Metabolic and translational efficiency in microbial organisms

The activity of RubisCO and energy demands for its biosynthesis. Comparative studies with CO2-reductases

Most CO₂ on Earth is fixed into organic matter via reactions catalysed by enzymes called carboxylases. CO₂-fixation via carboxylases occurs in the Calvin-Benson-Bassham (CBB) cycle, and the crucial role in this cycle is played by RubisCO (D-ribulose 1,5-bisphosphate carboxylase/oxygenase). CO₂ can also be fixed by pathways, where a reduction of CO₂ to formate or carbon monoxide (CO) occurs. The latter reactions are performed by so-called CO₂-reductases e.g. formate dehydrogenase (FDH), carbon-monooxide (CO) dehydrogenase (CODH), and crotonyl-CoA reductase/carboxylase (CCR). In general, a simple model of enzymatic activity based only on a turnover rate of an enzyme for an appropriate substrate (k_cat) is insufficient. Based on estimated metabolic costs of each amino acid, the average energetic costs of amino acid biosynthesis (E_aa), and the total costs (E_T) for selected CO₂-fixing enzymes were analyzed concerning 1) k_cat for CO₂ (k_C), and 2) specificity factor (S_rel) for RubisCO. A comparison of E_aa and E_T to their k_C showed that CODH and FDHs do not need to be more efficient enzymes in CO₂ capturing pathways than some forms of RubisCO. CCR was the only both low-cost and highly active CO₂-fixing enzyme. The obtained results showed also that there exists an evolutionarily conserved trade-off between S_rel of RubisCOs and the energetic demands needed for their biosynthesis. Phylogenetic analysis demonstrated that RubisCO, CODH, FDH, and CCR are enzymes formed as a result of parallel evolution. Moreover, the kinetic parameters (k_C) of CO₂-fixing enzymes were plausibly optimized already at the early stages of life evolution on Earth.

Selection for Reducing Energy Cost of Protein Production Drives the GC Content and Amino Acid Composition Bias in Gene Transfer Agents

Kin selection and group selection remain controversial topics in evolutionary biology. We argue that these types of selection are likely to operate in bacterial populations by showing that bacterial gene transfer agents (GTAs), but not related viruses, evolve under conditions of positive selection for the reduction of the energy cost of GTA particle production. We hypothesize that GTAs are dedicated devices mediating the survival of bacteria under conditions of nutrient limitation. The benefits conferred by GTAs under nutritional stress conditions appear to include horizontal dissemination of genes that could provide bacteria with enhanced capabilities for nutrient utilization and increases of nutrient availability occurring through the lysis of GTA-producing bacteria.

Gene transfer agents (GTAs) are virus-like elements integrated into bacterial genomes, particularly, those of Alphaproteobacteria. The GTAs can be induced under conditions of nutritional stress, incorporate random fragments of bacterial DNA into miniphage particles, lyse the host cells, and infect neighboring bacteria, thus enhancing horizontal gene transfer. We show that GTA genes evolve under conditions of pronounced positive selection for the reduction of the energy cost of protein production as shown by comparison of the amino acid compositions with those of both homologous viral genes and host genes. The energy saving in GTA genes is comparable to or even more pronounced than that in the genes encoding the most abundant, essential bacterial proteins. In cases in which viruses acquire genes from GTAs, the bias in amino acid composition disappears in the course of evolution, showing that reduction of the energy cost of protein production is an important factor of evolution of GTAs but not bacterial viruses. These findings strongly suggest that GTAs represent bacterial adaptations rather than selfish, virus-like elements. Because GTA production kills the host cell and does not propagate the GTA genome, it appears likely that the GTAs are retained in the course of evolution via kin or group selection. Therefore, we hypothesize that GTAs facilitate the survival of bacterial populations under energy-limiting conditions through the spread of metabolic and transport capabilities via horizontal gene transfer and increases in nutrient availability resulting from the altruistic suicide of GTA-producing cells.

Genome-wide nucleotide patterns and potential mechanisms of genome divergence following domestication in maize and soybean

BACKGROUND

Plant domestication provides a unique model to study genome evolution. Many studies have been conducted to examine genes, genetic diversity, genome structure, and epigenome changes associated with domestication. Interestingly, domesticated accessions have significantly higher [A] and [T] values across genome-wide polymorphic sites than accessions sampled from the corresponding progenitor species. However, the relative contributions of different genomic regions to this genome divergence pattern and underlying mechanisms have not been well characterized.

RESULTS

Here, we investigate the genome-wide base-composition patterns by analyzing millions of SNPs segregating among 100 accessions from a teosinte-maize comparison set and among 302 accessions from a wild-domesticated soybean comparison set. We show that non-genic part of the genome has a greater contribution than genic SNPs to the [AT]-increase observed between wild and domesticated accessions in maize and soybean. The separation between wild and domesticated accessions in [AT] values is significantly enlarged in non-genic and pericentromeric regions. Motif frequency and sequence context analyses show the motifs (PyCG) related to solar-UV signature are enriched in these regions, particularly when they are methylated. Additional analysis using population-private SNPs also implicates the role of these motifs in relatively recent mutations. With base-composition across polymorphic sites as a genome phenotype, genome scans identify a set of putative candidate genes involved in UV damage repair pathways.

CONCLUSIONS

The [AT]-increase is more pronounced in genomic regions that are non-genic, pericentromeric, transposable elements; methylated; and with low recombination. Our findings establish important links among UV radiation, mutation, DNA repair, methylation, and genome evolution.

Protein lipograms

Linguistic analysis of protein sequences is an underexploited technique. Here, we capitalize on the concept of the lipogram to characterize sequences at the proteome levels. A lipogram is a literary composition which omits one or more letters. A protein lipogram likewise omits one or more types of amino acid. In this article, we establish a usable terminology for the decomposition of a sequence collection in terms of the lipogram. Next, we characterize Uniref50 using a lipogram decomposition. At the global level, protein lipograms exhibit power-law properties. A clear correlation with metabolic cost is seen. Finally, we use the lipogram construction to assign proteomes to the four branches of the tree-of-life: archaea, bacteria, eukaryotes and viruses. We conclude from this pilot study that the lipogram demonstrates considerable potential as an additional tool for sequence analysis and proteome classification.

Selection for energy efficiency drives strand-biased gene distribution in prokaryotes

Lagging-strand genes accumulate more deleterious mutations. Genes are thus preferably located on the leading strand, an observation known as strand-biased gene distribution (SGD). Despite of this mechanistic understanding, a satisfactory quantitative model is still lacking. Replication-transcription-collisions induce stalling of the replication machinery, expose DNA to various attacks, and are followed by error-prone repairs. We found that mutational biases in non-transcribed regions can explain ~71% of the variations in SGDs in 1,552 genomes, supporting the mutagenesis origin of SGD. Mutational biases introduce energetically cheaper nucleotides on the lagging strand, and result in more expensive protein products; consistently, the cost difference between the two strands explains ~50% of the variance in SGDs. Protein costs decrease with increasing gene expression. At similar expression levels, protein products of leading-strand genes are generally cheaper than lagging-strand genes; however, highly-expressed lagging genes are still cheaper than lowly-expressed leading genes. Selection for energy efficiency thus drives some genes to the leading strand, especially those highly expressed and essential, but certainly not all genes. Stronger mutational biases are often associated with low-GC genomes; as low-GC genes encode expensive proteins, low-GC genomes thus tend to have stronger SGDs to alleviate the stronger pressure on efficient energy usage.

Evolution of Nucleotide Punctuation Marks: From Structural to Linear Signals

We present an evolutionary hypothesis assuming that signals marking nucleotide synthesis (DNA replication and RNA transcription) evolved from multi- to unidimensional structures, and were carried over from transcription to translation. This evolutionary scenario presumes that signals combining secondary and primary nucleotide structures are evolutionary transitions. Mitochondrial replication initiation fits this scenario. Some observations reported in the literature corroborate that several signals for nucleotide synthesis function in translation, and vice versa. (a) Polymerase-induced frameshift mutations occur preferentially at translational termination signals (nucleotide deletion is interpreted as termination of nucleotide polymerization, paralleling the role of stop codons in translation). (b) Stem-loop hairpin presence/absence modulates codon-amino acid assignments, showing that translational signals sometimes combine primary and secondary nucleotide structures (here codon and stem-loop). (c) Homopolymer nucleotide triplets (AAA, CCC, GGG, TTT) cause transcriptional and ribosomal frameshifts. Here we find in recently described human mitochondrial RNAs that systematically lack mono-, dinucleotides after each trinucleotide (delRNAs) that delRNA triplets include 2x more homopolymers than mitogenome regions not covered by delRNA. Further analyses of delRNAs show that the natural circular code X (a little-known group of 20 translational signals enabling ribosomal frame retrieval consisting of 20 codons {AAC, AAT, ACC, ATC, ATT, CAG, CTC, CTG, GAA, GAC, GAG, GAT, GCC, GGC, GGT, GTA, GTC, GTT, TAC, TTC} universally overrepresented in coding versus other frames of gene sequences), regulates frameshift in transcription and translation. This dual transcription and translation role confirms for X the hypothesis that translational signals were carried over from transcriptional signals.

Chlorobaculum tepidum Modulates Amino Acid Composition in Response to Energy Availability, as Revealed by a Systematic Exploration of the Energy Landscape of Phototrophic Sulfur Oxidation

Microbial sulfur metabolism, particularly the formation and consumption of insoluble elemental sulfur (S⁰), is an important biogeochemical engine that has been harnessed for applications ranging from bioleaching and biomining to remediation of waste streams. Chlorobaculum tepidum, a low-light-adapted photoautolithotrophic sulfur-oxidizing bacterium, oxidizes multiple sulfur species and displays a preference for more reduced electron donors: sulfide > S⁰ > thiosulfate. To understand this preference in the context of light energy availability, an "energy landscape" of phototrophic sulfur oxidation was constructed by varying electron donor identity, light flux, and culture duration. Biomass and cellular parameters of C. tepidum cultures grown across this landscape were analyzed. From these data, a correction factor for colorimetric protein assays was developed, enabling more accurate biomass measurements for C. tepidum, as well as other organisms. C. tepidum's bulk amino acid composition correlated with energy landscape parameters, including a tendency toward less energetically expensive amino acids under reduced light flux. This correlation, paired with an observation of increased cell size and storage carbon production under electron-rich growth conditions, suggests that C. tepidum has evolved to cope with changing energy availability by tuning its proteome for energetic efficiency and storing compounds for leaner times.

IMPORTANCE

How microbes cope with and adapt to varying energy availability is an important factor in understanding microbial ecology and in designing efficient biotechnological processes. We explored the response of a model phototrophic organism, Chlorobaculum tepidum, across a factorial experimental design that enabled simultaneous variation and analysis of multiple growth conditions, what we term the "energy landscape." C. tepidum biomass composition shifted toward less energetically expensive amino acids at low light levels. This observation provides experimental evidence for evolved efficiencies in microbial proteomes and emphasizes the role that energy flux may play in the adaptive responses of organisms. From a practical standpoint, our data suggest that bulk biomass amino acid composition could provide a simple proxy to monitor and identify energy stress in microbial systems.

Energy efficiency trade-offs drive nucleotide usage in transcribed regions

Efficient nutrient usage is a trait under universal selection. A substantial part of cellular resources is spent on making nucleotides. We thus expect preferential use of cheaper nucleotides especially in transcribed sequences, which are often amplified thousand-fold compared with genomic sequences. To test this hypothesis, we derive a mutation-selection-drift equilibrium model for nucleotide skews (strand-specific usage of 'A' versus 'T' and 'G' versus 'C'), which explains nucleotide skews across 1,550 prokaryotic genomes as a consequence of selection on efficient resource usage. Transcription-related selection generally favours the cheaper nucleotides 'U' and 'C' at synonymous sites. However, the information encoded in mRNA is further amplified through translation. Due to unexpected trade-offs in the codon table, cheaper nucleotides encode on average energetically more expensive amino acids. These trade-offs apply to both strand-specific nucleotide usage and GC content, causing a universal bias towards the more expensive nucleotides 'A' and 'G' at non-synonymous coding sites.

Amino Acid Flux from Metabolic Network Benefits Protein Translation: the Role of Resource Availability

Protein translation is a central step in gene expression and affected by many factors such as codon usage bias, mRNA folding energy and tRNA abundance. Despite intensive previous studies, how metabolic amino acid supply correlates with protein translation efficiency remains unknown. In this work, we estimated the amino acid flux from metabolic network for each protein in Escherichia coli and Saccharomyces cerevisiae by using Flux Balance Analysis. Integrated with the mRNA expression level, protein abundance and ribosome profiling data, we provided a detailed description of the role of amino acid supply in protein translation. Our results showed that amino acid supply positively correlates with translation efficiency and ribosome density. Moreover, with the rank-based regression model, we found that metabolic amino acid supply facilitates ribosome utilization. Based on the fact that the ribosome density change of well-amino-acid-supplied genes is smaller than poorly-amino-acid-supply genes under amino acid starvation, we reached the conclusion that amino acid supply may buffer ribosome density change against amino acid starvation and benefit maintaining a relatively stable translation environment. Our work provided new insights into the connection between metabolic amino acid supply and protein translation process by revealing a new regulation strategy that is dependent on resource availability.

Antibiotic-free selection in biotherapeutics: now and forever

The continuously improving sophistication of molecular engineering techniques gives access to novel classes of bio-therapeutics and new challenges for their production in full respect of the strengthening regulations. Among these biologic agents are DNA based vaccines or gene therapy products and to a lesser extent genetically engineered live vaccines or delivery vehicles. The use of antibiotic-based selection, frequently associated with genetic manipulation of microorganism is currently undergoing a profound metamorphosis with the implementation and diversification of alternative selection means. This short review will present examples of alternatives to antibiotic selection and their context of application to highlight their ineluctable invasion of the bio-therapeutic world.

Amino Acid metabolism conflicts with protein diversity

The 20 protein-coding amino acids are found in proteomes with different relative abundances. The most abundant amino acid, leucine, is nearly an order of magnitude more prevalent than the least abundant amino acid, cysteine. Amino acid metabolic costs differ similarly, constraining their incorporation into proteins. On the other hand, a diverse set of protein sequences is necessary to build functional proteomes. Here, we present a simple model for a cost-diversity trade-off postulating that natural proteomes minimize amino acid metabolic flux while maximizing sequence entropy. The model explains the relative abundances of amino acids across a diverse set of proteomes. We found that the data are remarkably well explained when the cost function accounts for amino acid chemical decay. More than 100 organisms reach comparable solutions to the trade-off by different combinations of proteome cost and sequence diversity. Quantifying the interplay between proteome size and entropy shows that proteomes can get optimally large and diverse.

Evolution of complete proteomes: guanine-cytosine pressure, phylogeny and environmental influences blend the proteomic architecture

Background

Guanine-cytosine (GC) composition is an important feature of genomes. Likewise, amino acid composition is a distinct, but less valued, feature of proteomes. A major concern is that it is not clear what valuable information can be acquired from amino acid composition data. To address this concern, in-depth analyses of the amino acid composition of the complete proteomes from 63 archaea, 270 bacteria, and 128 eukaryotes were performed.

Results

Principal component analysis of the amino acid matrices showed that the main contributors to proteomic architecture were genomic GC variation, phylogeny, and environmental influences. GC pressure drove positive selection on Ala, Arg, Gly, Pro, Trp, and Val, and adverse selection on Asn, Lys, Ile, Phe, and Tyr. The physico-chemical framework of the complete proteomes withstood GC pressure by frequency complementation of GC-dependent amino acid pairs with similar physico-chemical properties. Gln, His, Ser, and Val were responsible for phylogeny and their constituted components could differentiate archaea, bacteria, and eukaryotes. Environmental niche was also a significant factor in determining proteomic architecture, especially for archaea for which the main amino acids were Cys, Leu, and Thr. In archaea, hyperthermophiles, acidophiles, mesophiles, psychrophiles, and halophiles gathered successively along the environment-based principal component. Concordance between proteomic architecture and the genetic code was also related closely to genomic GC content, phylogeny, and lifestyles.

Conclusions

Large-scale analyses of the complete proteomes of a wide range of organisms suggested that amino acid composition retained the trace of GC variation, phylogeny, and environmental influences during evolution. The findings from this study will help in the development of a global understanding of proteome evolution, and even biological evolution.