Genome-wide screening identifies promiscuous phosphatases impairing terpenoid biosynthesis in Escherichia coli

Descriptor-augmented machine learning for enzyme-chemical interaction predictions

Descriptors play a pivotal role in enzyme design for the greener synthesis of biochemicals, as they could characterize enzymes and chemicals from the physicochemical and evolutionary perspective. This study examined the effects of various descriptors on the performance of Random Forest model used for enzyme-chemical relationships prediction. We curated activity data of seven specific enzyme families from the literature and developed the pipeline for evaluation the machine learning model performance using 10-fold cross-validation. The influence of protein and chemical descriptors was assessed in three scenarios, which were predicting the activity of unknown relations between known enzymes and known chemicals (new relationship evaluation), predicting the activity of novel enzymes on known chemicals (new enzyme evaluation), and predicting the activity of new chemicals on known enzymes (new chemical evaluation). The results showed that protein descriptors significantly enhanced the classification performance of model on new enzyme evaluation in three out of the seven datasets with the greatest number of enzymes, whereas chemical descriptors appear no effect. A variety of sequence-based and structure-based protein descriptors were constructed, among which the esm-2 descriptor achieved the best results. Using enzyme families as labels showed that descriptors could cluster proteins well, which could explain the contributions of descriptors to the machine learning model. As a counterpart, in the new chemical evaluation, chemical descriptors made significant improvement in four out of the seven datasets, while protein descriptors appear no effect. We attempted to evaluate the generalization ability of the model by correlating the statistics of the datasets with the performance of the models. The results showed that datasets with higher sequence similarity were more likely to get better results in the new enzyme evaluation and datasets with more enzymes were more likely beneficial from the protein descriptor strategy. This work provides guidance for the development of machine learning models for specific enzyme families.

Post-translational regulation of the mTORC1 pathway: A switch that regulates metabolism-related gene expression

The mechanistic target of rapamycin complex 1 (mTORC1) is a kinase complex that plays a crucial role in coordinating cell growth in response to various signals, including amino acids, growth factors, oxygen, and ATP. Activation of mTORC1 promotes cell growth and anabolism, while its suppression leads to catabolism and inhibition of cell growth, enabling cells to withstand nutrient scarcity and stress. Dysregulation of mTORC1 activity is associated with numerous diseases, such as cancer, metabolic disorders, and neurodegenerative conditions. This review focuses on how post-translational modifications, particularly phosphorylation and ubiquitination, modulate mTORC1 signaling pathway and their consequential implications for pathogenesis. Understanding the impact of phosphorylation and ubiquitination on the mTORC1 signaling pathway provides valuable insights into the regulation of cellular growth and potential therapeutic targets for related diseases.

New Target Gene Screening Using Shortened and Random sgRNA Libraries in Microbial CRISPR Interference

CRISPR interference (CRISPRi) screening has been used for identification of target genes related to specific phenotypes using single-molecular guide RNA (sgRNA) libraries. In CRISPRi screening, the sizes of random sgRNA libraries contained with the original target recognition sequences are large (∼10¹²). Here, we demonstrated that the length of the target recognition sequence (TRS) can be shortened in sgRNAs from the original 20 nucleotides (N₂₀) to 9 nucleotides (N₉) that is still sufficient for dCas9 to repress target genes in the xylose operon of Escherichia coli, regardless of binding to a promoter or open reading frame region. Based on the results, we constructed random sgRNA plasmid libraries with 5'-shortened TRS lengths, and identified xylose metabolic target genes by Sanger sequencing of sgRNA plasmids purified from Xyl^- phenotypic cells. Next, the random sgRNA libraries were harnessed to screen for target genes to enhance violacein pigment production in synthetic E. coli cells. Seventeen target genes were selected by analyzing the redundancy of the TRS in sgRNA plasmids in dark purple colonies. Among them, seven genes (tyrR, pykF, cra, ptsG, pykA, sdaA, and tnaA) have been known to increase the intracellular l-tryptophan pool, the precursor of a violacein. Seventeen cells with a single deletion of each target gene exhibited a significant increase in violacein production. These results indicate that using shortened random TRS libraries for CRISPRi can be simple and cost-effective for phenotype-based target gene screening.

Recent Advances in CRISPR-Cas Technologies for Synthetic Biology

With developments in synthetic biology, "engineering biology" has emerged through standardization and platformization based on hierarchical, orthogonal, and modularized biological systems. Genome engineering is necessary to manufacture and design synthetic cells with desired functions by using bioparts obtained from sequence databases. Among various tools, the CRISPR-Cas system is modularly composed of guide RNA and Cas nuclease; therefore, it is convenient for editing the genome freely. Recently, various strategies have been developed to accurately edit the genome at a single nucleotide level. Furthermore, CRISPR-Cas technology has been extended to molecular diagnostics for nucleic acids and detection of pathogens, including disease-causing viruses. Moreover, CRISPR technology, which can precisely control the expression of specific genes in cells, is evolving to find the target of metabolic biotechnology. In this review, we summarize the status of various CRISPR technologies that can be applied to synthetic biology and discuss the development of synthetic biology combined with CRISPR technology in microbiology.

Underground metabolism as a rich reservoir for pathway engineering

Motivation

Bioproduction of value-added compounds is frequently achieved by utilizing enzymes from other species. However, expression of such heterologous enzymes can be detrimental due to unexpected interactions within the host cell. Recently, an alternative strategy emerged, which relies on recruiting side activities of host enzymes to establish new biosynthetic pathways. Although such low-level ‘underground’ enzyme activities are prevalent, it remains poorly explored whether they may serve as an important reservoir for pathway engineering.

Results

Here, we use genome-scale modeling to estimate the theoretical potential of underground reactions for engineering novel biosynthetic pathways in Escherichia coli. We found that biochemical reactions contributed by underground enzyme activities often enhance the in silico production of compounds with industrial importance, including several cases where underground activities are indispensable for production. Most of these new capabilities can be achieved by the addition of one or two underground reactions to the native network, suggesting that only a few side activities need to be enhanced during implementation. Remarkably, we find that the contribution of underground reactions to the production of value-added compounds is comparable to that of heterologous reactions, underscoring their biotechnological potential. Taken together, our genome-wide study demonstrates that exploiting underground enzyme activities could be a promising addition to the toolbox of industrial strain development.

Availability and implementation

The data and scripts underlying this article are available on GitHub at https://github.com/pappb/Kovacs-et-al-Underground-metabolism.

Supplementary information

Supplementary data are available at Bioinformatics online.

Repurposing CRISPR-Cas Systems as Genetic Tools for the Enterobacteriales

Over the last decade, the study of CRISPR-Cas systems has progressed from a newly discovered bacterial defense mechanism to a diverse suite of genetic tools that have been applied across all domains of life. While the initial applications of CRISPR-Cas technology fulfilled a need to more precisely edit eukaryotic genomes, creative "repurposing" of this adaptive immune system has led to new approaches for genetic analysis of microorganisms, including improved gene editing, conditional gene regulation, plasmid curing and manipulation, and other novel uses. The main objective of this review is to describe the development and current state-of-the-art use of CRISPR-Cas techniques specifically as it is applied to members of the Enterobacteriales. While many of the applications covered have been initially developed in Escherichia coli, we also highlight the potential, along with the limitations, of this technology for expanding the availability of genetic tools in less-well-characterized non-model species, including bacterial pathogens.

Approaches for completing metabolic networks through metabolite damage and repair discovery

Metabolites are prone to damage, either via enzymatic side reactions, which collectively form the underground metabolism, or via spontaneous chemical reactions. The resulting non-canonical metabolites that can be toxic, are mended by dedicated "metabolite repair enzymes." Deficiencies in the latter can cause severe disease in humans, whereas inclusion of repair enzymes in metabolically engineered systems can improve the production yield of value-added chemicals. The metabolite damage and repair loops are typically not yet included in metabolic reconstructions and it is likely that many remain to be discovered. Here, we review strategies and associated challenges for unveiling non-canonical metabolites and metabolite repair enzymes, including systematic approaches based on high-resolution mass spectrometry, metabolome-wide side-activity prediction, as well as high-throughput substrate and phenotypic screens.

CRISPR screens in the era of microbiomes

Recent advances in genomics have uncovered the tremendous diversity and richness of microbial ecosystems. New functional genomics methods are now needed to probe gene function in high-throughput and provide mechanistic insights. Here, we review how the CRISPR toolbox can be used to inactivate, repress or overexpress genes in a sequence-specific manner and how this offers diverse attractive solutions to identify gene function in high-throughput. Developed both in eukaryotes and prokaryotes, CRISPR screening technologies have already provided meaningful insights in microbiology and host-pathogen interactions. In the era of microbiomes, the versatility and the functional diversity of CRISPR-derived tools has the potential to significantly improve our understanding of microbial communities and their interaction with the host.

Haloacid Dehalogenase Proteins: Novel Mediators of Metabolic Plasticity in Plasmodium falciparum

Widespread antimalarial drug resistance has prompted the need for new therapeutics and greater understanding of malaria parasite biology. To this end, the isoprenoid biosynthesis inhibitor fosmidomycin has been used to probe the metabolic regulation in the malaria parasite, Plasmodium falciparum. Genetic changes in the haloacid dehalogenase (HAD) superfamily member HAD2 conferred resistance to fosmidomycin, at the cost of decreased fitness. In the absence of fosmidomycin, parasites gained mutations to phosphofructokinase that restored growth and fosmidomycin sensitivity, thus revealing an intriguing example of plasticity in a core glycolytic process. Moreover, this study marks a second report of a HAD superfamily protein-modulating metabolic homeostasis in P falciparum parasites. Haloacid dehalogenase enzymes are distributed across all domains of life and have increasingly been found to influence central carbon metabolism and drug sensitivity in P falciparum. Investigating the mechanisms by which HAD superfamily members modulate metabolism may shed light on how metabolic networks are connected in apicomplexan parasites and other organisms and may guide future therapeutic endeavors.