Essence of life: essential genes of minimal genomes

Essential genes are absolutely required for cell survival. Determination of the universal minimal set of genes needed to sustain life is, therefore, expected to contribute greatly to our understanding of life at its simplest level, with applications in medicine and synthetic biology. The search for the minimal genome has led to the identification of often variable gene sets. We argue here that, based on the outcome of these analyses, it is becoming increasingly evident that some genes, and the functions encoded by them, are absolutely necessary for the survival of any living entity, whereas others can be omitted. We also examine ways of determining the minimal genome and discuss possible practical applications of a minimal cell.     

A genetic replacement system for selection-based engineering of essential proteins

ABSTRACT: BACKGROUND: Essential genes represent the core of biological functions required for viability. Molecular understanding of essentiality as well as design of synthetic cellular systems includes the engineering of essential proteins. An impediment to this effort is the lack of growth-based selection systems suitable for directed evolution approaches. RESULTS: We established a simple strategy for genetic replacement of an essential gene by a (library of) variant(s) during a transformation. The system was validated using three different essential genes and plasmid combinations and it reproducibly shows transformation efficiencies on the order of 107 transformants per microgram of DNA without any identifiable false positives. This allowed for reliable recovery of functional variants out of at least a 105-fold excess of non-functional variants. This outperformed selection in conventional bleach-out strains by at least two orders of magnitude, where recombination between functional and non-functional variants interfered with reliable recovery even in recA negative strains. CONCLUSIONS: We propose that this selection system is extremely suitable for evaluating large libraries of engineered essential proteins resulting in the reliable isolation of functional variants in a clean strain background which can readily be used for in vivo applications as well as expression and purification for use in in vitro studies.     

Ancestry analysis indicates two different sets of essential genes in eukaryotic model species

Essential genes are so-called because they are crucial for organism perpetuation. Those genes are usually related to essential functions to cellular metabolism or multicellular homeostasis. Deleterious alterations on essential genes produce a spectrum of phenotypes in multicellular organisms. The effects range from the impairment of the fertilization process, disruption of fetal development, to loss of reproductive capacity. Essential genes are described as more evolutionarily conserved than non-essential genes. However, there is no consensus about the relationship between gene essentiality and gene age. Here, we identified essential genes in five model eukaryotic species (Saccharomyces cerevisiae, Schizosaccharomyces pombe, Drosophila melanogaster, Caenorhabditis elegans, and Mus musculus) and estimate their evolutionary ancestry and their network properties. We observed that essential genes, on average, are older than other genes in all species investigated. The relationship of network properties and gene essentiality convey with previous findings, showing essential genes as important nodes in biological networks. As expected, we also observed that essential orthologs shared by the five species evaluated here are old. However, all the species evaluated here have a specific set of young essential genes not shared among them. Additionally, these two groups of essential genes are involved with distinct biological functions, suggesting two sets of essential genes: (i) a set of old essential genes common to all the evaluated species, regulating basic cellular functions, and (ii) a set of young essential genes exclusive to each species, which perform specific essential functions in each species.

     

Comparative Analysis of Yeast Metabolic Network Models Highlights Progress,Opportunities for Metabolic Reconstruction

We have compared 12 genome-scale models of the Saccharomyces cerevisiae metabolic network published since 2003 to evaluate progress in reconstruction of the yeast metabolic network. We compared the genomic coverage, overlap of annotated metabolites, predictive ability for single gene essentiality with a selection of model parameters, and biomass production predictions in simulated nutrient-limited conditions. We have also compared pairwise gene knockout essentiality predictions for 10 of these models. We found that varying approaches to model scope and annotation reflected the involvement of multiple research groups in model development; that single-gene essentiality predictions were affected by simulated medium, objective function, and the reference list of essential genes; and that predictive ability for single-gene essentiality did not correlate well with predictive ability for our reference list of synthetic lethal gene interactions (R = 0.159). We conclude that the reconstruction of the yeast metabolic network is indeed gradually improving through the iterative process of model development, and there remains great opportunity for advancing our understanding of biology through continued efforts to reconstruct the full biochemical reaction network that constitutes yeast metabolism. Additionally, we suggest that there is opportunity for refining the process of deriving a metabolic model from a metabolic network reconstruction to facilitate mechanistic investigation and discovery. This comparative study lays the groundwork for developing improved tools and formalized methods to quantitatively assess metabolic network reconstructions independently of any particular model application, which will facilitate ongoing efforts to advance our understanding of the relationship between genotype and cellular phenotype.     

Modularity: a new perspective in biology

Several decades of research in biochemistry and molecular biology have been devoted for studies on isolated enzymes and proteins. Recent high throughput technologies in genomics and proteomics have resulted in avalanche of information about several genes, proteins and enzymes in variety of living systems. Though these efforts have greatly contributed to the detailed understanding of a large number of individual genes and proteins, this explosion of information has simultaneously brought out the limitations of reductionism in understanding complex biological processes. The genes or gene products do not function in isolation in vivo. A delicate and dynamic molecular architecture is required for precision of the chemical reactions associated with "life". In future, a paradigm shift is, therefore, envisaged, in biology leading to exploration of molecular organizations in physical and genomic context, a subtle transition from conventional molecular biology to modular biology. A module can be defined as an organization of macromolecules performing a synchronous function in a given metabolic pathway. In modular biology, the biological processes of interest are explored as complex systems of functionally interacting macromolecules. The present article describes the perceptions of the concept of modularity, in terms of associations among genes and proteins, presenting a link between reductionist approach and system biology.     

Essential genes on metabolic maps

Within the past five years genome-scale gene essentiality data sets have been published for ten diverse bacterial species. These data are a rich source of information about cellular networks that we are only beginning to explore. The analysis of these data, very heterogeneous in nature, is a challenging task. Even the definition of 'essential genes' in various genome-scale studies varies from genes 'absolutely required for survival' to those 'strongly contributing to fitness' and robust competitive growth. A comparative analysis of gene essentiality across multiple organisms based on projection of experimentally observed essential genes to functional roles in a collection of metabolic pathways and subsystems is emerging as a powerful tool of systems biology.     

A complete collection of single‐gene deletion mutants of Acinetobacter baylyi ADP1

We have constructed a collection of single‐gene deletion mutants for all dispensable genes of the soil bacterium Acinetobacter baylyi ADP1. A total of 2594 deletion mutants were obtained, whereas 499 (16%) were not, and are therefore candidate essential genes for life on minimal medium. This essentiality data set is 88% consistent with the Escherichia coli data set inferred from the Keio mutant collection profiled for growth on minimal medium, while 80% of the orthologous genes described as essential in Pseudomonas aeruginosa are also essential in ADP1. Several strategies were undertaken to investigate ADP1 metabolism by (1) searching for discrepancies between our essentiality data and current metabolic knowledge, (2) comparing this essentiality data set to those from other organisms, (3) systematic phenotyping of the mutant collection on a variety of carbon sources (quinate, 2‐3 butanediol, glucose, etc.). This collection provides a new resource for the study of gene function by forward and reverse genetic approaches and constitutes a robust experimental data source for systems biology approaches.     

Reconstruction and Validation of a Genome-Scale Metabolic Model for the Filamentous Fungus Neurospora crassa Using FARM

The filamentous fungus Neurospora crassa played a central role in the development of twentieth-century genetics, biochemistry and molecular biology, and continues to serve as a model organism for eukaryotic biology. Here, we have reconstructed a genome-scale model of its metabolism. This model consists of 836 metabolic genes, 257 pathways, 6 cellular compartments, and is supported by extensive manual curation of 491 literature citations. To aid our reconstruction, we developed three optimization-based algorithms, which together comprise Fast Automated Reconstruction of Metabolism (FARM). These algorithms are: LInear MEtabolite Dilution Flux Balance Analysis (limed-FBA), which predicts flux while linearly accounting for metabolite dilution; One-step functional Pruning (OnePrune), which removes blocked reactions with a single compact linear program; and Consistent Reproduction Of growth/no-growth Phenotype (CROP), which reconciles differences between in silico and experimental gene essentiality faster than previous approaches. Against an independent test set of more than 300 essential/non-essential genes that were not used to train the model, the model displays 93% sensitivity and specificity. We also used the model to simulate the biochemical genetics experiments originally performed on Neurospora by comprehensively predicting nutrient rescue of essential genes and synthetic lethal interactions, and we provide detailed pathway-based mechanistic explanations of our predictions. Our model provides a reliable computational framework for the integration and interpretation of ongoing experimental efforts in Neurospora, and we anticipate that our methods will substantially reduce the manual effort required to develop high-quality genome-scale metabolic models for other organisms.     

The Role of Protein Interactions in Mediating Essentiality and Synthetic Lethality

Genes are characterized as essential if their knockout is associated with a lethal phenotype, and these “essential genes” play a central role in biological function. In addition, some genes are only essential when deleted in pairs, a phenomenon known as synthetic lethality. Here we consider genes displaying synthetic lethality as “essential pairs” of genes, and analyze the properties of yeast essential genes and synthetic lethal pairs together. As gene duplication initially produces an identical pair or sets of genes, it is often invoked as an explanation for synthetic lethality. However, we find that duplication explains only a minority of cases of synthetic lethality. Similarly, disruption of metabolic pathways leads to relatively few examples of synthetic lethality. By contrast, the vast majority of synthetic lethal gene pairs code for proteins with related functions that share interaction partners. We also find that essential genes and synthetic lethal pairs cluster in the protein-protein interaction network. These results suggest that synthetic lethality is strongly dependent on the formation of protein-protein interactions. Compensation by duplicates does not usually occur mainly because the genes involved are recent duplicates, but is more commonly due to functional similarity that permits preservation of essential protein complexes. This unified view, combining genes that are individually essential with those that form essential pairs, suggests that essentiality is a feature of physical interactions between proteins protein-protein interactions, rather than being inherent in gene and protein products themselves.     

Building the blueprint of life

With recent breakthroughs in experimental microbiology making it possible to synthesize and implant an entire genome to create a living cell, the challenge of constructing a working blueprint for the first truly minimal synthetic organism is more important than ever. Here we review the significant progress made in the design and creation of a minimal organism. We discuss how comparative genomes, gene essentiality data, naturally small genomes, and metabolic modeling are all being applied to produce a catalogue of the biological functions essential for life. We compare the minimal gene sets from three published sources with functions identified in 13 existing gene essentiality datasets. We examine how genome-scale metabolic models have been applied to design a minimal metabolism for growth in simple and complex media. Additionally, we survey the progress of efforts to construct a minimal organism, either through implementation of combinatorial deletions in Bacillus subtilis and Escherichia coli or through the synthesis and implantation of synthetic genomes.     

Metabolic network analysis revealed distinct routes of deletion effects between essential and non-essential genes

Interest in essential genes has arisen recently given their importance in antimicrobial drug development. Although knockouts of essential genes are commonly known to cause lethal phenotypes, there is insufficient understanding on the intermediate changes followed by genetic perturbation and to what extent essential genes correlate to other genes. Here, we characterized the gene knockout effects by using a list of affected genes, termed as 'damage lists'. These damage lists were identified through a refined cascading failure approach that was based on a previous topological flux balance analysis. Using an Escherichia coli metabolic network, we incorporated essentiality information into damage lists and revealed that the knockout of an essential gene mainly affects a large range of other essential genes whereas knockout of a non-essential gene only interrupts other non-essential genes. Also, genes sharing common damage lists tend to have the same essentiality. We extracted 72 core functional modules from the common damage lists of essential genes and demonstrated their ability to halt essential metabolites production. Overall, our network analysis revealed that essential and non-essential genes propagated their deletion effects via distinct routes, conferring mechanistic explanation to the observed lethality phenotypes of essential genes.     

How to make a minimal genome for synthetic minimal cell

As a key focus of synthetic biology, building a minimal artificial cell has given rise to many discussions. A synthetic minimal cell will provide an appropriate chassis to integrate functional synthetic parts, devices and systems with functions that cannot generally be found in nature. The design and construction of a functional minimal genome is a key step while building such a cell/chassis since all the cell functions can be traced back to the genome. Kinds of approaches, based on bioinformatics and molecular biology, have been developed and proceeded to derive essential genes and minimal gene sets for the synthetic minimal genome. Experiments about streamlining genomes of model bacteria revealed genome reduction led to unanticipated beneficial properties, such as high electroporation efficiency and accurate propagation of recombinant genes and plasmids that were unstable in other strains. Recent achievements in chemical synthesis technology for large DNA segments together with the rapid development of the whole-genome sequencing, have transferred synthesis of genes to assembly of the whole genomes based on oligonucleotides, and thus created strong preconditions for synthesis of artificial minimal genome. Here in this article, we review briefly the history and current state of research in this field and summarize the main methods for making a minimal genome. We also discuss the impacts of minimized genome on metabolism and regulation of artificial cell.     

Advances in the chemistry of small molecule fluorescent probes

Small molecule fluorophores are essential tools for chemical biology. A benefit of synthetic dyes is the ability to employ chemical approaches to control the properties and direct the position of the fluorophore. Applying modern synthetic organic chemistry strategies enables efficient tailoring of the chemical structure to obtain probes for specific biological experiments. Chemistry can also be used to activate fluorophores; new fluorogenic enzyme substrates and photoactivatable compounds with improved properties have been prepared that facilitate advanced imaging experiments with low background fluorescence. Finally, chemical reactions in live cells can be used to direct the spatial distribution of the fluorophore, allowing labeling of defined cellular regions with synthetic dyes.     

Comprehensive mutational analysis of a herpesvirus gene in the viral genome context reveals a region essential for virus replication

Essential viral proteins perform vital functions during morphogenesis via a complex interaction with other viral and cellular gene products. Here, we present a novel approach to comprehensive mutagenesis of essential cytomegalovirus genes and biological analysis in the 230-kbp-genome context. A random Tn7-based mutagenesis procedure at the single-gene level was combined with site-specific recombination via the FLP/FLP recognition target site system for viral genome reconstitution. We show the function of more than 100 mutants from a larger library of M50/p35, a protein involved in capsid egress from the nucleus. This protein recruits other viral proteins and cellular enzymes to the inner nuclear membrane. Our approach enabled us to rapidly discriminate between essential and nonessential regions within the coding sequence. Based on the prediction of the screen, we were able to map a site essential for viral protein-protein interaction at the amino acid level.     

Gene essentiality determines chromosome organisation in bacteria

In Escherichia coli and Bacillus subtilis, essentiality, not expressivity, drives the distribution of genes between the two replicating strands. Although essential genes tend to be coded in the leading replicating strand, the underlying selective constraints and the evolutionary extent of these findings have still not been subject to comparative studies. Here, we extend our previous analysis to the genomes of low G + C firmicutes and γ-proteobacteria, and in a second step to all sequenced bacterial genomes. The inference of essentiality by homology allows us to show that essential genes are much more frequent in the leading strand than other genes, even when compared with non- essential highly expressed genes. Smaller biases were found in the genomes of obligatory intracellular bacteria, for which the assignment of essentiality by homology from fast growing free-living bacteria is most problematic. Cross-comparisons used to assess potential errors in the assignment of essentiality by homology revealed that, in most cases, variations in the assignment criteria have little influence on the overall results. Essential genes tend to be more conserved in the leading strand than average genes, which is consistent with selection for this positioning and may impose a strong constraint on chromosomal rearrangements. These results indicate that essentiality plays a fundamental role in the distribution of genes in most bacterial genomes.     

蛋白质预算：合成生物学的成本标尺

合成生物学以创建人工生命体系为目的.实践中人们希望人工生命体系具有更强的生产能力、转化能力、环境适应与监测能力,从而获得更优质的生产方式.生命体系的优化涉及到多层次的调控网络,而根本上还是对细胞中蛋白质的含量、定位、活性的控制.在蛋白质表达水平上进行控制是合成生物学元件设计、模块组装以及适配性研究最核心的手段.类似于工厂中的成本计算,合成生物学创建的人工生命体系(人工细胞工厂)以蛋白质预算为依据.优化蛋白质预算的研究策略已经成功应用于合成生物学研究实践中.     

LoxTnSeq: random transposon insertions combined with cre/lox recombination and counterselection to generate large random genome reductions

The removal of unwanted genetic material is a key aspect in many synthetic biology efforts and often requires preliminary knowledge of which genomic regions are dispensable. Typically, these efforts are guided by transposon mutagenesis studies, coupled to deepsequencing (TnSeq) to identify insertion points and gene essentiality. However, epistatic interactions can cause unforeseen changes in essentiality after the deletion of a gene, leading to the redundancy of these essentiality maps. Here, we present LoxTnSeq, a new methodology to generate and catalogue libraries of genome reduction mutants. LoxTnSeq combines random integration of lox sites by transposon mutagenesis, and the generation of mutants via Cre recombinase, catalogued via deep sequencing. When LoxTnSeq was applied to the naturally genome reduced bacterium Mycoplasma pneumoniae, we obtained a mutant pool containing 285 unique deletions. These deletions spanned from > 50 bp to 28 Kb, which represents 21% of the total genome. LoxTnSeq also highlighted large regions of non-essential genes that could be removed simultaneously, and other non-essential regions that could not, providing a guide for future genome reductions.     

Synthetic biology with RNA motifs

Structural motifs in naturally occurring RNAs and RNPs can be employed as new molecular parts for synthetic biology to facilitate the development of novel devices and systems that modulate cellular functions. In this review, we focus on the following: (i) experimental evolution techniques of RNA molecules in vitro and (ii) their applications for regulating gene expression systems in vivo. For experimental evolution, new artificial RNA aptamers and RNA enzymes (ribozymes) have been selected in vitro. These functional RNA molecules are likely to be applicable in the reprogramming of existing gene regulatory systems. Furthermore, they may be used for designing hypothetical RNA-based living systems in the so-called RNA world. For the regulation of gene expressions in living cells, the development of new riboswitches allows us to modulate the target gene expression in a tailor-made manner. Moreover, recently RNA-based synthetic genetic circuits have been reported by employing functional RNA molecules, expanding the repertory of synthetic biology with RNA motifs.