Genome-wide genotype-phenotype associations in microbes

Unraveling the genetic basis of microbial metal resistance: Shift from mendelian to systems biology

Microbial metal resistance, a trait that enables microorganisms to withstand high levels of toxic metals, has been studied for over a century. The significance of uncovering these mechanisms goes beyond basic science as they have implications for human health through their connection to microbial pathogenesis, metal bioremediation, and biomining. Recent advances in analytical chemistry and molecular biology have accelerated the discovery and understanding of genetic mechanisms underlying microbial metal resistance, identifying specific metal resistance genes and their operons. The emergence of omics tools has further propelled research towards a comprehensive understanding of how cells respond to metal stress at the systemic level, revealing the complex regulatory networks and evolutionary dynamics that drive microbial adaptation to metal-rich environments. In this article, we present a historical overview of the evolving understanding of the genetic determinants of metal resistance in microbes. Through multiple narrative threads, we illustrate how our knowledge of microbial metal resistance and genetics has interacted with genetic tools and concept development. This review also discusses how our understanding of microbial metal resistance has progressed from the Mendelian perspective to the current systems biology viewpoint, particularly as omics approaches have considerably enhanced our understanding. This system-level understanding has opened new possibilities for genetically engineered microorganisms to regulate metal homeostasis.

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.

Assessment of Genomic and Metabolic Characteristics of Cholesterol-Reducing and GABA Producer Limosilactobacillus fermentum AGA52 Isolated from Lactic Acid Fermented Shalgam Based on "In Silico" and "In Vitro" Approaches

This study aimed to characterize the genomic and metabolic properties of a novel Lb. fermentum strain AGA52 which was isolated from a lactic acid fermented beverage called "shalgam." The genome size of AGA52 was 2,001,184 bp, which is predicted to carry 2024 genes, including 50 tRNAs, 3 rRNAs, 3 ncRNAs, 15 CRISPR repeats, 14 CRISPR spacers, and 1 CRISPR array. The genome has a GC content of 51.82% including 95 predicted pseudogenes, 56 complete or partial transposases, and 2 intact prophages. The similarity of the clusters of orthologous groups (COG) was analyzed by comparison with the other Lb. fermentum strains. The detected resistome on the genome of AGA52 was found to be intrinsic originated. Besides, it has been determined that AGA52 has an obligate heterofermentative carbohydrate metabolism due to the absence of the 1-phosphofructokinase (pfK) enzyme. Furthermore, the strain is found to have a better antioxidant capacity and to be tolerant to gastrointestinal simulated conditions. It was also observed that the AGA52 has antimicrobial activity against Yersinia enterocolitica ATCC9610, Bacillus cereus ATCC33019, Salmonella enterica sv. Typhimurium, Escherichia coli O157:h7 ATCC43897, Listeria monocytogenes ATCC7644, Klebsiella pneumoniae ATCC13883, and Proteus vulgaris ATCC8427. Additionally, AGA52 exhibited 42.74 ± 4.82% adherence to HT29 cells. Cholesterol assimilation (33.9 ± 0.005%) and GABA production capacities were also confirmed by "in silico" and "in vitro." Overall, the investigation of genomic and metabolic features of the AGA52 revealed that is a potential psychobiotic and probiotic dietary supplement candidate and can bring functional benefits to the host.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

Genome-scale CRISPRi screening: A powerful tool in engineering microbiology

Deciphering gene function is fundamental to engineering of microbiology. The clustered regularly interspaced short palindromic repeats (CRISPR) system has been adapted for gene repression across a range of hosts, creating a versatile tool called CRISPR interference (CRISPRi) that enables genome-scale analysis of gene function. This approach has yielded significant advances in the design of genome-scale CRISPRi libraries, as well as in applications of CRISPRi screening in medical and industrial microbiology. This review provides an overview of the recent progress made in pooled and arrayed CRISPRi screening in microorganisms and highlights representative studies that have employed this method. Additionally, the challenges associated with CRISPRi screening are discussed, and potential solutions for optimizing this strategy are proposed.

Development of a synthetic transcription factor-based S-adenosylmethionine biosensor in Saccharomyces cerevisiae

S-Adenosylmethionine (SAM) is a crucial small-molecule metabolite widely used in food and medicine. The development of high-throughput biosensors for SAM biosynthesis can significantly improve the titer of SAM. This paper constructed a synthetic transcription factor (TF)-based biosensor for SAM detecting in Saccharomyces cerevisiae. The synthetic TF, named MetJ-hER-VP16, consists of an Escherichia coli-derived DNA-binding domain MetJ, GS linker, the human estrogen receptor binding domain hER, and the viral activation domain VP16. The synthetic biosensor is capable of sensing SAM in a dose-dependent manner with fluorescence as the output. Additionally, it is tightly regulated by the inducer SAM and β-estradiol, which means that the fluorescence output is only available when both are present together. The synthetic SAM biosensor could potentially be applied for high-throughput metabolic engineering and is expected to improve SAM production.

Base editor enables rational genome-scale functional screening for enhanced industrial phenotypes in Corynebacterium glutamicum

Genome-scale functional screening accelerates comprehensive assessment of gene function in cells. Here, we have established a genome-scale loss-of-function screening strategy that combined a cytosine base editor with approximately 12,000 parallel sgRNAs targeting 98.1% of total genes in Corynebacterium glutamicum ATCC 13032. Unlike previous data processing methods developed in yeast or mammalian cells, we developed a new data processing procedure to locate candidate genes by statistical sgRNA enrichment analysis. Known and novel functional genes related to 5-fluorouracil resistance, 5-fluoroorotate resistance, oxidative stress tolerance, or furfural tolerance have been identified. In particular, purU and serA were proven to be related to the furfural tolerance in C. glutamicum. A cloud platform named FSsgRNA-Analyzer was provided to accelerate sequencing data processing for CRISPR-based functional screening. Our method would be broadly useful to functional genomics study and strain engineering in other microorganisms.

Bacterial Resuscitation from Starvation-Induced Dormancy Results in Phenotypic Diversity Coupled with Translational Activity Depending on Carbon Substrate Availability

Dormancy is a survival strategy of stressed bacteria inhabiting a various environment. Frequent dormant-active transitions owing to environmental changes play an important role in functional redundancy. However, a proper understanding of the phenotypic changes in bacteria during these transitions remains to be clarified. In this study, orthogonal approaches, such as electron microscopy, flow cytometry, and Raman spectroscopy, which can evaluate phenotypic heterogeneity at the single-cell level, were used to observe morphological and molecular phenotypic changes in resuscitated cells, and RNA sequencing (RNASeq) was used to determine the genetic characteristics associated with phenotypes. Within 12 h of the resuscitation process, morphological (cell size and shape) and physiological (growth and viability) characteristics as well as molecular phenotypes (cellular components) were found to be recovered to the extent that they were similar to those in active cells. The recovery rate and detailed phenotypic properties of the resuscitated cells differed significantly depending on the type or concentration of carbon sources. RNASeq analysis revealed that genes related to translation were significantly upregulated under all resuscitation conditions. The simpler the carbon source (e.g., glucose), the higher the expression of genes involved in cellular repair, and the more complex the carbon source (e.g., beef extract), the higher the expression of genes associated with increased energy production associated with cellular aerobic respiration. This study of phenotypic plasticity of resuscitated cells provides fundamental insight into understanding the adaptive fine-tuning of the microbiome in response to environmental changes and the functional redundancy resulting from phenotype heterogeneity.