential genes of the model bacterium Escherichia coli. This library enables targeted studies of essential gene depletions and high-throughput determination of antibiotic targets and facilitates studies targeting the outer membrane, an essential component that serves as the major barrier to antibiotics.

Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

Antibacterial agents target the products of essential genes but rarely achieve complete target inhibition. Thus, the all-or-none definition of essentiality afforded by traditional genetic approaches fails to discern the most attractive bacterial targets: those whose incomplete inhibition results in major fitness costs. In contrast, gene "vulnerability" is a continuous, quantifiable trait that relates the magnitude of gene inhibition to the effect on bacterial fitness. We developed a CRISPR interference-based functional genomics method to systematically titrate gene expression in Mycobacterium tuberculosis (Mtb) and monitor fitness outcomes. We identified highly vulnerable genes in various processes, including novel targets unexplored for drug discovery. Equally important, we identified invulnerable essential genes, potentially explaining failed drug discovery efforts. Comparison of vulnerability between the reference and a hypervirulent Mtb isolate revealed incomplete conservation of vulnerability and that differential vulnerability can predict differential antibacterial susceptibility. Our results quantitatively redefine essential bacterial processes and identify high-value targets for drug development.

Integrated mass spectrometry-based multi-omics for elucidating mechanisms of bacterial virulence

Despite being considered the simplest form of life, bacteria remain enigmatic, particularly in light of pathogenesis and evolving antimicrobial resistance. After three decades of genomics, we remain some way from understanding these organisms, and a substantial proportion of genes remain functionally unknown. Methodological advances, principally mass spectrometry (MS), are paving the way for parallel analysis of the proteome, metabolome and lipidome. Each provides a global, complementary assay, in addition to genomics, and the ability to better comprehend how pathogens respond to changes in their internal (e.g. mutation) and external environments consistent with infection-like conditions. Such responses include accessing necessary nutrients for survival in a hostile environment where co-colonizing bacteria and normal flora are acclimated to the prevailing conditions. Multi-omics can be harnessed across temporal and spatial (sub-cellular) dimensions to understand adaptation at the molecular level. Gene deletion libraries, in conjunction with large-scale approaches and evolving bioinformatics integration, will greatly facilitate next-generation vaccines and antimicrobial interventions by highlighting novel targets and pathogen-specific pathways. MS is also central in phenotypic characterization of surface biomolecules such as lipid A, as well as aiding in the determination of protein interactions and complexes. There is increasing evidence that bacteria are capable of widespread post-translational modification, including phosphorylation, glycosylation and acetylation; with each contributing to virulence. This review focuses on the bacterial genotype to phenotype transition and surveys the recent literature showing how the genome can be validated at the proteome, metabolome and lipidome levels to provide an integrated view of organism response to host conditions.

CRISPR interference identifies vulnerable cellular pathways with bactericidal phenotypes in Mycobacterium tuberculosis

Mycobacterium tuberculosis remains a leading cause of death for which new drugs are needed. The identification of drug targets has been advanced by high-throughput and targeted genetic deletion strategies. Each though has limitations including the inability to distinguish between levels of vulnerability, lethality and scalability as a molecular tool. Using mycobacterial CRISPR interference in combination with phenotypic screening we have overcome these individual issues to investigate essentiality, vulnerability and lethality for 94 target genes from a diverse array of cellular pathways, many of which are potential antibiotic targets. Essential genes involved in cell wall synthesis and central cellular functions were equally vulnerable and often had bactericidal consequences. Conversely, essential genes involved in metabolism, oxidative phosphorylation or amino acid synthesis were less vulnerable to inhibition and frequently bacteriostatic. In conclusion, this study provides novel insights into mycobacterial genetics and biology that will help to prioritise potential drug targets.

A CRISPR Interference Screen of Essential Genes Reveals that Proteasome Regulation Dictates Acetic Acid Tolerance in Saccharomyces cerevisiae

CRISPR interference (CRISPRi) is a powerful tool to study cellular physiology under different growth conditions, and this technology provides a means for screening changed expression of essential genes. In this study, a Saccharomyces cerevisiae CRISPRi library was screened for growth in medium supplemented with acetic acid. Acetic acid is a growth inhibitor challenging the use of yeast for the industrial conversion of lignocellulosic biomasses. Tolerance to acetic acid that is released during biomass hydrolysis is crucial for cell factories to be used in biorefineries. The CRISPRi library screened consists of >9,000 strains, where >98% of all essential and respiratory growth-essential genes were targeted with multiple guide RNAs (gRNAs). The screen was performed using the high-throughput, high-resolution Scan-o-matic platform, where each strain is analyzed separately. Our study identified that CRISPRi targeting of genes involved in vesicle formation or organelle transport processes led to severe growth inhibition during acetic acid stress, emphasizing the importance of these intracellular membrane structures in maintaining cell vitality. In contrast, strains in which genes encoding subunits of the 19S regulatory particle of the 26S proteasome were downregulated had increased tolerance to acetic acid, which we hypothesize is due to ATP salvage through an increased abundance of the 20S core particle that performs ATP-independent protein degradation. This is the first study where high-resolution CRISPRi library screening paves the way to understanding and bioengineering the robustness of yeast against acetic acid stress.

IMPORTANCE Acetic acid is inhibitory to the growth of the yeast Saccharomyces cerevisiae, causing ATP starvation and oxidative stress, which leads to the suboptimal production of fuels and chemicals from lignocellulosic biomass. In this study, where each strain of a CRISPRi library was characterized individually, many essential and respiratory growth-essential genes that regulate tolerance to acetic acid were identified, providing a new understanding of the stress response of yeast and new targets for the bioengineering of industrial yeast. Our findings on the fine-tuning of the expression of proteasomal genes leading to increased tolerance to acetic acid suggest that this could be a novel strategy for increasing stress tolerance, leading to improved strains for the production of biobased chemicals.

Emerging tools and strategies in cyanobacterial omics

Cyanobacteria are emerging as a popular system in both basic and applied microbial research. However, the incomplete understanding of their molecular biology hinders their practical applications in the industrial, agricultural, and environmental sectors. We present the potential of recently developed omics approaches to obtain deeper insights into cyanobacterial molecular physiology.

CRISPRi-Library-Guided Target Identification for Engineering Carotenoid Production by Corynebacterium glutamicum

Corynebacterium glutamicum is a prominent production host for various value-added compounds in white biotechnology. Gene repression by dCas9/clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi) allows for the identification of target genes for metabolic engineering. In this study, a CRISPRi-based library for the repression of 74 genes of C. glutamicum was constructed. The chosen genes included genes encoding enzymes of glycolysis, the pentose phosphate pathway, and the tricarboxylic acid cycle, regulatory genes, as well as genes of the methylerythritol phosphate and carotenoid biosynthesis pathways. As expected, CRISPRi-mediated repression of the carotenogenesis repressor gene crtR resulted in increased pigmentation and cellular content of the native carotenoid pigment decaprenoxanthin. CRISPRi screening identified 14 genes that affected decaprenoxanthin biosynthesis when repressed. Carotenoid biosynthesis was significantly decreased upon CRISPRi-mediated repression of 11 of these genes, while repression of 3 genes was beneficial for decaprenoxanthin production. Largely, but not in all cases, deletion of selected genes identified in the CRISPRi screen confirmed the pigmentation phenotypes obtained by CRISPRi. Notably, deletion of pgi as well as of gapA improved decaprenoxanthin levels 43-fold and 9-fold, respectively. The scope of the designed library to identify metabolic engineering targets, transfer of gene repression to stable gene deletion, and limitations of the approach were discussed.