Toolbox model of evolution of prokaryotic metabolic networks and their regulation

Scaling of Protein Function across the Tree of Life

Scaling laws are a powerful way to compare genomes because they put all organisms onto a single curve and reveal nontrivial generalities as genomes change in size. The abundance of functional categories across genomes has previously been found to show power law scaling with respect to the total number of functional categories, suggesting that universal constraints shape genomic category abundance. Here, we look across the tree of life to understand how genome evolution may be related to functional scaling. We revisit previous observations of functional genome scaling with an expanded taxonomy by analyzing 3,726 bacterial, 220 archaeal, and 79 unicellular eukaryotic genomes. We find that for some functional classes, scaling is best described by multiple exponents, revealing previously unobserved shifts in scaling as genome-encoded protein annotations increase or decrease. Furthermore, we find that scaling varies between phyletic groups at both the domain and phyla levels and is less universal than previously thought. This variability in functional scaling is not related to taxonomic phylogeny resolved at the phyla level, suggesting that differences in cell plan or physiology outweigh broad patterns of taxonomic evolution. Since genomes are maintained and replicated by the functional proteins encoded by them, these results point to functional degeneracy between taxonomic groups and unique evolutionary trajectories toward these. We also find that individual phyla frequently span scaling exponents of functional classes, revealing that individual clades can move across scaling exponents. Together, our results reveal unique shifts in functions across the tree of life and highlight that as genomes grow or shrink, proteins of various functions may be added or lost.

The ensemble of gene regulatory networks at mutation-selection balance

The evolution of diverse phenotypes both involves and is constrained by molecular interaction networks. When these networks influence patterns of expression, we refer to them as gene regulatory networks (GRNs). Here, we develop a model of GRN evolution analogous to work from quasi-species theory, which is itself essentially the mutation-selection balance model from classical population genetics extended to multiple loci. With this GRN model, we prove that-across a broad spectrum of selection pressures-the dynamics converge to a stationary distribution over GRNs. Next, we show from first principles how the frequency of GRNs at equilibrium is related to the topology of the genotype network, in particular, via a specific network centrality measure termed the eigenvector centrality. Finally, we determine the structural characteristics of GRNs that are favoured in response to a range of selective environments and mutational constraints. Our work connects GRN evolution to quasi-species theory-and thus to classical populations genetics-providing a mechanistic explanation for the observed distribution of GRNs evolving in response to various evolutionary forces, and shows how complex fitness landscapes can emerge from simple evolutionary rules.

Exploration of the presence and abundance of multidrug resistance efflux genes in oil and gas environments

As sequencing technology improves and the cost of metagenome sequencing decreases, the number of sequenced environments increases. These metagenomes provide a wealth of data in the form of annotated and unannotated genes. The role of multidrug resistance efflux pumps (MDREPs) is the removal of antibiotics, biocides and toxic metabolites created during aromatic hydrocarbon metabolism. Due to their naturally occurring role in hydrocarbon metabolism and their role in biocide tolerance, MDREP genes are of particular importance for the protection of pipeline assets. However, the heterogeneity of MDREP genes creates a challenge during annotation and detection. Here we use a selection of primers designed to target MDREPs in six pure species and apply them to publicly available metagenomes associated with oil and gas environments. Using in silico PCR with relaxed primer binding conditions we probed the metagenomes of a shale reservoir, a heavy oil tailings pond, a civil wastewater treatment, two marine sediments exposed to hydrocarbons following the Deepwater Horizon oil spill and a non-exposed marine sediment to assess the presence and abundance of MDREP genes. Through relaxed primer binding conditions during in silico PCR, the prevalence of MDREPs was determined. The percentage of nucleotide sequences identified by the MDREP primers was partially augmented by exposure to hydrocarbons in marine sediment and in shale reservoir compared to hydrocarbon-free marine sediments while tailings ponds and wastewater had the highest percentages. We believe this approach lays the groundwork for a supervised method of identifying poorly conserved genes within metagenomes.

Horizontal gene transfer drives the evolution of dependencies in bacteria

Many naturally occurring bacteria lead a lifestyle of metabolic dependency for crucial resources. We do not understand what factors drive bacteria toward this lifestyle and how. Here, we systematically show the crucial role of horizontal gene transfer (HGT) in dependency evolution in bacteria. Across 835 bacterial species, we map gene gain-loss dynamics on a deep evolutionary tree and assess the impact of HGT and gene loss on metabolic networks. Our analyses suggest that HGT-enabled gene gains can affect which genes are later lost. HGT typically adds new catabolic routes to bacterial metabolic networks, leading to new metabolic interactions between bacteria. We also find that gaining new routes can promote the loss of ancestral routes (”coupled gains and losses”, CGLs). Phylogenetic patterns indicate that both dependencies—mediated by CGLs and those purely by gene loss—are equally likely. Our results highlight HGT as an important driver of metabolic dependency evolution in bacteria.

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Metabolic dependencies are widespread across bacterial genomes

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New genes expand bacterial catabolism via the process of horizontal gene transfer

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During evolution, efficient pathways are gained, whereas redundant pathways are lost

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Gained pathways often depend on the metabolic byproducts of the surrounding community

Genome-wide lone strand adenine methylation in Deinococcus radiodurans R1: Regulation of gene expression through DR0643-dependent adenine methylation

DNA methylation is a covalent modification of adenine or cytosine in the genome of an organism and is found in diverse microbes including the radiation resistant bacterium Deinococcus radiodurans R1. Although earlier findings have confirmed repression or de-repression of certain genes in adenine methyltransferase (DR_0643/Dam1_DR) deficient D. radiodurans mutant however, the overall regulatory aspects of Dam1_DR-mediated adenine methylation remain mostly unexplored. In the present study, we compared the genome-wide methylome and the corresponding transcriptome of D. radiodurans WT and Δdam1 mutant to explore the correlation between methylation and gene expression. In D. radiodurans, deletion of DR_0643 ORF (Δdam1) led to hypomethylation of 512 genes resulting in differential expression of 168 genes (99 genes are upregulated and 69 genes are downregulated). The modification patterns deduced for Dam1_DR (DR_0643) and Dam2_DR (DR_2267) were non-palindromic and atypical. Moreover, we observed methylation at opportunistic sites that show adenine methylation only in D. radiodurans Δdam1 and not in D. radiodurans WT. Correlation between the methylome and transcriptome suggests that hypomethylation at Dam1_DR specific sites had both negative as well as a positive effects on gene expression. Pathways such as amino acid metabolism, transport, oxidative phosphorylation, quorum sensing, signal transduction, two-component system, glycolysis/gluconeogenesis, TCA cycle, glyoxylate and dicarboxylate metabolism were modulated by Dam1_DR-mediated adenine methylation in D. radiodurans. Processes such as DNA repair, recombination, ATPase and transmembrane transporter activity were enriched when Dam1_DR mutant was subjected to radiation stress. We further evaluated the molecular interactions and mode of binding between Dam1_DR protein and S-adenosyl methionine using molecular docking followed by MD simulation. To get a better insight into the methylation mechanism, the Dam1_DR-SAM complex was also docked with a DNA molecule to elucidate DNA-Dam1_DR structural interaction during methyl-group transfer reaction. In summary, our work presents comprehensive and integrative approaches to investigate both functional and structural aspects of DNA adenine methyltransferase (Dam1_DR) in D. radiodurans biology.

Unique niche-specific adaptation of fructophilic lactic acid bacteria and proposal of three Apilactobacillus species as novel members of the group

BACKGROUND

Fructophilic lactic acid bacteria (FLAB) found in D-fructose rich niches prefer D-fructose over D-glucose as a growth substrate. They need electron acceptors for growth on D-glucose. The organisms share carbohydrate metabolic properties. Fructobacillus spp., Apilactobacillus kunkeei, and Apilactobacillus apinorum are members of this unique group. Here we studied the fructophilic characteristics of recently described species Apilactobacillus micheneri, Apilactobacillus quenuiae, and Apilactobacillus timberlakei.

RESULTS

The three species prefer D-fructose over D-glucose and only metabolize D-glucose in the presence of electron acceptors. The genomic characteristics of the three species, i.e. small genomes and thus a low number of coding DNA sequences, few genes involved in carbohydrate transport and metabolism, and partial deletion of adhE gene, are characteristic of FLAB. The three species thus are novel members of FLAB. Reduction of genes involved in carbohydrate transport and metabolism in accordance with reduction of genome size were the common characteristics of the family Lactobacillaceae, but FLAB markedly reduced the gene numbers more than other species in the family. Pan-genome analysis of genes involved in metabolism displayed a lack of specific carbohydrate metabolic pathways in FLAB, leading to a unique cluster separation.

CONCLUSIONS

The present study expanded FLAB group. Fructose-rich environments have induced similar evolution in phylogenetically distant FLAB species. These are examples of convergent evolution of LAB.

A model for the interplay between plastic tradeoffs and evolution in changing environments

Performance tradeoffs are ubiquitous in both ecological and evolutionary modeling, yet they are usually postulated and built into fitness and ecological landscapes. However, tradeoffs depend on genetic background and evolutionary history and can themselves evolve. We present a simple model capable of capturing the key feedback loop: evolutionary history shapes tradeoff strength, which, in turn, shapes evolutionary future. One consequence of this feedback is that genomes with identical fitness can have different evolutionary properties shaped by prior environmental exposure. Another is that, generically, the best adaptations to one environment may evolve in another. Our simple framework bridges the gap between the phenotypic Fisher's Geometric Model and the genotypic properties, such as modularity and evolvability, and can serve as a rich playground for investigating evolution in multiple or changing environments.

Genomic adaptations in information processing underpin trophic strategy in a whole-ecosystem nutrient enrichment experiment

Several universal genomic traits affect trade-offs in the capacity, cost, and efficiency of the biochemical information processing that underpins metabolism and reproduction. We analyzed the role of these traits in mediating the responses of a planktonic microbial community to nutrient enrichment in an oligotrophic, phosphorus-deficient pond in Cuatro Ciénegas, Mexico. This is one of the first whole-ecosystem experiments to involve replicated metagenomic assessment. Mean bacterial genome size, GC content, total number of tRNA genes, total number of rRNA genes, and codon usage bias in ribosomal protein sequences were all higher in the fertilized treatment, as predicted on the basis of the assumption that oligotrophy favors lower information-processing costs whereas copiotrophy favors higher processing rates. Contrasting changes in trait variances also suggested differences between traits in mediating assembly under copiotrophic versus oligotrophic conditions. Trade-offs in information-processing traits are apparently sufficiently pronounced to play a role in community assembly because the major components of metabolism-information, energy, and nutrient requirements-are fine-tuned to an organism's growth and trophic strategy.

A Case of Adaptive Laboratory Evolution (ALE): Biodegradation of Furfural by Pseudomonas pseudoalcaligenes CECT 5344

Pseudomonas pseudoalcaligenes CECT 5344 is a bacterium able to assimilate cyanide as a nitrogen source at alkaline pH. Genome sequencing of this strain allowed the detection of genes related to the utilization of furfurals as a carbon and energy source. Furfural and 5-(hydroxymethyl) furfural (HMF) are byproducts of sugars production during the hydrolysis of lignocellulosic biomass. Since they inhibit the yeast fermentation to obtain bioethanol from sugars, the biodegradation of these compounds has attracted certain scientific interest. P. pseudoalcaligenes was able to use furfuryl alcohol, furfural and furoic acid as carbon sources, but after a lag period of several days. Once adapted, the evolved strain (R1D) did not show any more prolonged lag phases. The transcriptomic analysis (RNA-seq) of R1D revealed a non-conservative punctual mutation (L261R) in BN5_2307, a member of the AraC family of activators, modifying the charge of the HTH region of the protein. The inactivation of the mutated gene in the evolved strain by double recombination reverted to the original phenotype. Although the bacterium did not assimilate HMF, it transformed it into value-added building blocks for the chemical industry. These results could be used to improve the production of cost-effective second-generation biofuels from agricultural wastes.

Cycles of external dependency drive evolution of avian carotenoid networks

All organisms depend on input of exogenous compounds that cannot be internally produced. Gain and loss of such dependencies structure ecological communities and drive species' evolution, yet the evolution of mechanisms that accommodate these variable dependencies remain elusive. Here, we show that historical cycles of gains and losses of external dependencies in avian carotenoid-producing networks are linked to their evolutionary diversification. This occurs because internalization of metabolic controls-produced when gains in redundancy of dietary inputs coincide with increased branching of their derived products-enables rapid and sustainable exploration of an existing network by shielding it from environmental fluctuations in inputs. Correspondingly, loss of internal controls constrains evolution to the rate of the gains and losses of dietary precursors. Because internalization of a network's controls necessarily bridges diet-specific enzymatic modules within a network, it structurally links local adaptation and continuous evolution even for traits fully dependent on contingent external inputs.

Each of 3,323 metabolic innovations in the evolution of E. coli arose through the horizontal transfer of a single DNA segment

Even closely related prokaryotes often show an astounding diversity in their ability to grow in different nutritional environments. It has been hypothesized that complex metabolic adaptations-those requiring the independent acquisition of multiple new genes-can evolve via selectively neutral intermediates. However, it is unclear whether this neutral exploration of phenotype space occurs in nature, or what fraction of metabolic adaptations is indeed complex. Here, we reconstruct metabolic models for the ancestors of a phylogeny of 53 Escherichia coli strains, linking genotypes to phenotypes on a genome-wide, macroevolutionary scale. Based on the ancestral and extant metabolic models, we identify 3,323 phenotypic innovations in the history of the E. coli clade that arose through changes in accessory genome content. Of these innovations, 1,998 allow growth in previously inaccessible environments, while 1,325 increase biomass yield. Strikingly, every observed innovation arose through the horizontal acquisition of a single DNA segment less than 30 kb long. Although we found no evidence for the contribution of selectively neutral processes, 10.6% of metabolic innovations were facilitated by horizontal gene transfers on earlier phylogenetic branches, consistent with a stepwise adaptation to successive environments. Ninety-eight percent of metabolic phenotypes accessible to the combined E. coli pangenome can be bestowed on any individual strain by transferring a single DNA segment from one of the extant strains. These results demonstrate an amazing ability of the E. coli lineage to adapt to novel environments through single horizontal gene transfers (followed by regulatory adaptations), an ability likely mirrored in other clades of generalist bacteria.

Metabolic adaptations underlying genome flexibility in prokaryotes

Even across genomes of the same species, prokaryotes exhibit remarkable flexibility in gene content. We do not know whether this flexible or "accessory" content is mostly neutral or adaptive, largely due to the lack of explicit analyses of accessory gene function. Here, across 96 diverse prokaryotic species, I show that a considerable fraction (~40%) of accessory genomes harbours beneficial metabolic functions. These functions take two forms: (1) they significantly expand the biosynthetic potential of individual strains, and (2) they help reduce strain-specific metabolic auxotrophies via intra-species metabolic exchanges. I find that the potential of both these functions increases with increasing genome flexibility. Together, these results are consistent with a significant adaptive role for prokaryotic pangenomes.

Zipf and Heaps laws from dependency structures in component systems

Complex natural and technological systems can be considered, on a coarse-grained level, as assemblies of elementary components: for example, genomes as sets of genes or texts as sets of words. On one hand, the joint occurrence of components emerges from architectural and specific constraints in such systems. On the other hand, general regularities may unify different systems, such as the broadly studied Zipf and Heaps laws, respectively concerning the distribution of component frequencies and their number as a function of system size. Dependency structures (i.e., directed networks encoding the dependency relations between the components in a system) were proposed recently as a possible organizing principles underlying some of the regularities observed. However, the consequences of this assumption were explored only in binary component systems, where solely the presence or absence of components is considered, and multiple copies of the same component are not allowed. Here we consider a simple model that generates, from a given ensemble of dependency structures, a statistical ensemble of sets of components, allowing for components to appear with any multiplicity. Our model is a minimal extension that is memoryless and therefore accessible to analytical calculations. A mean-field analytical approach (analogous to the "Zipfian ensemble" in the linguistics literature) captures the relevant laws describing the component statistics as we show by comparison with numerical computations. In particular, we recover a power-law Zipf rank plot, with a set of core components, and a Heaps law displaying three consecutive regimes (linear, sublinear, and saturating) that we characterize quantitatively.

Structure versus time in the evolutionary diversification of avian carotenoid metabolic networks

Historical associations of genes and proteins are thought to delineate pathways available to subsequent evolution; however, the effects of past functional involvements on contemporary evolution are rarely quantified. Here, we examined the extent to which the structure of a carotenoid enzymatic network persists in avian evolution. Specifically, we tested whether the evolution of carotenoid networks was most concordant with phylogenetically structured expansion from core reactions of common ancestors or with subsampling of biochemical pathway modules from an ancestral network. We compared structural and historical associations in 467 carotenoid networks of extant and ancestral species and uncovered the overwhelming effect of pre-existing metabolic network structure on carotenoid diversification over the last 50 million years of avian evolution. Over evolutionary time, birds repeatedly subsampled and recombined conserved biochemical modules, which likely maintained the overall structure of the carotenoid metabolic network during avian evolution. These findings explain the recurrent convergence of evolutionary distant species in carotenoid metabolism and weak phylogenetic signal in avian carotenoid evolution. Remarkable retention of an ancient metabolic structure throughout extensive and prolonged ecological diversification in avian carotenoid metabolism illustrates a fundamental requirement of organismal evolution - historical continuity of a deterministic network that links past and present functional associations of its components.

Connectivity and complex systems: learning from a multi-disciplinary perspective

In recent years, parallel developments in disparate disciplines have focused on what has come to be termed connectivity; a concept used in understanding and describing complex systems. Conceptualisations and operationalisations of connectivity have evolved largely within their disciplinary boundaries, yet similarities in this concept and its application among disciplines are evident. However, any implementation of the concept of connectivity carries with it both ontological and epistemological constraints, which leads us to ask if there is one type or set of approach(es) to connectivity that might be applied to all disciplines. In this review we explore four ontological and epistemological challenges in using connectivity to understand complex systems from the standpoint of widely different disciplines. These are: (i) defining the fundamental unit for the study of connectivity; (ii) separating structural connectivity from functional connectivity; (iii) understanding emergent behaviour; and (iv) measuring connectivity. We draw upon discipline-specific insights from Computational Neuroscience, Ecology, Geomorphology, Neuroscience, Social Network Science and Systems Biology to explore the use of connectivity among these disciplines. We evaluate how a connectivity-based approach has generated new understanding of structural-functional relationships that characterise complex systems and propose a 'common toolbox' underpinned by network-based approaches that can advance connectivity studies by overcoming existing constraints.

Family-specific scaling laws in bacterial genomes

Among several quantitative invariants found in evolutionary genomics, one of the most striking is the scaling of the overall abundance of proteins, or protein domains, sharing a specific functional annotation across genomes of given size. The size of these functional categories change, on average, as power-laws in the total number of protein-coding genes. Here, we show that such regularities are not restricted to the overall behavior of high-level functional categories, but also exist systematically at the level of single evolutionary families of protein domains. Specifically, the number of proteins within each family follows family-specific scaling laws with genome size. Functionally similar sets of families tend to follow similar scaling laws, but this is not always the case. To understand this systematically, we provide a comprehensive classification of families based on their scaling properties. Additionally, we develop a quantitative score for the heterogeneity of the scaling of families belonging to a given category or predefined group. Under the common reasonable assumption that selection is driven solely or mainly by biological function, these findings point to fine-tuned and interdependent functional roles of specific protein domains, beyond our current functional annotations. This analysis provides a deeper view on the links between evolutionary expansion of protein families and the functional constraints shaping the gene repertoire of bacterial genomes.

Major evolutionary transitions of life, metabolic scaling and the number and size of mitochondria and chloroplasts

We investigate the effects of trophic lifestyle and two types of major evolutionary transitions in individuality-the endosymbiotic acquisition of organelles and development of multicellularity-on organellar and cellular metabolism and allometry. We develop a quantitative framework linking the size and metabolic scaling of eukaryotic cells to the abundance, size and metabolic scaling of mitochondria and chloroplasts and analyse a newly compiled, unprecedented database representing unicellular and multicellular cells covering diverse phyla and tissues. Irrespective of cellularity, numbers and total volumes of mitochondria scale linearly with cell volume, whereas chloroplasts scale sublinearly and sizes of both organelles remain largely invariant with cell size. Our framework allows us to estimate the metabolic scaling exponents of organelles and cells. Photoautotrophic cells and organelles exhibit photosynthetic scaling exponents always less than one, whereas chemoheterotrophic cells and organelles have steeper respiratory scaling exponents close to one. Multicellularity has no discernible effect on the metabolic scaling of organelles and cells. In contrast, trophic lifestyle has a profound and uniform effect, and our results suggest that endosymbiosis fundamentally altered the metabolic scaling of free-living bacterial ancestors of mitochondria and chloroplasts, from steep ancestral scaling to a shallower scaling in their endosymbiotic descendants.

Models of life: epigenetics, diversity and cycles

This review emphasizes aspects of biology that can be understood through repeated applications of simple causal rules. The selected topics include perspectives on gene regulation, phage lambda development, epigenetics, microbial ecology, as well as model approaches to diversity and to punctuated equilibrium in evolution. Two outstanding features are repeatedly described. One is the minimal number of rules to sustain specific states of complex systems for a long time. The other is the collapse of such states and the subsequent dynamical cycle of situations that restitute the system to a potentially new metastable state.

Supra-operonic clusters of functionally related genes (SOCs) are a source of horizontal gene co-transfers

Adaptation of bacteria occurs predominantly via horizontal gene transfer (HGT). While it is widely recognized that horizontal acquisitions frequently encompass multiple genes, it is unclear what the size distribution of successfully transferred DNA segments looks like and what evolutionary forces shape this distribution. Here, we identified 1790 gene family pairs that were consistently co-gained on the same branches across a phylogeny of 53 E. coli strains. We estimated a lower limit of their genomic distances at the time they were transferred to their host genomes; this distribution shows a sharp upper bound at 30 kb. The same gene-pairs can have larger distances (up to 70 kb) in other genomes. These more distant pairs likely represent recent acquisitions via transduction that involve the co-transfer of excised prophage genes, as they are almost always associated with intervening phage-associated genes. The observed distribution of genomic distances of co-transferred genes is much broader than expected from a model based on the co-transfer of genes within operons; instead, this distribution is highly consistent with the size distribution of supra-operonic clusters (SOCs), groups of co-occurring and co-functioning genes that extend beyond operons. Thus, we propose that SOCs form a basic unit of horizontal gene transfer.

Genome-wide gene expression and RNA half-life measurements allow predictions of regulation and metabolic behavior in Methanosarcina acetivorans

BACKGROUND

While a few studies on the variations in mRNA expression and half-lives measured under different growth conditions have been used to predict patterns of regulation in bacterial organisms, the extent to which this information can also play a role in defining metabolic phenotypes has yet to be examined systematically. Here we present the first comprehensive study for a model methanogen.

RESULTS

We use expression and half-life data for the methanogen Methanosarcina acetivorans growing on fast- and slow-growth substrates to examine the regulation of its genes. Unlike Escherichia coli where only small shifts in half-lives were observed, we found that most mRNA have significantly longer half-lives for slow growth on acetate compared to fast growth on methanol or trimethylamine. Interestingly, half-life shifts are not uniform across functional classes of enzymes, suggesting the existence of a selective stabilization mechanism for mRNAs. Using the transcriptomics data we determined whether transcription or degradation rate controls the change in transcript abundance. Degradation was found to control abundance for about half of the metabolic genes underscoring its role in regulating metabolism. Genes involved in half of the metabolic reactions were found to be differentially expressed among the substrates suggesting the existence of drastically different metabolic phenotypes that extend beyond just the methanogenesis pathways. By integrating expression data with an updated metabolic model of the organism (iST807) significant differences in pathway flux and production of metabolites were predicted for the three growth substrates.

CONCLUSIONS

This study provides the first global picture of differential expression and half-lives for a class II methanogen, as well as provides the first evidence in a single organism that drastic genome-wide shifts in RNA half-lives can be modulated by growth substrate. We determined which genes in each metabolic pathway control the flux and classified them as regulated by transcription (e.g. transcription factor) or degradation (e.g. post-transcriptional modification). We found that more than half of genes in metabolism were controlled by degradation. Our results suggest that M. acetivorans employs extensive post-transcriptional regulation to optimize key metabolic steps, and more generally that degradation could play a much greater role in optimizing an organism's metabolism than previously thought.

Theory of prokaryotic genome evolution

Bacteria and archaea typically possess small genomes that are tightly packed with protein-coding genes. The compactness of prokaryotic genomes is commonly perceived as evidence of adaptive genome streamlining caused by strong purifying selection in large microbial populations. In such populations, even the small cost incurred by nonfunctional DNA because of extra energy and time expenditure is thought to be sufficient for this extra genetic material to be eliminated by selection. However, contrary to the predictions of this model, there exists a consistent, positive correlation between the strength of selection at the protein sequence level, measured as the ratio of nonsynonymous to synonymous substitution rates, and microbial genome size. Here, by fitting the genome size distributions in multiple groups of prokaryotes to predictions of mathematical models of population evolution, we show that only models in which acquisition of additional genes is, on average, slightly beneficial yield a good fit to genomic data. These results suggest that the number of genes in prokaryotic genomes reflects the equilibrium between the benefit of additional genes that diminishes as the genome grows and deletion bias (i.e., the rate of deletion of genetic material being slightly greater than the rate of acquisition). Thus, new genes acquired by microbial genomes, on average, appear to be adaptive. The tight spacing of protein-coding genes likely results from a combination of the deletion bias and purifying selection that efficiently eliminates nonfunctional, noncoding sequences.

Structuring evolution: biochemical networks and metabolic diversification in birds

Background

Recurrence and predictability of evolution are thought to reflect the correspondence between genomic and phenotypic dimensions of organisms, and the connectivity in deterministic networks within these dimensions. Direct examination of the correspondence between opportunities for diversification imbedded in such networks and realized diversity is illuminating, but is empirically challenging because both the deterministic networks and phenotypic diversity are modified in the course of evolution. Here we overcome this problem by directly comparing the structure of a “global” carotenoid network – comprising of all known enzymatic reactions among naturally occurring carotenoids – with the patterns of evolutionary diversification in carotenoid-producing metabolic networks utilized by birds.

Results

We found that phenotypic diversification in carotenoid networks across 250 species was closely associated with enzymatic connectivity of the underlying biochemical network – compounds with greater connectivity occurred the most frequently across species and were the hotspots of metabolic pathway diversification. In contrast, we found no evidence for diversification along the metabolic pathways, corroborating findings that the utilization of the global carotenoid network was not strongly influenced by history in avian evolution.

Conclusions

The finding that the diversification in species-specific carotenoid networks is qualitatively predictable from the connectivity of the underlying enzymatic network points to significant structural determinism in phenotypic evolution.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-016-0731-z) contains supplementary material, which is available to authorized users.

Intrinsic limits to gene regulation by global crosstalk

Gene regulation relies on the specificity of transcription factor (TF)–DNA interactions. Limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF–DNA interactions or remains erroneously inactive. As each TF can have numerous interactions with noncognate cis-regulatory elements, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyse the effects of global crosstalk on gene regulation. We find that crosstalk presents a significant challenge for organisms with low-specificity TFs, such as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting at equilibrium, including variants of cooperativity and combinatorial regulation. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements.

Limited specificity of transcription factor-DNA interactions leads to crosstalk in gene regulation. Here the authors consider global crosstalk in regulatory networks of growing size and complexity, and show that it imposes constraints on gene regulation and on the evolution of regulatory networks.

Horizontal gene transfer: essentiality and evolvability in prokaryotes, and roles in evolutionary transitions

The wide spread of gene exchange and loss in the prokaryotic world has prompted the concept of ‘lateral genomics’ to the point of an outright denial of the relevance of phylogenetic trees for evolution. However, the pronounced coherence congruence of the topologies of numerous gene trees, particularly those for (nearly) universal genes, translates into the notion of a statistical tree of life (STOL), which reflects a central trend of vertical evolution. The STOL can be employed as a framework for reconstruction of the evolutionary processes in the prokaryotic world. Quantitatively, however, horizontal gene transfer (HGT) dominates microbial evolution, with the rate of gene gain and loss being comparable to the rate of point mutations and much greater than the duplication rate. Theoretical models of evolution suggest that HGT is essential for the survival of microbial populations that otherwise deteriorate due to the Muller’s ratchet effect. Apparently, at least some bacteria and archaea evolved dedicated vehicles for gene transfer that evolved from selfish elements such as plasmids and viruses. Recent phylogenomic analyses suggest that episodes of massive HGT were pivotal for the emergence of major groups of organisms such as multiple archaeal phyla as well as eukaryotes. Similar analyses appear to indicate that, in addition to donating hundreds of genes to the emerging eukaryotic lineage, mitochondrial endosymbiosis severely curtailed HGT. These results shed new light on the routes of evolutionary transitions, but caution is due given the inherent uncertainty of deep phylogenies.

Adaptive evolution of complex innovations through stepwise metabolic niche expansion

A central challenge in evolutionary biology concerns the mechanisms by which complex metabolic innovations requiring multiple mutations arise. Here, we propose that metabolic innovations accessible through the addition of a single reaction serve as stepping stones towards the later establishment of complex metabolic features in another environment. We demonstrate the feasibility of this hypothesis through three complementary analyses. First, using genome-scale metabolic modelling, we show that complex metabolic innovations in Escherichia coli can arise via changing nutrient conditions. Second, using phylogenetic approaches, we demonstrate that the acquisition patterns of complex metabolic pathways during the evolutionary history of bacterial genomes support the hypothesis. Third, we show how adaptation of laboratory populations of E. coli to one carbon source facilitates the later adaptation to another carbon source. Our work demonstrates how complex innovations can evolve through series of adaptive steps without the need to invoke non-adaptive processes.

The relative efficiency of modular and non-modular networks of different size

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity.

Resource constrained flux balance analysis predicts selective pressure on the global structure of metabolic networks

Background

A universal feature of metabolic networks is their hourglass or bow-tie structure on cellular level. This architecture reflects the conversion of multiple input nutrients into multiple biomass components via a small set of precursor metabolites. However, it is yet unclear to what extent this structural feature is the result of natural selection.

Results

We extend flux balance analysis to account for limited cellular resources. Using this model, optimal structure of metabolic networks can be calculated for different environmental conditions. We observe a significant structural reshaping of metabolic networks for a toy-network and E. coli core metabolism if we increase the share of invested resources for switching between different nutrient conditions. Here, hub nodes emerge and the optimal network structure becomes bow-tie-like as a consequence of limited cellular resource constraint. We confirm this theoretical finding by comparing the reconstructed metabolic networks of bacterial species with respect to their lifestyle.

Conclusions

We show that bow-tie structure can give a system-level fitness advantage to organisms that live in highly competitive and fluctuating environments. Here, limitation of cellular resources can lead to an efficiency-flexibility tradeoff where it pays off for the organism to shorten catabolic pathways if they are frequently activated and deactivated. As a consequence, generalists that shuttle between diverse environmental conditions should have a more predominant bow-tie structure than specialists that visit just a few isomorphic habitats during their life cycle.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0232-5) contains supplementary material, which is available to authorized users.

Role of physical and mental training in brain network configuration

It is hypothesized that the topology of brain networks is constructed by connecting nodes which may be continuously remodeled by appropriate training. Efficiency of physical and/or mental training on the brain relies on the flexibility of networks' architecture molded by local remodeling of proteins and synapses of excitatory neurons producing transformations in network topology. Continuous remodeling of proteins of excitatory neurons is fine-tuning the scaling and strength of excitatory synapses up or down via regulation of intra-cellular metabolic and regulatory networks of the genome-transcriptome-proteome interface. Alzheimer's disease is a model of “energy cost-driven small-world network disorder” with dysfunction of high-energy cost wiring as the network global efficiency is impaired by the deposition of an informed agent, the amyloid-β, selectively targeting high-degree nodes. In schizophrenia, the interconnectivity and density of rich-club networks are significantly reduced. Training-induced homeostatic synaptogenesis-enhancement, presumably via reconfiguration of brain networks into greater small-worldness, appears essential in learning, memory, and executive functions. A macroscopic cartography of creation-removal of synaptic connections in a macro-network, and at the intra-cellular scale, micro-networks regulate the physiological mechanisms for the preferential attachment of synapses. The strongest molecular relationship of exercise and functional connectivity was identified for brain-derived neurotrophic factor (BDNF). The allele variant, rs7294919, also shows a powerful relationship with the hippocampal volume. How the brain achieves this unique quest of reconfiguration remains a puzzle. What are the underlying mechanisms of synaptogenesis promoting communications brain ↔ muscle and brain ↔ brain in such trainings? What is the respective role of independent mental, physical, or combined-mental-physical trainings? Physical practice seems to be playing an instrumental role in the cognitive enhancement (brain ↔ muscle com.). However, mental training, meditation or virtual reality (films, games) require only minimal motor activity and cardio-respiratory stimulation. Therefore, other potential paths (brain ↔ brain com.) molding brain networks are nonetheless essential. Patients with motor neuron disease/injury (e.g., amyotrophic lateral sclerosis, traumatism) also achieve successful cognitive enhancement albeit they may only elicit mental practice.

Horizontal gene transfer and genome evolution in Methanosarcina

Background

Genomes of Methanosarcina spp. are among the largest archaeal genomes. One suggested reason for that is massive horizontal gene transfer (HGT) from bacteria. Genes of bacterial origin may be involved in the central metabolism and solute transport, in particular sugar synthesis, sulfur metabolism, phosphate metabolism, DNA repair, transport of small molecules etc. Horizontally transferred (HT) genes are considered to play the key role in the ability of Methanosarcina spp. to inhabit diverse environments. At the moment, genomes of three Methanosarcina spp. have been sequenced, and while these genomes vary in length and number of protein-coding genes, they all have been shown to accumulate HT genes. However, previous estimates had been made when fewer archaeal genomes were known. Moreover, several Methanosarcinaceae genomes from other genera have been sequenced recently. Here, we revise the census of genes of bacterial origin in Methanosarcinaceae.

Results

About 5 % of Methanosarcina genes have been shown to be horizontally transferred from various bacterial groups to the last common ancestor either of Methanosarcinaceae, or Methanosarcina, or later in the evolution. Simulation of the composition of the NCBI protein non-redundant database for different years demonstrates that the estimates of the HGT rate have decreased drastically since 2002, the year of publication of the first Methanosarcina genome.

The phylogenetic distribution of HT gene donors is non-uniform. Most HT genes were transferred from Firmicutes and Proteobacteria, while no HGT events from Actinobacteria to the common ancestor of Methanosarcinaceae were found. About 50 % of HT genes are involved in metabolism. Horizontal transfer of transcription factors is not common, while 46 % of horizontally transferred genes have demonstrated differential expression in a variety of conditions. HGT of complete operons is relatively infrequent and half of HT genes do not belong to operons.

Conclusions

While genes of bacterial origin are still more frequent in Methanosarcinaceae than in other Archaea, most HGT events described earlier as Methanosarcina-specific seem to have occurred before the divergence of Methanosarcinaceae. Genes horizontally transferred from bacteria to archaea neither tend to be transferred with their regulators, nor in long operons.

Electronic supplementary material

The online version of this article (doi:10.1186/s12862-015-0393-2) contains supplementary material, which is available to authorized users.

The Turbulent Network Dynamics of Microbial Evolution and the Statistical Tree of Life

The wide spread and high rate of gene exchange and loss in the prokaryotic world translate into “network genomics”. The rates of gene gain and loss are comparable with the rate of point mutations but are substantially greater than the duplication rate. Thus, evolution of prokaryotes is primarily shaped by gene gain and loss. These processes are essential to prevent mutational meltdown of microbial populations by stopping Muller’s ratchet and appear to trigger emergence of major novel clades by opening up new ecological niches. At least some bacteria and archaea seem to have evolved dedicated devices for gene transfer. Despite the dominance of gene gain and loss, evolution of genes is intrinsically tree-like. The significant coherence between the topologies of numerous gene trees, particularly those for (nearly) universal genes, is compatible with the concept of a statistical tree of life, which forms the framework for reconstruction of the evolutionary processes in the prokaryotic world.

Comparative analysis of sugarcane bagasse metagenome reveals unique and conserved biomass-degrading enzymes among lignocellulolytic microbial communities

BACKGROUND

As one of the most abundant agricultural wastes, sugarcane bagasse is largely under-exploited, but it possesses a great potential for the biofuel, fermentation, and cellulosic biorefinery industries. It also provides a unique ecological niche, as the microbes in this lignocellulose-rich environment thrive in relatively high temperatures (50°C) with varying microenvironments of aerobic surface to anoxic interior. The microbial community in bagasse thus presents a good resource for the discovery and characterization of new biomass-degrading enzymes; however, it remains largely unexplored.

RESULTS

We have constructed a fosmid library of sugarcane bagasse and obtained the largest bagasse metagenome to date. A taxonomic classification of the bagasse metagenome reviews the predominance of Proteobacteria, which are also found in high abundance in other aerobic environments. Based on the functional characterization of biomass-degrading enzymes, we have demonstrated that the bagasse microbial community benefits from a large repertoire of lignocellulolytic enzymes, which allows them to digest different components of lignocelluoses into single molecule sugars. Comparative genomic analyses with other lignocellulolytic and non-lignocellulolytic metagenomes show that microbial communities are taxonomically separable by their aerobic "open" or anoxic "closed" environments. Importantly, a functional analysis of lignocellulose-active genes (based on the CAZy classifications) reveals core enzymes highly conserved within the lignocellulolytic group, regardless of their taxonomic compositions. Cellulases, in particular, are markedly more pronounced compared to the non-lignocellulolytic group. In addition to the core enzymes, the bagasse fosmid library also contains some uniquely enriched glycoside hydrolases, as well as a large repertoire of the newly defined auxiliary activity proteins.

CONCLUSIONS

Our study demonstrates a conservation and diversification of carbohydrate-active genes among diverse microbial species in different biomass-degrading niches, and signifies the importance of taking a global approach to functionally investigate a microbial community as a whole, as compared to focusing on individual organisms.

Integron associated mobile genes: Just a collection of plug in apps or essential components of cell network hardware?

Lateral gene transfer (LGT) impacts on the evolution of prokaryotes in both the short and long-term. The short-term impacts of mobilized genes are a concern to humans since LGT explains the global rise of multi drug resistant pathogens seen in the past 70 years. However, LGT has been a feature of prokaryotes from the earliest days of their existence and the concept of a bifurcating tree of life is not entirely applicable to prokaryotes since most genes in extant prokaryotic genomes have probably been acquired from other lineages. Successful transfer and maintenance of a gene in a new host is understandable if it acts independently of cell networks and confers an advantage. Antibiotic resistance provides an example of this whereby a gene can be advantageous in virtually any cell across broad species backgrounds. In a longer evolutionary context however laterally transferred genes can be assimilated into even essential cell networks. How this happens is not well understood and we discuss recent work that identifies a mobile gene, unique to a cell lineage, which is detrimental to the cell when lost. We also present some additional data and believe our emerging model will be helpful in understanding how mobile genes integrate into cell networks.

Large-scale dynamics of horizontal transfers

The widespread exchange of genes between bacteria must have consequences on the global architecture of their genomes, which are being found in the abundant genomic data available today. Most of the expansion of bacterial protein families can be attributed to transfer events, which are positively biased for smaller evolutionary distances between genomes, and more frequent for classes that are larger, when summed over all known bacteria. Moreover, “innovation” events where horizontal transfers carry exogenous evolutionary families appear to be less frequent for larger genomes. This dynamic expansion of evolutionary families is interconnected with the acquisition of new biological functions and thus with the size and distribution of the genes’ functional categories found on a genome. This commentary presents our recent contributions to this line of work and possible future directions.

Merging molecular mechanism and evolution: theory and computation at the interface of biophysics and evolutionary population genetics

The variation among sequences and structures in nature is both determined by physical laws and by evolutionary history. However, these two factors are traditionally investigated by disciplines with different emphasis and philosophy-molecular biophysics on one hand and evolutionary population genetics in another. Here, we review recent theoretical and computational approaches that address the crucial need to integrate these two disciplines. We first articulate the elements of these approaches. Then, we survey their contribution to our mechanistic understanding of molecular evolution, the polymorphisms in coding region, the distribution of fitness effects (DFE) of mutations, the observed folding stability of proteins in nature, and the distribution of protein folds in genomes.

Core microbial functional activities in ocean environments revealed by global metagenomic profiling analyses

Metagenomics-based functional profiling analysis is an effective means of gaining deeper insight into the composition of marine microbial populations and developing a better understanding of the interplay between the functional genome content of microbial communities and abiotic factors. Here we present a comprehensive analysis of 24 datasets covering surface and depth-related environments at 11 sites around the world's oceans. The complete datasets comprises approximately 12 million sequences, totaling 5,358 Mb. Based on profiling patterns of Clusters of Orthologous Groups (COGs) of proteins, a core set of reference photic and aphotic depth-related COGs, and a collection of COGs that are associated with extreme oxygen limitation were defined. Their inferred functions were utilized as indicators to characterize the distribution of light- and oxygen-related biological activities in marine environments. The results reveal that, while light level in the water column is a major determinant of phenotypic adaptation in marine microorganisms, oxygen concentration in the aphotic zone has a significant impact only in extremely hypoxic waters. Phylogenetic profiling of the reference photic/aphotic gene sets revealed a greater variety of source organisms in the aphotic zone, although the majority of individual photic and aphotic depth-related COGs are assigned to the same taxa across the different sites. This increase in phylogenetic and functional diversity of the core aphotic related COGs most probably reflects selection for the utilization of a broad range of alternate energy sources in the absence of light.

Cross-species gene-family fluctuations reveal the dynamics of horizontal transfers

Prokaryotes vary their protein repertoire mainly through horizontal transfer and gene loss. To elucidate the links between these processes and the cross-species gene-family statistics, we perform a large-scale data analysis of the cross-species variability of gene-family abundance (the number of members of the family found on a given genome). We find that abundance fluctuations are related to the rate of horizontal transfers. This is rationalized by a minimal theoretical model, which predicts this link. The families that are not captured by the model show abundance profiles that are markedly peaked around a mean value, possibly because of specific abundance selection. Based on these results, we define an abundance variability index that captures a family's evolutionary behavior (and thus some of its relevant functional properties) purely based on its cross-species abundance fluctuations. Analysis and model, combined, show a quantitative link between cross-species family abundance statistics and horizontal transfer dynamics, which can be used to analyze genome ‘flux’. Groups of families with different values of the abundance variability index correspond to genome sub-parts having different plasticity in terms of the level of horizontal exchange allowed by natural selection.

Bacterial genome instability

Bacterial genomes are remarkably stable from one generation to the next but are plastic on an evolutionary time scale, substantially shaped by horizontal gene transfer, genome rearrangement, and the activities of mobile DNA elements. This implies the existence of a delicate balance between the maintenance of genome stability and the tolerance of genome instability. In this review, we describe the specialized genetic elements and the endogenous processes that contribute to genome instability. We then discuss the consequences of genome instability at the physiological level, where cells have harnessed instability to mediate phase and antigenic variation, and at the evolutionary level, where horizontal gene transfer has played an important role. Indeed, this ability to share DNA sequences has played a major part in the evolution of life on Earth. The evolutionary plasticity of bacterial genomes, coupled with the vast numbers of bacteria on the planet, substantially limits our ability to control disease.

Horizontal gene transfer can rescue prokaryotes from Muller's ratchet: benefit of DNA from dead cells and population subdivision

Horizontal gene transfer (HGT) is a major factor in the evolution of prokaryotes. An intriguing question is whether HGT is maintained during evolution of prokaryotes owing to its adaptive value or is a byproduct of selection driven by other factors such as consumption of extracellular DNA (eDNA) as a nutrient. One hypothesis posits that HGT can restore genes inactivated by mutations and thereby prevent stochastic, irreversible deterioration of genomes in finite populations known as Muller’s ratchet. To examine this hypothesis, we developed a population genetic model of prokaryotes undergoing HGT via homologous recombination. Analysis of this model indicates that HGT can prevent the operation of Muller’s ratchet even when the source of transferred genes is eDNA that comes from dead cells and on average carries more deleterious mutations than the DNA of recipient live cells. Moreover, if HGT is sufficiently frequent and eDNA diffusion sufficiently rapid, a subdivided population is shown to be more resistant to Muller’s ratchet than an undivided population of an equal overall size. Thus, to maintain genomic information in the face of Muller’s ratchet, it is more advantageous to partition individuals into multiple subpopulations and let them “cross-reference” each other’s genetic information through HGT than to collect all individuals in one population and thereby maximize the efficacy of natural selection. Taken together, the results suggest that HGT could be an important condition for the long-term maintenance of genomic information in prokaryotes through the prevention of Muller’s ratchet.

A maximum entropy framework for nonexponential distributions

Probability distributions having power-law tails are observed in a broad range of social, economic, and biological systems. We describe here a potentially useful common framework. We derive distribution functions for situations in which a "joiner particle" k pays some form of price to enter a community of size , where costs are subject to economies of scale. Maximizing the Boltzmann-Gibbs-Shannon entropy subject to this energy-like constraint predicts a distribution having a power-law tail; it reduces to the Boltzmann distribution in the absence of economies of scale. We show that the predicted function gives excellent fits to 13 different distribution functions, ranging from friendship links in social networks, to protein-protein interactions, to the severity of terrorist attacks. This approach may give useful insights into when to expect power-law distributions in the natural and social sciences.

Universality and predictability in molecular quantitative genetics

Molecular traits, such as gene expression levels or protein binding affinities, are increasingly accessible to quantitative measurement by modern high-throughput techniques. Such traits measure molecular functions and, from an evolutionary point of view, are important as targets of natural selection. We review recent developments in evolutionary theory and experiments that are expected to become building blocks of a quantitative genetics of molecular traits. We focus on universal evolutionary characteristics: these are largely independent of a trait's genetic basis, which is often at least partially unknown. We show that universal measurements can be used to infer selection on a quantitative trait, which determines its evolutionary mode of conservation or adaptation. Furthermore, universality is closely linked to predictability of trait evolution across lineages. We argue that universal trait statistics extends over a range of cellular scales and opens new avenues of quantitative evolutionary systems biology.

Use of a global metabolic network to curate organismal metabolic networks

The difficulty in annotating the vast amounts of biological information poses one of the greatest current challenges in biological research. The number of genomic, proteomic, and metabolomic datasets has increased dramatically over the last two decades, far outstripping the pace of curation efforts. Here, we tackle the challenge of curating metabolic network reconstructions. We predict organismal metabolic networks using sequence homology and a global metabolic network constructed from all available organismal networks. While sequence homology has been a standard to annotate metabolic networks it has been faulted for its lack of predictive power. We show, however, that when homology is used with a global metabolic network one is able to predict organismal metabolic networks that have enhanced network connectivity. Additionally, we compare the annotation behavior of current database curation efforts with our predictions and find that curation efforts are biased towards adding (rather than removing) reactions to organismal networks.

Topology of molecular interaction networks

Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.

Increments and duplication events of enzymes and transcription factors influence metabolic and regulatory diversity in prokaryotes

In this work, the content of enzymes and DNA-binding transcription factors (TFs) in 794 non-redundant prokaryotic genomes was evaluated. The identification of enzymes was based on annotations deposited in the KEGG database as well as in databases of functional domains (COG and PFAM) and structural domains (Superfamily). For identifications of the TFs, hidden Markov profiles were constructed based on well-known transcriptional regulatory families. From these analyses, we obtained diverse and interesting results, such as the negative rate of incremental changes in the number of detected enzymes with respect to the genome size. On the contrary, for TFs the rate incremented as the complexity of genome increased. This inverse related performance shapes the diversity of metabolic and regulatory networks and impacts the availability of enzymes and TFs. Furthermore, the intersection of the derivatives between enzymes and TFs was identified at 9,659 genes, after this point, the regulatory complexity grows faster than metabolic complexity. In addition, TFs have a low number of duplications, in contrast to the apparent high number of duplications associated with enzymes. Despite the greater number of duplicated enzymes versus TFs, the increment by which duplicates appear is higher in TFs. A lower proportion of enzymes among archaeal genomes (22%) than in the bacterial ones (27%) was also found. This low proportion might be compensated by the interconnection between the metabolic pathways in Archaea. A similar proportion was also found for the archaeal TFs, for which the formation of regulatory complexes has been proposed. Finally, an enrichment of multifunctional enzymes in Bacteria, as a mechanism of ecological adaptation, was detected.

Transcriptomic and phylogenetic analysis of a bacterial cell cycle reveals strong associations between gene co-expression and evolution

BACKGROUND

The genetic network involved in the bacterial cell cycle is poorly understood even though it underpins the remarkable ability of bacteria to proliferate. How such network evolves is even less clear. The major aims of this work were to identify and examine the genes and pathways that are differentially expressed during the Caulobacter crescentus cell cycle, and to analyze the evolutionary features of the cell cycle network.

RESULTS

We used deep RNA sequencing to obtain high coverage RNA-Seq data of five C. crescentus cell cycle stages, each with three biological replicates. We found that 1,586 genes (over a third of the genome) display significant differential expression between stages. This gene list, which contains many genes previously unknown for their cell cycle regulation, includes almost half of the genes involved in primary metabolism, suggesting that these "house-keeping" genes are not constitutively transcribed during the cell cycle, as often assumed. Gene and module co-expression clustering reveal co-regulated pathways and suggest functionally coupled genes. In addition, an evolutionary analysis of the cell cycle network shows a high correlation between co-expression and co-evolution. Most co-expression modules have strong phylogenetic signals, with broadly conserved genes and clade-specific genes predominating different substructures of the cell cycle co-expression network. We also found that conserved genes tend to determine the expression profile of their module.

CONCLUSION

We describe the first phylogenetic and single-nucleotide-resolution transcriptomic analysis of a bacterial cell cycle network. In addition, the study suggests how evolution has shaped this network and provides direct biological network support that selective pressure is not on individual genes but rather on the relationship between genes, which highlights the importance of integrating phylogenetic analysis into biological network studies.

Adaptive laboratory evolution -- principles and applications for biotechnology

Adaptive laboratory evolution is a frequent method in biological studies to gain insights into the basic mechanisms of molecular evolution and adaptive changes that accumulate in microbial populations during long term selection under specified growth conditions. Although regularly performed for more than 25 years, the advent of transcript and cheap next-generation sequencing technologies has resulted in many recent studies, which successfully applied this technique in order to engineer microbial cells for biotechnological applications. Adaptive laboratory evolution has some major benefits as compared with classical genetic engineering but also some inherent limitations. However, recent studies show how some of the limitations may be overcome in order to successfully incorporate adaptive laboratory evolution in microbial cell factory design. Over the last two decades important insights into nutrient and stress metabolism of relevant model species were acquired, whereas some other aspects such as niche-specific differences of non-conventional cell factories are not completely understood. Altogether the current status and its future perspectives highlight the importance and potential of adaptive laboratory evolution as approach in biotechnological engineering.