Stochasticity in protein levels drives colinearity of gene order in metabolic operons of Escherichia coli

Gene cluster from plant to microbes: Their role in genome architecture, organism's development, specialized metabolism and drug discovery

Plants and microbes fulfil our daily requirements through different high-value chemicals, e.g., nutraceuticals, pharmaceuticals, cosmetics, and through varieties of fruits, crops, vegetables, and many more. Utmost care would therefore be taken for growth, development and sustainability of these important crops and medicinal plants and microbes. Homeobox genes and HOX clusters and their recently characterized expanded family members, including newly discovered homeobox, WOX gene from medicinal herb, Panax ginseng, significantly contributes in the growth and development of these organisms. On the other hand, secondary metabolites produced through secondary metabolism of plants and microbes are used as organisms defense as well as drugs/drug-like molecules for humans. Both the developmental HOX cluster and the biosynthetic gene-cluster (BGC) for secondary metabolites are organised in organisms genome. Genome mining and genomewide analysis of these clusters will definitely identify and characterize many more important molecules from unexplored plants and microbes and underexplored human microbiota and the evolution studies of these clusters will indicate their source of origin. Although genomics revolution now continues at a pace, till date only few hundred plant genome sequences are available. However, next-generation sequencing (NGS) technology now in market and may be applied even for plants with recalcitrant genomes, eventually may discover genomic potential towards production of secondary metabolites of diverse plants and micro-organisms present in the environment and microbiota. Additionally, the development of tools for genome mining e.g., antiSMASH, plantiSMASH, and more and more computational approaches that predicts hundreds of secondary metabolite BGCs will be discussed.

Position preference of essential genes in prokaryotic operons

Essential genes, which form the basis of life activities, are crucial for the survival of organisms. Essential genes tend to be located in operons, but how they are distributed in operons is still unclear for most prokaryotes. In order to clarify the general rule of position preference of essential genes in operons, an index of the average position of genes in an operon was proposed, and the distributions of essential and non-essential genes in operons in 51 bacterial genomes and two archaeal genomes were analyzed based on this new index. Consequently, essential genes were found to preferentially occupy the front positions of the operons, which tend to be expressed at higher levels.

The Exploration of Novel Regulatory Relationships Drives Haloarchaeal Operon-Like Structural Dynamics over Short Evolutionary Distances

Operons are a dominant feature of bacterial and archaeal genome organization. Numerous investigations have related aspects of operon structure to operon function, making operons exemplars for studies aimed at deciphering Nature's design principles for genomic organization at a local scale. We consider this understanding to be both fundamentally important and ultimately useful in the de novo design of increasingly complex synthetic circuits. Here we analyze the evolution of the genomic context of operon-like structures in a set of 76 sequenced and annotated species of halophilic archaea. The phylogenetic depth and breadth of this dataset allows insight into changes in operon-like structures over shorter evolutionary time scales than have been studied in previous cross-species analysis of operon evolution. Our analysis, implemented in the updated software package JContextExplorer finds that operon-like context as measured by changes in structure frequently differs from a sequence divergence model of whole-species phylogeny and that changes seem to be dominated by the exploration of novel regulatory relationships.

Evolutionary dynamics of natural product biosynthesis in bacteria

Covering: 2008 up to 2019The forces of biochemical adaptive evolution operate at the level of genes, manifesting in complex phenotypes and the global biodiversity of proteins and metabolites. While evolutionary histories have been deciphered for some other complex traits, the origins of natural product biosynthesis largely remain a mystery. This fundamental knowledge gap is surprising given the many decades of research probing the genetic, chemical, and biophysical mechanisms of bacterial natural product biosynthesis. Recently, evolutionary thinking has begun to permeate this otherwise mechanistically dominated field. Natural products are now sometimes referred to as 'specialized' rather than 'secondary' metabolites, reinforcing the importance of their biological and ecological functions. Here, we review known evolutionary mechanisms underlying the overwhelming chemical diversity of bacterial secondary metabolism, focusing on enzyme promiscuity and the evolution of enzymatic domains that enable metabolic traits. We discuss the mechanisms that drive the assembly of natural product biosynthetic gene clusters and propose formal definitions for 'specialized' and 'secondary' metabolism. We further explore how biosynthetic gene clusters evolve to synthesize related molecular species, and in turn how the biological and ecological roles that emerge from metabolic diversity are acted on by selection. Finally, we reconcile chemical, functional, and genetic data into an evolutionary model, the dynamic chemical matrix evolutionary hypothesis, in which the relationships between chemical distance, biomolecular activity, and relative fitness shape adaptive landscapes.

Investigation on the Evolutionary Relation of Diverse Polyhydroxyalkanoate Gene Clusters in Betaproteobacteria

Products of numerous genes (phaC, phaA, phaB, phaP, phaR, and phaZ) are involved in the synthesis and degradation processes of the ubiquitous prokaryotic polyhydroxyalkanoate (PHA) intracellular reserve storage system. In this study, we performed a bioinformatics analysis to identify PHA-related genes and proteins in the genome of 66 selected organisms (class: Betaproteobacteria) that occur in various habitats; besides, evolutionary trajectories of the PHA system are reported here. The identified PHA-related genes were organized into clusters, and the gene arrangement was highly diverse. The occurrence and distribution of PHA-related clusters revealed that a single cluster was primarily segmented into small gene groups among various genomes, which were further reorganized as novel clusters based on various functional genes. The individual phylogenies of gene and protein sequences supported that the clusters were assembled through the relocation of native orthologous genes that underwent insertion, deletion, and elongation events. Furthermore, the neighboring genes provided valuable evolutionary and functional cues regarding the conservation and maintenance of PHA-related genes in the genome. Overall, the aforementioned results strongly indicate the influence of horizontal gene transfer on the organization of PHA-related gene clusters. Therefore, our results reveal new insights into the organization, evolutionary history, and cluster conservation of the PHA-related gene inventories among Betaproteobacterial organisms.

A simple comparison of the extrinsic noise in gene expression between native and foreign regulations in Escherichia coli

Living cells reorganize their gene expression through regulatory machineries in response to external perturbations. The contribution of the regulation to the noise in gene expression is of great interest. In this study, we evaluate the contribution of both native and foreign regulations to the extrinsic noise in gene expression. We analyzed the gene expression data of a mini-library containing 70 genetic constructs of 136 clones into which the gfp gene had been chromosomally incorporated under the control of either native or foreign regulation. We found that the substitution of native by foreign regulation, i.e., the insertion of the P_tet promoter, triggered a decrease in the extrinsic noise, which was independent of the protein abundance. The reanalyses of varied genomic data sets verified that the noisy gene expression mediated by native regulations is a common feature, regardless of the diversity in the genetic approaches used. Disturbing native regulations by a synthetic promoter reduced the extrinsic noise in gene expression in Escherichia coli. It indicated that the extrinsic noise in gene expression caused by the native regulation could be further repressed. These results suggest a tendency of released regulation leading to reduced noise and a linkage between noise and plasticity in the regulation of gene expression.

Cellular Growth Arrest and Persistence from Enzyme Saturation

Metabolic efficiency depends on the balance between supply and demand of metabolites, which is sensitive to environmental and physiological fluctuations, or noise, causing shortages or surpluses in the metabolic pipeline. How cells can reliably optimize biomass production in the presence of metabolic fluctuations is a fundamental question that has not been fully answered. Here we use mathematical models to predict that enzyme saturation creates distinct regimes of cellular growth, including a phase of growth arrest resulting from toxicity of the metabolic process. Noise can drive entry of single cells into growth arrest while a fast-growing majority sustains the population. We confirmed these predictions by measuring the growth dynamics of Escherichia coli utilizing lactose as a sole carbon source. The predicted heterogeneous growth emerged at high lactose concentrations, and was associated with cell death and production of antibiotic-tolerant persister cells. These results suggest how metabolic networks may balance costs and benefits, with important implications for drug tolerance.

In bacteria, changes in gene expression, with resulting changes in protein concentration, can drastically change how fast cells and cellular populations grow. This fact has big implications for how we treat infectious disease, which types of organisms make up our microbiomes, and what patterns of gene regulation have undergone evolutionary selection. Here, we show how, in principle, the expression level of a single enzyme can affect bacterial population growth by creating a threshold where cells grow optimally fast just below it, but rapidly reach a state of no growth just above it because metabolic byproducts build up and halt growth. The narrow margin between these two states makes entering either of them possible for the same bacterium because of intrinsic uncertainty, or "noise", in gene expression. The predicted result is a variety of growth rates in a single population of genetically identical cells, manifested as a mix of fast- and slow-growing cells. We created laboratory conditions that reproduce the effect in the model organism E. coli, and showed that there may be a benefit to having slower growing cells, because they can survive antibiotic exposure for longer.

Operon Gene Order Is Optimized for Ordered Protein Complex Assembly

The assembly of heteromeric protein complexes is an inherently stochastic process in which multiple genes are expressed separately into proteins, which must then somehow find each other within the cell. Here, we considered one of the ways by which prokaryotic organisms have attempted to maximize the efficiency of protein complex assembly: the organization of subunit-encoding genes into operons. Using structure-based assembly predictions, we show that operon gene order has been optimized to match the order in which protein subunits assemble. Exceptions to this are almost entirely highly expressed proteins for which assembly is less stochastic and for which precisely ordered translation offers less benefit. Overall, these results show that ordered protein complex assembly pathways are of significant biological importance and represent a major evolutionary constraint on operon gene organization.

•

Operon-encoded subunits tend to be encoded by neighboring genes and form large interfaces

•

Operon gene order is often optimized for the order of protein complex assembly

•

Exceptions are mostly highly expressed proteins for which assembly is less stochastic

Coevolution of the Organization and Structure of Prokaryotic Genomes

The cytoplasm of prokaryotes contains many molecular machines interacting directly with the chromosome. These vital interactions depend on the chromosome structure, as a molecule, and on the genome organization, as a unit of genetic information. Strong selection for the organization of the genetic elements implicated in these interactions drives replicon ploidy, gene distribution, operon conservation, and the formation of replication-associated traits. The genomes of prokaryotes are also very plastic with high rates of horizontal gene transfer and gene loss. The evolutionary conflicts between plasticity and organization lead to the formation of regions with high genetic diversity whose impact on chromosome structure is poorly understood. Prokaryotic genomes are remarkable documents of natural history because they carry the imprint of all of these selective and mutational forces. Their study allows a better understanding of molecular mechanisms, their impact on microbial evolution, and how they can be tinkered in synthetic biology.

DLGP: A database for lineage-conserved and lineage-specific gene pairs in animal and plant genomes

The conservation of gene organization in the genome with lineage-specificity is an invaluable resource to decipher their potential functionality with diverse selective constraints, especially in higher animals and plants. Gene pairs appear to be the minimal structure for such kind of gene clusters that tend to reside in their preferred locations, representing the distinctive genomic characteristics in single species or a given lineage. Despite gene families having been investigated in a widespread manner, the definition of gene pair families in various taxa still lacks adequate attention. To address this issue, we report DLGP (http://lcgbase.big.ac.cn/DLGP/) that stores the pre-calculated lineage-based gene pairs in currently available 134 animal and plant genomes and inspect them under the same analytical framework, bringing out a set of innovational features. First, the taxonomy or lineage has been classified into four levels such as Kingdom, Phylum, Class and Order. It adopts all-to-all comparison strategy to identify the possible conserved gene pairs in all species for each gene pair in certain species and reckon those that are conserved in over a significant proportion of species in a given lineage (e.g. Primates, Diptera or Poales) as the lineage-conserved gene pairs. Furthermore, it predicts the lineage-specific gene pairs by retaining the above-mentioned lineage-conserved gene pairs that are not conserved in any other lineages. Second, it carries out pairwise comparison for the gene pairs between two compared species and creates the table including all the conserved gene pairs and the image elucidating the conservation degree of gene pairs in chromosomal level. Third, it supplies gene order browser to extend gene pairs to gene clusters, allowing users to view the evolution dynamics in the gene context in an intuitive manner. This database will be able to facilitate the particular comparison between animals and plants, between vertebrates and arthropods, and between monocots and eudicots, accounting for the significant contribution of gene pairs to speciation and diversification in specific lineages.

Natural selection for operons depends on genome size

In prokaryotes, genome size is associated with metabolic versatility, regulatory complexity, effective population size, and horizontal transfer rates. We therefore analyzed the covariation of genome size and operon conservation to assess the evolutionary models of operon formation and maintenance. In agreement with previous results, intraoperonic pairs of essential and of highly expressed genes are more conserved. Interestingly, intraoperonic pairs of genes are also more conserved when they encode proteins at similar cell concentrations, suggesting a role of cotranscription in diminishing the cost of waste and shortfall in gene expression. Larger genomes have fewer and smaller operons that are also less conserved. Importantly, lower conservation in larger genomes was observed for all classes of operons in terms of gene expression, essentiality, and balanced protein concentration. We reached very similar conclusions in independent analyses of three major bacterial clades (α- and β-Proteobacteria and Firmicutes). Operon conservation is inversely correlated to the abundance of transcription factors in the genome when controlled for genome size. This suggests a negative association between the complexity of genetic networks and operon conservation. These results show that genome size and/or its proxies are key determinants of the intensity of natural selection for operon organization. Our data fit better the evolutionary models based on the advantage of coregulation than those based on genetic linkage or stochastic gene expression. We suggest that larger genomes with highly complex genetic networks and many transcription factors endure weaker selection for operons than smaller genomes with fewer alternative tools for genetic regulation.

Conserved patterns in bacterial genomes: a conundrum physically tailored by evolutionary tinkering

The proper functioning of bacteria is encoded in their genome at multiple levels or scales, each of which is constrained by specific physical forces. At the smallest spatial scales, interatomic forces dictate the folding and function of proteins and nucleic acids. On longer length scales, stochastic forces emerging from the thermal jiggling of proteins and RNAs impose strong constraints on the organization of genes along chromosomes, more particularly in the context of the building of nucleoprotein complexes and the operational mode of regulatory agents. At the cellular level, transcription, replication and cell division activities generate forces that act on both the internal structure and cellular location of chromosomes. The overall result is a complex multi-scale organization of genomes that reflects the evolutionary tinkering of bacteria. The goal of this review is to highlight avenues for deciphering this complexity by focusing on patterns that are conserved among evolutionarily distant bacteria. To this end, I discuss three different organizational scales: the protein structures, the chromosomal organization of genes and the global structure of chromosomes.

Dynamic optimization identifies optimal programmes for pathway regulation in prokaryotes

To survive in fluctuating environmental conditions, microorganisms must be able to quickly react to environmental challenges by upregulating the expression of genes encoding metabolic pathways. Here we show that protein abundance and protein synthesis capacity are key factors that determine the optimal strategy for the activation of a metabolic pathway. If protein abundance relative to protein synthesis capacity increases, the strategies shift from the simultaneous activation of all enzymes to the sequential activation of groups of enzymes and finally to a sequential activation of individual enzymes along the pathway. In the case of pathways with large differences in protein abundance, even more complex pathway activation strategies with a delayed activation of low abundance enzymes and an accelerated activation of high abundance enzymes are optimal. We confirm the existence of these pathway activation strategies as well as their dependence on our proposed constraints for a large number of metabolic pathways in several hundred prokaryotes.

Interplay of gene expression noise and ultrasensitive dynamics affects bacterial operon organization

Bacterial chromosomes are organized into polycistronic cotranscribed operons, but the evolutionary pressures maintaining them are unclear. We hypothesized that operons alter gene expression noise characteristics, resulting in selection for or against maintaining operons depending on network architecture. Mathematical models for 6 functional classes of network modules showed that three classes exhibited decreased noise and 3 exhibited increased noise with same-operon cotranscription of interacting proteins. Noise reduction was often associated with a decreased chance of reaching an ultrasensitive threshold. Stochastic simulations of the lac operon demonstrated that the predicted effects of transcriptional coupling hold for a complex network module. We employed bioinformatic analysis to find overrepresentation of noise-minimizing operon organization compared with randomized controls. Among constitutively expressed physically interacting protein pairs, higher coupling frequencies appeared at lower expression levels, where noise effects are expected to be dominant. Our results thereby suggest an important role for gene expression noise, in many cases interacting with an ultrasensitive switch, in maintaining or selecting for operons in bacterial chromosomes.

In some species, most notably bacteria, chromosomal genes are arranged into clusters called operons. In operons, the process of transcription is physically coupled: a single pass of the RNA polymerase enzyme reading that region of the chromosome simultaneously produces messenger RNA encoding multiple proteins. So far, we do not have a satisfying explanation for what evolutionary forces have maintained operons on bacterial chromosomes. We hypothesized that different types of interactions between operon-coded proteins affect how strongly operons are selected for between two genes. The proposed mechanism for this effect is that operons correlate gene expression noise, changing how it manifests in the post-translational network depending on the type of protein interaction. Mathematical models demonstrate that operons reduce noise for some types of interactions but not others. We found that operon-dependent noise reduction has an underlying dependence on surprisingly high sensitivity of the network to the ratio of proteins from each gene. Databases of genetic information show that E. coli has operons more frequently than random if operons reduce noise for the type of interaction various gene pairs have, but not otherwise. Our study thus provides an example of how the architecture of post-translational networks affects bacterial evolution.

Refactoring the nitrogen fixation gene cluster from Klebsiella oxytoca

Bacterial genes associated with a single trait are often grouped in a contiguous unit of the genome known as a gene cluster. It is difficult to genetically manipulate many gene clusters because of complex, redundant, and integrated host regulation. We have developed a systematic approach to completely specify the genetics of a gene cluster by rebuilding it from the bottom up using only synthetic, well-characterized parts. This process removes all native regulation, including that which is undiscovered. First, all noncoding DNA, regulatory proteins, and nonessential genes are removed. The codons of essential genes are changed to create a DNA sequence as divergent as possible from the wild-type (WT) gene. Recoded genes are computationally scanned to eliminate internal regulation. They are organized into operons and placed under the control of synthetic parts (promoters, ribosome binding sites, and terminators) that are functionally separated by spacer parts. Finally, a controller consisting of genetic sensors and circuits regulates the conditions and dynamics of gene expression. We applied this approach to an agriculturally relevant gene cluster from Klebsiella oxytoca encoding the nitrogen fixation pathway for converting atmospheric N(2) to ammonia. The native gene cluster consists of 20 genes in seven operons and is encoded in 23.5 kb of DNA. We constructed a "refactored" gene cluster that shares little DNA sequence identity with WT and for which the function of every genetic part is defined. This work demonstrates the potential for synthetic biology tools to rewrite the genetics encoding complex biological functions to facilitate access, engineering, and transferability.

A genome-wide analysis of promoter-mediated phenotypic noise in Escherichia coli

Gene expression is subject to random perturbations that lead to fluctuations in the rate of protein production. As a consequence, for any given protein, genetically identical organisms living in a constant environment will contain different amounts of that particular protein, resulting in different phenotypes. This phenomenon is known as “phenotypic noise.” In bacterial systems, previous studies have shown that, for specific genes, both transcriptional and translational processes affect phenotypic noise. Here, we focus on how the promoter regions of genes affect noise and ask whether levels of promoter-mediated noise are correlated with genes' functional attributes, using data for over 60% of all promoters in Escherichia coli. We find that essential genes and genes with a high degree of evolutionary conservation have promoters that confer low levels of noise. We also find that the level of noise cannot be attributed to the evolutionary time that different genes have spent in the genome of E. coli. In contrast to previous results in eukaryotes, we find no association between promoter-mediated noise and gene expression plasticity. These results are consistent with the hypothesis that, in bacteria, natural selection can act to reduce gene expression noise and that some of this noise is controlled through the sequence of the promoter region alone.

Many biological processes in a cell involve small numbers of molecules and therefore fluctuate over time. As a consequence, genetically identical cells that live in the same environment differ from each other in many phenotypic traits, including the expression level of different genes. Our aim was to identify types of genes with particularly low or high levels of variation (“noise”) and to understand molecular and evolutionary factors that determine noise level. Working with the bacterium E. coli, we analyzed the expression—at the single cell level—of more than 1,500 different genes. We found particularly low levels of noise in genes that E. coli needs to live and genes that this bacterium shares with many related taxa. This suggests that cellular functions that are particularly important for this organism evolved towards low levels of variation. In contrast to previous results with yeast, we find that genes that change their expression levels in response to environmental signals do not have high levels of noise. This suggests that there may be fundamental differences in how noise is controlled in bacteria and eukaryotes.

Validation of the essential ClpP protease in Mycobacterium tuberculosis as a novel drug target

Mycobacterium tuberculosis is a pathogen of major global importance. Validated drug targets are required in order to develop novel therapeutics for drug-resistant strains and to shorten therapy. The Clp protease complexes provide a means for quality control of cellular proteins; the proteolytic activity of ClpP in concert with the ATPase activity of the ClpX/ClpC subunits results in degradation of misfolded or damaged proteins. Thus, the Clp system plays a major role in basic metabolism, as well as in stress responses and pathogenic mechanisms. M. tuberculosis has two ClpP proteolytic subunits. Here we demonstrate that ClpP1 is essential for viability in this organism in culture, since the gene could only be deleted from the chromosome when a second functional copy was provided. Overexpression of clpP1 had no effect on growth in aerobic culture or viability under anaerobic conditions or during nutrient starvation. In contrast, clpP2 overexpression was toxic, suggesting different roles for the two homologs. We synthesized known activators of ClpP protease activity; these acyldepsipeptides (ADEPs) were active against M. tuberculosis. ADEP activity was enhanced by the addition of efflux pump inhibitors, demonstrating that ADEPs gain access to the cell but that export occurs. Taken together, the genetic and chemical validation of ClpP as a drug target leads to new avenues for drug discovery.

Error prevention and mitigation as forces in the evolution of genes and genomes

Why are short introns rarely a multiple of three nucleotides long? Why do essential genes cluster? Why are genes in operons often lined up in the order in which they are needed in the encoded pathway? In this Opinion article, we argue that these and many other - ostensibly disparate - observations are all pieces of an emerging picture in which multiple aspects of gene anatomy and genome architecture have evolved in response to error-prone gene expression.

Integrated analysis of the gene neighbouring impact on bacterial metabolic networks

Different levels of abstraction are needed to represent a living system. Unfortunately information of different nature is not superposable in an obvious way, but requires a dedicated framework. Because biological abstractions, i.e., genomic or metabolic information, can be easily represented as graphs, it is intuitive to integrate them into a unique graph, in which one can perform graph analysis for investigating a given biological assumption. This study follows such a philosophy and completes a genome and metabolome combination. In a such integrated framework and as illustration, we applied a graph analysis that automatically investigates impacts of the gene adjacency to predict functional relationships between genes and reactions. Our approach, called SIPPER, creates a weighted graph, in which the weights rely on the given relationship between genes, and computes (alternative) chains of reactions catalysed by genes. This method, as a generalisation of methods already published, can be easily adapted to several biological assumptions, properties or measures. This paper evaluates SIPPER on Escherichia coli. We automatically extract subgraphs, called k-SIPs, and quantify their interest in both genomic and metabolic contexts by showing functional compounds like operons or functional modules.

Fundamental relationship between operon organization and gene expression

Half a century has passed since the discovery of operons (groups of genes that are transcribed together as a single mRNA). Despite the importance of operons in bacterial gene networks, the relationship between their organization and gene expression remains poorly understood. Here we show using synthetic operons in Escherichia coli that the expression of a given gene increases with the length of the operon and as its position moves farther from the end of the operon. These findings can be explained by a common mechanism; increasing the distance from the start of a gene to the end of the operon (termed the "transcription distance") provides more time for translation to occur during transcription, resulting in increased expression. We confirmed experimentally that the increased expression is indeed due to increased translation. Furthermore our analysis indicates the translation initiation rate for an mRNA is sixfold greater during transcription than after its release, which amplifies the impact of the transcription distance on gene expression. As a result of these mechanisms, gene expression increases by ∼40% for each 1,000 nucleotides of transcription distance. In summary, we demonstrate that a fundamental relationship exists between gene expression and the number, length, and order of the genes in an operon. This relationship has important implications for understanding the functional basis of genome organization and practical applications for synthetic biology.

Impact of gene expression noise on organismal fitness and the efficacy of natural selection

Gene expression noise is a universal phenomenon across all life forms. Although beneficial under certain circumstances, expression noise is generally thought to be deleterious. However, neither the magnitude of the deleterious effect nor the primary mechanism of this effect is known. Here, we model the impact of expression noise on the fitness of unicellular organisms by considering the influence of suboptimal expressions of enzymes on the rate of biomass production and the energetic cost associated with imprecise amounts of protein synthesis. Our theoretical modeling and empirical analysis of yeast data show four findings. (i) Expression noise reduces the mean fitness of a cell by at least 25%, and this reduction cannot be substantially alleviated by gene overexpression. (ii) Higher sensitivity of fitness to the expression fluctuations of essential genes than nonessential genes creates stronger selection against noise in essential genes, resulting in a decrease in their noise. (iii) Reduction of expression noise by genome doubling offers a substantial fitness advantage to diploids over haploids, even in the absence of sex. (iv) Expression noise generates fitness variation among isogenic cells, which lowers the efficacy of natural selection similar to the effect of population shrinkage. Thus, expression noise renders organisms both less adapted and less adaptable. Because expression noise is only one of many manifestations of the stochasticity in cellular molecular processes, our results suggest a much more fundamental role of molecular stochasticity in evolution than is currently appreciated.

From hormones to secondary metabolism: the emergence of metabolic gene clusters in plants

Gene clusters for the synthesis of secondary metabolites are a common feature of microbial genomes. Well-known examples include clusters for the synthesis of antibiotics in actinomycetes, and also for the synthesis of antibiotics and toxins in filamentous fungi. Until recently it was thought that genes for plant metabolic pathways were not clustered, and this is certainly true in many cases; however, five plant secondary metabolic gene clusters have now been discovered, all of them implicated in synthesis of defence compounds. An obvious assumption might be that these eukaryotic gene clusters have arisen by horizontal gene transfer from microbes, but there is compelling evidence to indicate that this is not the case. This raises intriguing questions about how widespread such clusters are, what the significance of clustering is, why genes for some metabolic pathways are clustered and those for others are not, and how these clusters form. In answering these questions we may hope to learn more about mechanisms of genome plasticity and adaptive evolution in plants. It is noteworthy that for the five plant secondary metabolic gene clusters reported so far, the enzymes for the first committed steps all appear to have been recruited directly or indirectly from primary metabolic pathways involved in hormone synthesis. This may or may not turn out to be a common feature of plant secondary metabolic gene clusters as new clusters emerge.

Transcriptional coupling of neighboring genes and gene expression noise: evidence that gene orientation and noncoding transcripts are modulators of noise

How is noise in gene expression modulated? Do mechanisms of noise control impact genome organization? In yeast, the expression of one gene can affect that of a very close neighbor. As the effect is highly regionalized, we hypothesize that genes in different orientations will have differing degrees of coupled expression and, in turn, different noise levels. Divergently organized gene pairs, in particular those with bidirectional promoters, have close promoters, maximizing the likelihood that expression of one gene affects the neighbor. With more distant promoters, the same is less likely to hold for gene pairs in nondivergent orientation. Stochastic models suggest that coupled chromatin dynamics will typically result in low abundance-corrected noise (ACN). Transcription of noncoding RNA (ncRNA) from a bidirectional promoter, we thus hypothesize to be a noise-reduction, expression-priming, mechanism. The hypothesis correctly predicts that protein-coding genes with a bidirectional promoter, including those with a ncRNA partner, have lower ACN than other genes and divergent gene pairs uniquely have correlated ACN. Moreover, as predicted, ACN increases with the distance between promoters. The model also correctly predicts ncRNA transcripts to be often divergently transcribed from genes that a priori would be under selection for low noise (essential genes, protein complex genes) and that the latter genes should commonly reside in divergent orientation. Likewise, that genes with bidirectional promoters are rare subtelomerically, cluster together, and are enriched in essential gene clusters is expected and observed. We conclude that gene orientation and transcription of ncRNAs are candidate modulators of noise.

The pseudogenes of Mycobacterium leprae reveal the functional relevance of gene order within operons

Almost 50 years following the discovery of the prokaryotic operon, the functional relevance of gene order within operons remains unclear. In this work, we take advantage of the eroded genome of Mycobacterium leprae to add evidence supporting the notion that functionally less important genes have a tendency to be located at the end of its operons. M. leprae’s genome includes 1133 pseudogenes and 1614 protein-coding genes and can be compared with the close genome of M. tuberculosis. Assuming M. leprae’s pseudogenes to represent dispensable genes, we have studied the position of these pseudogenes in the operons of M. leprae and of their orthologs in M. tuberculosis. We observed that both tend to be located in the 3′ (downstream) half of the operon (P-values of 0.03 and 0.18, respectively). Analysis of pseudogenes in all available prokaryotic genomes confirms this trend (P-value of 7.1 × 10⁻⁷). In a complementary analysis, we found a significant tendency for essential genes to be located at the 5′ (upstream) half of the operon (P-value of 0.006). Our work provides an indication that, in prokarya, functionally less important genes have a tendency to be located at the end of operons, while more relevant genes tend to be located toward operon starts.

Selection upon genome architecture: conservation of functional neighborhoods with changing genes

An increasing number of evidences show that genes are not distributed randomly across eukaryotic chromosomes, but rather in functional neighborhoods. Nevertheless, the driving force that originated and maintains such neighborhoods is still a matter of controversy. We present the first detailed multispecies cartography of genome regions enriched in genes with related functions and study the evolutionary implications of such clustering. Our results indicate that the chromosomes of higher eukaryotic genomes contain up to 12% of genes arranged in functional neighborhoods, with a high level of gene co-expression, which are consistently distributed in phylogenies. Unexpectedly, neighborhoods with homologous functions are formed by different (non-orthologous) genes in different species. Actually, instead of being conserved, functional neighborhoods present a higher degree of synteny breaks than the genome average. This scenario is compatible with the existence of selective pressures optimizing the coordinated transcription of blocks of functionally related genes. If these neighborhoods were broken by chromosomal rearrangements, selection would favor further rearrangements reconstructing other neighborhoods of similar function. The picture arising from this study is a dynamic genomic landscape with a high level of functional organization.

We describe here the most extensive functional cartography of the genomes of multiple species carried out to date. Our study shows, for the first time, how neighborhoods of functionally related genes arise and how they are maintained through evolution following a pattern that is fully consistent with the evolutionary trees of the analyzed species. Contrary to what would be expected, such neighborhoods are not composed of the same genes in different species but rather by genes unrelated, annotated, however, with the same function. Our analysis also reveals that such neighborhoods are dynamically rebuilt in a way that, while the particular genes often change, it is the function of the genes present in the neighborhood, as the ultimate target of selection, that is preserved.

Evolution and multiplicity of arginine decarboxylases in polyamine biosynthesis and essential role in Bacillus subtilis biofilm formation

Arginine decarboxylases (ADCs; EC 4.1.1.19) from four different protein fold families are important for polyamine biosynthesis in bacteria, archaea, and plants. Biosynthetic alanine racemase fold (AR-fold) ADC is widespread in bacteria and plants. We report the discovery and characterization of an ancestral form of the AR-fold ADC in the bacterial Chloroflexi and Bacteroidetes phyla. The ancestral AR-fold ADC lacks a large insertion found in Escherichia coli and plant AR-fold ADC and is more similar to the lysine biosynthetic enzyme meso-diaminopimelate decarboxylase, from which it has evolved. An E. coli acid-inducible ADC belonging to the aspartate aminotransferase fold (AAT-fold) is involved in acid resistance but not polyamine biosynthesis. We report here that the acid-inducible AAT-fold ADC has evolved from a shorter, ancestral biosynthetic AAT-fold ADC by fusion of a response regulator receiver domain protein to the N terminus. Ancestral biosynthetic AAT-fold ADC appears to be limited to firmicute bacteria. The phylogenetic distribution of different forms of ADC distinguishes bacteria from archaea, euryarchaeota from crenarchaeota, double-membraned from single-membraned bacteria, and firmicutes from actinobacteria. Our findings extend to eight the different enzyme forms carrying out the activity described by EC 4.1.1.19. ADC gene clustering reveals that polyamine biosynthesis employs diverse and exchangeable synthetic modules. We show that in Bacillus subtilis, ADC and polyamines are essential for biofilm formation, and this appears to be an ancient, evolutionarily conserved function of polyamines in bacteria. Also of relevance to human health, we found that arginine decarboxylation is the dominant pathway for polyamine biosynthesis in human gut microbiota.

Conditions for the evolution of gene clusters in bacterial genomes

Genes encoding proteins in a common pathway are often found near each other along bacterial chromosomes. Several explanations have been proposed to account for the evolution of these structures. For instance, natural selection may directly favour gene clusters through a variety of mechanisms, such as increased efficiency of coregulation. An alternative and controversial hypothesis is the selfish operon model, which asserts that clustered arrangements of genes are more easily transferred to other species, thus improving the prospects for survival of the cluster. According to another hypothesis (the persistence model), genes that are in close proximity are less likely to be disrupted by deletions. Here we develop computational models to study the conditions under which gene clusters can evolve and persist. First, we examine the selfish operon model by re-implementing the simulation and running it under a wide range of conditions. Second, we introduce and study a Moran process in which there is natural selection for gene clustering and rearrangement occurs by genome inversion events. Finally, we develop and study a model that includes selection and inversion, which tracks the occurrence and fixation of rearrangements. Surprisingly, gene clusters fail to evolve under a wide range of conditions. Factors that promote the evolution of gene clusters include a low number of genes in the pathway, a high population size, and in the case of the selfish operon model, a high horizontal transfer rate. The computational analysis here has shown that the evolution of gene clusters can occur under both direct and indirect selection as long as certain conditions hold. Under these conditions the selfish operon model is still viable as an explanation for the evolution of gene clusters.

Genes involved in a common pathway or function are frequently found near each other on bacterial chromosomes. A number of hypotheses have been previously presented to explain this observation. A particularly influential theory is the selfish operon model, which posits that horizontal transfer could promote gene clustering by favouring transfer of arrangements of genes that are close together. Subsequent theoretical development and analysis of genomic data have contributed to the debate about the plausibility of this model. Here, by re-examining the evolutionary dynamics of gene clusters, we provide and discuss conditions under which gene clusters can evolve. We find that first, some form of bias for clustering is required for clusters to evolve. This bias can be in the form of bias in horizontal transfer towards genes that are close together, or direct natural selection for gene proximity. Our computational work does not present a theoretical obstacle to the selfish operon model as a possible explanation for the evolution of gene clusters.