Amino Acid Cost and Codon-Usage Biases in 6 Prokaryotic Genomes: A Whole-Genome Analysis

PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts

The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi.

Cross-feeding in the gut microbiome: Ecology and mechanisms

Microbial communities are shaped by positive and negative interactions ranging from competition to mutualism. In the context of the mammalian gut and its microbial inhabitants, the integrated output of the community has important impacts on host health. Cross-feeding, the sharing of metabolites between different microbes, has emergent roles in establishing communities of gut commensals that are stable, resistant to invasion, and resilient to external perturbation. In this review, we first explore the ecological and evolutionary implications of cross-feeding as a cooperative interaction. We then survey mechanisms of cross-feeding across trophic levels, from primary fermenters to H2 consumers that scavenge the final metabolic outputs of the trophic network. We extend this analysis to also include amino acid, vitamin, and cofactor cross-feeding. Throughout, we highlight evidence for the impact of these interactions on each species' fitness as well as host health. Understanding cross-feeding illuminates an important aspect of microbe-microbe and host-microbe interactions that establishes and shapes our gut communities.

Phenotypic response of yeast metabolic network to availability of proteinogenic amino acids

Genome-scale metabolism can best be described as a highly interconnected network of biochemical reactions and metabolites. The flow of metabolites, i.e., flux, throughout these networks can be predicted and analyzed using approaches such as flux balance analysis (FBA). By knowing the network topology and employing only a few simple assumptions, FBA can efficiently predict metabolic functions at the genome scale as well as microbial phenotypes. The network topology is represented in the form of genome-scale metabolic models (GEMs), which provide a direct mapping between network structure and function via the enzyme-coding genes and corresponding metabolic capacity. Recently, the role of protein limitations in shaping metabolic phenotypes have been extensively studied following the reconstruction of enzyme-constrained GEMs. This framework has been shown to significantly improve the accuracy of predicting microbial phenotypes, and it has demonstrated that a global limitation in protein availability can prompt the ubiquitous metabolic strategy of overflow metabolism. Being one of the most abundant and differentially expressed proteome sectors, metabolic proteins constitute a major cellular demand on proteinogenic amino acids. However, little is known about the impact and sensitivity of amino acid availability with regards to genome-scale metabolism. Here, we explore these aspects by extending on the enzyme-constrained GEM framework by also accounting for the usage of amino acids in expressing the metabolic proteome. Including amino acids in an enzyme-constrained GEM of Saccharomyces cerevisiae, we demonstrate that the expanded model is capable of accurately reproducing experimental amino acid levels. We further show that the metabolic proteome exerts variable demands on amino acid supplies in a condition-dependent manner, suggesting that S. cerevisiae must have evolved to efficiently fine-tune the synthesis of amino acids for expressing its metabolic proteins in response to changes in the external environment. Finally, our results demonstrate how the metabolic network of S. cerevisiae is robust towards perturbations of individual amino acids, while simultaneously being highly sensitive when the relative amino acid availability is set to mimic a priori distributions of both yeast and non-yeast origins.

Key aspects of the past 30 years of protein design

Proteins are the workhorse of life. They are the building infrastructure of living systems; they are the most efficient molecular machines known, and their enzymatic activity is still unmatched in versatility by any artificial system. Perhaps proteins' most remarkable feature is their modularity. The large amount of information required to specify each protein's function is analogically encoded with an alphabet of just ∼20 letters. The protein folding problem is how to encode all such information in a sequence of 20 letters. In this review, we go through the last 30 years of research to summarize the state of the art and highlight some applications related to fundamental problems of protein evolution.

Incorporation of transition to transversion ratio and nonsense mutations, improves the estimation of the number of synonymous and non-synonymous sites in codons

A common approach to estimate the strength and direction of selection acting on protein coding sequences is to calculate the dN/dS ratio. The method to calculate dN/dS has been widely used by many researchers and many critical reviews have been made on its application after the proposition by Nei and Gojobori in 1986. However, the method is still evolving considering the non-uniform substitution rates and pretermination codons. In our study of SNPs in 586 genes across 156 Escherichia coli strains, synonymous polymorphism in 2-fold degenerate codons were higher in comparison to that in 4-fold degenerate codons, which could be attributed to the difference between transition (Ti) and transversion (Tv) substitution rates where the average rate of a transition is four times more than that of a transversion in general. We considered both the Ti/Tv ratio, and nonsense mutation in pretermination codons, to improve estimates of synonymous (S) and non-synonymous (NS) sites. The accuracy of estimating dN/dS has been improved by considering the Ti/Tv ratio and nonsense substitutions in pretermination codons. We showed that applying the modified approach based on Ti/Tv ratio and pretermination codons results in higher values of dN/dS in 29 common genes of equal reading-frames between E. coli and Salmonella enterica. This study emphasizes the robustness of amino acid composition with varying codon degeneracy, as well as the pretermination codons when calculating dN/dS values.

Convergent Usage of Amino Acids in Human Cancers as A Reversed Process of Tissue Development

Genome- and transcriptome-wide amino acid usage preference across different species is a well-studied phenomenon in molecular evolution, but its characteristics and implication in cancer evolution and therapy remain largely unexplored. Here, we analyzed large-scale transcriptome/proteome profiles, such as The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and found that compared to normal tissues, different cancer types showed a convergent pattern toward using biosynthetically low-cost amino acids. Such a pattern can be accurately captured by a single index based on the average biosynthetic energy cost of amino acids, termed energy cost per amino acid (ECPA). With this index, we further compared the trends of amino acid usage and the contributing genes in cancer and tissue development, and revealed their reversed patterns. Finally, focusing on the liver, a tissue with a dramatic increase in ECPA during development, we found that ECPA represents a powerful biomarker that could distinguish liver tumors from normal liver samples consistently across 11 independent patient cohorts and outperforms any index based on single genes. Our study reveals an important principle underlying cancer evolution and suggests the global amino acid usage as a system-level biomarker for cancer diagnosis.

Yeast has evolved to minimize protein resource cost for synthesizing amino acids

Proteins, as essential biomolecules, account for a large fraction of cell mass, and thus the synthesis of the complete set of proteins (i.e., the proteome) represents a substantial part of the cellular resource budget. Therefore, cells might be under selective pressures to optimize the resource costs for protein synthesis, particularly the biosynthesis of the 20 proteinogenic amino acids. Previous studies showed that less energetically costly amino acids are more abundant in the proteomes of bacteria that survive under energy-limited conditions, but the energy cost of synthesizing amino acids was reported to be weakly associated with the amino acid usage in Saccharomyces cerevisiae. Here we present a modeling framework to estimate the protein cost of synthesizing each amino acid (i.e., the protein mass required for supporting one unit of amino acid biosynthetic flux) and the glucose cost (i.e., the glucose consumed per amino acid synthesized). We show that the logarithms of the relative abundances of amino acids in S. cerevisiae’s proteome correlate well with the protein costs of synthesizing amino acids (Pearson’s r = −0.89), which is better than that with the glucose costs (Pearson’s r = −0.5). Therefore, we demonstrate that S. cerevisiae tends to minimize protein resource, rather than glucose or energy, for synthesizing amino acids.

Emerging connections between gut microbiome bioenergetics and chronic metabolic diseases

The conventional viewpoint of single-celled microbial metabolism fails to adequately depict energy flow at the systems level in host-adapted microbial communities. Emerging paradigms instead support that distinct microbiomes develop interconnected and interdependent electron transport chains that rely on cooperative production and sharing of bioenergetic machinery (i.e., directly involved in generating ATP) in the extracellular space. These communal resources represent an important subset of the microbial metabolome, designated here as the "pantryome" (i.e., pantry or external storage compartment), that critically supports microbiome function and can exert multifunctional effects on host physiology. We review these interactions as they relate to human health by detailing the genomic-based sharing potential of gut-derived bacterial and archaeal reference strains. Aromatic amino acids, metabolic cofactors (B vitamins), menaquinones (vitamin K2), hemes, and short-chain fatty acids (with specific emphasis on acetate as a central regulator of symbiosis) are discussed in depth regarding their role in microbiome-related metabolic diseases.

Causes and Consequences of Purifying Selection on SARS-CoV-2

Owing to a lag between a deleterious mutation's appearance and its selective removal, gold-standard methods for mutation rate estimation assume no meaningful loss of mutations between parents and offspring. Indeed, from analysis of closely related lineages, in SARS-CoV-2, the Ka/Ks ratio was previously estimated as 1.008, suggesting no within-host selection. By contrast, we find a higher number of observed SNPs at 4-fold degenerate sites than elsewhere and, allowing for the virus's complex mutational and compositional biases, estimate that the mutation rate is at least 49-67% higher than would be estimated based on the rate of appearance of variants in sampled genomes. Given the high Ka/Ks one might assume that the majority of such intrahost selection is the purging of nonsense mutations. However, we estimate that selection against nonsense mutations accounts for only ∼10% of all the "missing" mutations. Instead, classical protein-level selective filters (against chemically disparate amino acids and those predicted to disrupt protein functionality) account for many missing mutations. It is less obvious why for an intracellular parasite, amino acid cost parameters, notably amino acid decay rate, is also significant. Perhaps most surprisingly, we also find evidence for real-time selection against synonymous mutations that move codon usage away from that of humans. We conclude that there is common intrahost selection on SARS-CoV-2 that acts on nonsense, missense, and possibly synonymous mutations. This has implications for methods of mutation rate estimation, for determining times to common ancestry and the potential for intrahost evolution including vaccine escape.

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.

Survival of the cheapest: how proteome cost minimization drives evolution

Darwin's theory of evolution emphasized that positive selection of functional proficiency provides the fitness that ultimately determines the structure of life, a view that has dominated biochemical thinking of enzymes as perfectly optimized for their specific functions. The 20th-century modern synthesis, structural biology, and the central dogma explained the machinery of evolution, and nearly neutral theory explained how selection competes with random fixation dynamics that produce molecular clocks essential e.g. for dating evolutionary histories. However, quantitative proteomics revealed that selection pressures not relating to optimal function play much larger roles than previously thought, acting perhaps most importantly via protein expression levels. This paper first summarizes recent progress in the 21st century toward recovering this universal selection pressure. Then, the paper argues that proteome cost minimization is the dominant, underlying 'non-function' selection pressure controlling most of the evolution of already functionally adapted living systems. A theory of proteome cost minimization is described and argued to have consequences for understanding evolutionary trade-offs, aging, cancer, and neurodegenerative protein-misfolding diseases.

Microbial energy management-A product of three broad tradeoffs

Wherever thermodynamics allows, microbial life has evolved to transform and harness energy. Microbial life thus abounds in the most unexpected places, enabled by profound metabolic diversity. Within this diversity, energy is transformed primarily through variations on a few core mechanisms. Energy is further managed by the physiological processes of cell growth and maintenance that use energy. Some aspects of microbial physiology are streamlined for energetic efficiency while other aspects seem suboptimal or even wasteful. We propose that the energy that a microbe harnesses and devotes to growth and maintenance is a product of three broad tradeoffs: (i) economic, trading enzyme synthesis or operational cost for functional benefit, (ii) environmental, trading optimization for a single environment for adaptability to multiple environments, and (iii) thermodynamic, trading energetic yield for forward metabolic flux. Consideration of these tradeoffs allows one to reconcile features of microbial physiology that seem to opposingly promote either energetic efficiency or waste.

Nonspecific toxicities of Streptococcus pyogenes and Staphylococcus aureus dCas9 in Chlamydia trachomatis

Chlamydiae are common, important pathogens for humans and animals alike. Despite recent advancement in genetics, scientists are still searching for efficient tools to knock out or knock down the expression of chromosomal genes. We attempted to adopt a dCas9-based CRISPR interference (CRISPRi) technology to conditionally knock down gene expression in Chlamydia trachomatis using an anhydrotetracycline (ATC)-inducible expression system. Surprisingly, expression of the commonly used Streptococcus pyogenes dCas9 in C. trachomatis causes strong inhibition in the absence of any guide RNA (gRNA). Staphylococcus aureus dCas9 also shows strong toxicity in the presence of only an empty gRNA scaffold. Toxicity of the S. pyogenes dCas9 is readily observed with as little as 0.2 nM ATC. Growth inhibition by S. aureus dCas9 is evident starting at 1.0 nM ATC. In contrast, C. trachomatis growth was not affected by methionine-tRNA ligase overexpression induced with 10 nM ATC. We conclude that S. pyogenes and S. aureus dCas9 proteins in their current forms have limited utility for chlamydial research and suggest strategies to overcome this problem.

Adapting to the Airways: Metabolic Requirements of Pseudomonas aeruginosa during the Infection of Cystic Fibrosis Patients

Pseudomonas aeruginosa is one of the major causes of morbidity and mortality of cystic fibrosis patients. During the infection, the bacteria colonize the nutritional rich lung mucus, which is present in the airway secretions in the patients, and they adapt their phenotype accordingly to the lung environment. In the airways, P. aeruginosa undergoes a broad metabolic rewiring as a consequence of the nutritional and stressful complexity of the lungs. However, the role of such metabolic rewiring on the infection outcome is poorly understood. Here, we review the metabolic evolution of clinical strains of P. aeruginosa during a cystic fibrosis lung infection and the metabolic functions operating in vivo under patho-physiological conditions. Finally, we discuss the perspective of modeling the cystic fibrosis environment using genome scale metabolic models of P. aeruginosa. Understanding the physiological changes occurring during the infection may pave the way to a more effective treatment for P. aeruginosa lung infections.

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.

Thermophilic Adaptation in Prokaryotes Is Constrained by Metabolic Costs of Proteostasis

Prokaryotes evolved to thrive in an extremely diverse set of habitats, and their proteomes bear signatures of environmental conditions. Although correlations between amino acid usage and environmental temperature are well-documented, understanding of the mechanisms of thermal adaptation remains incomplete. Here, we couple the energetic costs of protein folding and protein homeostasis to build a microscopic model explaining both the overall amino acid composition and its temperature trends. Low biosynthesis costs lead to low diversity of physical interactions between amino acid residues, which in turn makes proteins less stable and drives up chaperone activity to maintain appropriate levels of folded, functional proteins. Assuming that the cost of chaperone activity is proportional to the fraction of unfolded client proteins, we simulated thermal adaptation of model proteins subject to minimization of the total cost of amino acid synthesis and chaperone activity. For the first time, we predicted both the proteome-average amino acid abundances and their temperature trends simultaneously, and found strong correlations between model predictions and 402 genomes of bacteria and archaea. The energetic constraint on protein evolution is more apparent in highly expressed proteins, selected by codon adaptation index. We found that in bacteria, highly expressed proteins are similar in composition to thermophilic ones, whereas in archaea no correlation between predicted expression level and thermostability was observed. At the same time, thermal adaptations of highly expressed proteins in bacteria and archaea are nearly identical, suggesting that universal energetic constraints prevail over the phylogenetic differences between these domains of life.

The social network of microorganisms - how auxotrophies shape complex communities

Microorganisms engage in complex interactions with other organisms and their environment. Recent studies have shown that these interactions are not limited to the exchange of electron donors. Most microorganisms are auxotrophs, thus relying on external nutrients for growth, including the exchange of amino acids and vitamins. Currently, we lack a deeper understanding of auxotrophies in microorganisms and how nutrient requirements differ between different strains and different environments. In this Opinion article, we describe how the study of auxotrophies and nutrient requirements among members of complex communities will enable new insights into community composition and assembly. Understanding this complex network over space and time is crucial for developing strategies to interrogate and shape microbial communities.

Ecology and evolution of metabolic cross-feeding interactions in bacteria

Literature covered: early 2000s to late 2017Bacteria frequently exchange metabolites with other micro- and macro-organisms. In these often obligate cross-feeding interactions, primary metabolites such as vitamins, amino acids, nucleotides, or growth factors are exchanged. The widespread distribution of this type of metabolic interactions, however, is at odds with evolutionary theory: why should an organism invest costly resources to benefit other individuals rather than using these metabolites to maximize its own fitness? Recent empirical work has shown that bacterial genotypes can significantly benefit from trading metabolites with other bacteria relative to cells not engaging in such interactions. Here, we will provide a comprehensive overview over the ecological factors and evolutionary mechanisms that have been identified to explain the evolution and maintenance of metabolic mutualisms among microorganisms. Furthermore, we will highlight general principles that underlie the adaptive evolution of interconnected microbial metabolic networks as well as the evolutionary consequences that result for cells living in such communities.

Biosynthetic energy cost of potentially highly expressed proteins vary with niche in selected actinobacteria

Amino acid and protein biosynthesis requires a number of high energy phosphate bonds and includes a dual energy cost for the synthesis of chemical intermediates during the fueling reactions and the conversion of precursor molecules to final products. One popular hypothesis is that the proteins encoded by putative highly expressed genes (hence called PHXPs) generally utilize low energy consuming amino acids to reduce the biosynthetic cost of the essential proteins. In our study, we found that this idea was not supported in the case of actinobacteria. With the actinobacteria, the energy costs of PHXPs varied in relation to their niche. Free-living, including aquatic, soil and extremophilic, and plant-associated actinobacteria were found to use energetically expensive amino acids in their PHXPs. An exception occurred with some animal-host-associated actinobacteria that used energy efficient amino acids. One explanation for these results may be due to the diverse metabolic patterns exhibited by actinobacteria under varied niches influenced by nutritional availability and physical environment.

Vitamin and Amino Acid Auxotrophy in Anaerobic Consortia Operating under Methanogenic Conditions

Microbial interactions between Archaea and Bacteria mediate many important chemical transformations in the biosphere from degrading abundant polymers to synthesis of toxic compounds. Two of the most pressing issues in microbial interactions are how consortia are established and how we can modulate these microbial communities to express desirable functions. Here, we propose that public goods (i.e., metabolites of high energy demand in biosynthesis) facilitate energy conservation for life under energy-limited conditions and determine the assembly and function of the consortia. Our report suggests that an understanding of public good dynamics could result in new ways to improve microbial pollutant degradation in anaerobic systems.

Syntrophy among Archaea and Bacteria facilitates the anaerobic degradation of organic compounds to CH₄ and CO₂. Particularly during aliphatic and aromatic hydrocarbon mineralization, as in the case of crude oil reservoirs and petroleum-contaminated sediments, metabolic interactions between obligate mutualistic microbial partners are of central importance. Using micromanipulation combined with shotgun metagenomic approaches, we describe the genomes of complex consortia within short-chain alkane-degrading cultures operating under methanogenic conditions. Metabolic reconstruction revealed that only a small fraction of genes in the metagenome-assembled genomes encode the capacity for fermentation of alkanes facilitated by energy conservation linked to H₂ metabolism. Instead, the presence of inferred lifestyles based on scavenging anabolic products and intermediate fermentation products derived from detrital biomass was a common feature. Additionally, inferred auxotrophy for vitamins and amino acids suggests that the hydrocarbon-degrading microbial assemblages are structured and maintained by multiple interactions beyond the canonical H₂-producing and syntrophic alkane degrader-methanogen partnership. Compared to previous work, our report points to a higher order of complexity in microbial consortia engaged in anaerobic hydrocarbon transformation.

IMPORTANCE Microbial interactions between Archaea and Bacteria mediate many important chemical transformations in the biosphere from degrading abundant polymers to synthesis of toxic compounds. Two of the most pressing issues in microbial interactions are how consortia are established and how we can modulate these microbial communities to express desirable functions. Here, we propose that public goods (i.e., metabolites of high energy demand in biosynthesis) facilitate energy conservation for life under energy-limited conditions and determine the assembly and function of the consortia. Our report suggests that an understanding of public good dynamics could result in new ways to improve microbial pollutant degradation in anaerobic systems.

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.

Superoxide dismutase 1 is positively selected to minimize protein aggregation in great apes

Positive (adaptive) selection has recently been implied in human superoxide dismutase 1 (SOD1), a highly abundant antioxidant protein with energy signaling and antiaging functions, one of very few examples of direct selection on a human protein product (exon); the molecular drivers of this selection are unknown. We mapped 30 extant SOD1 sequences to the recently established mammalian species tree and inferred ancestors, key substitutions, and signatures of selection during the protein's evolution. We detected elevated substitution rates leading to great apes (Hominidae) at ~1 per 2 million years, significantly higher than in other primates and rodents, although these paradoxically generally evolve much faster. The high evolutionary rate was partly due to relaxation of some selection pressures and partly to distinct positive selection of SOD1 in great apes. We then show that higher stability and net charge and changes at the dimer interface were selectively introduced upon separation from old world monkeys and lesser apes (gibbons). Consequently, human, chimpanzee and gorilla SOD1s have a net charge of -6 at physiological pH, whereas the closely related gibbons and macaques have -3. These features consistently point towards selection against the malicious aggregation effects of elevated SOD1 levels in long-living great apes. The findings mirror the impact of human SOD1 mutations that reduce net charge and/or stability and cause ALS, a motor neuron disease characterized by oxidative stress and SOD1 aggregates and triggered by aging. Our study thus marks an example of direct selection for a particular chemical phenotype (high net charge and stability) in a single human protein with possible implications for the evolution of aging.

Global survey of mRNA levels and decay rates of Chlamydia trachomatis trachoma and lymphogranuloma venereum biovars

Interpreting the intricate bacterial transcriptomics implies understanding the dynamic relationship established between de novo transcription and the degradation of transcripts. Here, we performed a comparative overview of gene expression levels and mRNA decay rates for different-biovar (trachoma and lymphogranuloma venereum) strains of the obligate intracellular bacterium Chlamydia trachomatis. By using RNA-sequencing to measure gene expression levels at mid developmental stage and mRNA decay rates upon rifampicin-based transcription blockage, we observed that: i) 60-70% of the top-50 expressed genes encode proteins with unknown function and proteins involved in "Translation, ribosomal structure and biogenesis" for all strains; ii) the expression ranking by genes' functional categories was in general concordant among different-biovar strains; iii) the median of the half-life time (t_1/2) values of transcripts were 15-17 min, indicating that the degree of transcripts' stability seems to correlate with the bacterial intracellular life-style, as these values are considerably higher than the ones observed in other studies for facultative intracellular and free-living bacteria; iv) transcript decay rates were highly heterogeneous within each C. trachomatis strain and did not correlate with steady-state expression levels; v) only at very few instances (essentially at gene functional category level) was possible to unveil dissimilarities potentially underlying phenotypic differences between biovars. In summary, the unveiled transcriptomic scenario, marked by a general lack of correlation between transcript production and degradation and a huge inter-transcript heterogeneity in decay rates, likely reflects the challenges underlying the unique biphasic developmental cycle of C. trachomatis and its intricate interactions with the human host, which probably exacerbate the complexity of the bacterial transcription regulation.

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.

Characterization of parasite-specific indels and their proposed relevance for selective anthelminthic drug targeting

Insertions and deletions (indels) are important sequence variants that are considered as phylogenetic markers that reflect evolutionary adaptations in different species. In an effort to systematically study indels specific to the phylum Nematoda and their structural impact on the proteins bearing them, we examined over 340,000 polypeptides from 21 nematode species spanning the phylum, compared them to non-nematodes and identified indels unique to nematode proteins in more than 3000 protein families. Examination of the amino acid composition revealed uneven usage of amino acids for insertions and deletions. The amino acid composition and cost, along with the secondary structure constitution of the indels, were analyzed in the context of their biological pathway associations. Species-specific indels could enable indel-based targeting for drug design in pathogens/parasites. Therefore, we screened the spatial locations of the indels in the parasite's protein 3D structures, determined the location of the indel and identified potential unique drug targeting sites. These indels could be confirmed by RNA-Seq data. Examples are presented illustrating the close proximity of some indels to established small-molecule binding pockets that can potentially facilitate selective targeting to the parasites and bypassing their host, thus reducing or eliminating the toxicity of the potential drugs. This study presents an approach for understanding the adaptation of pathogens/parasites at a molecular level, and outlines a strategy to identify such nematode-selective targets that remain essential to the organism. With further experimental characterization and validation, it opens a possible channel for the development of novel treatments with high target specificity, addressing both host toxicity and resistance concerns.

Extraordinarily adaptive properties of the genetically encoded amino acids

Using novel advances in computational chemistry, we demonstrate that the set of 20 genetically encoded amino acids, used nearly universally to construct all coded terrestrial proteins, has been highly influenced by natural selection. We defined an adaptive set of amino acids as one whose members thoroughly cover relevant physico-chemical properties, or “chemistry space.” Using this metric, we compared the encoded amino acid alphabet to random sets of amino acids. These random sets were drawn from a computationally generated compound library containing 1913 alternative amino acids that lie within the molecular weight range of the encoded amino acids. Sets that cover chemistry space better than the genetically encoded alphabet are extremely rare and energetically costly. Further analysis of more adaptive sets reveals common features and anomalies, and we explore their implications for synthetic biology. We present these computations as evidence that the set of 20 amino acids found within the standard genetic code is the result of considerable natural selection. The amino acids used for constructing coded proteins may represent a largely global optimum, such that any aqueous biochemistry would use a very similar set.

Effects of varying nitrogen sources on amino acid synthesis costs in Arabidopsis thaliana under different light and carbon-source conditions

Plants as sessile organisms cannot escape their environment and have to adapt to any changes in the availability of sunlight and nutrients. The quantification of synthesis costs of metabolites, in terms of consumed energy, is a prerequisite to understand trade-offs arising from energetic limitations. Here, we examine the energy consumption of amino acid synthesis in Arabidopsis thaliana. To quantify these costs in terms of the energy equivalent ATP, we introduce an improved cost measure based on flux balance analysis and apply it to three state-of-the-art metabolic reconstructions to ensure robust results. We present the first systematic in silico analysis of the effect of nitrogen supply (nitrate/ammonium) on individual amino acid synthesis costs as well as of the effect of photoautotrophic and heterotrophic growth conditions, integrating day/night-specific regulation. Our results identify nitrogen supply as a key determinant of amino acid costs, in agreement with experimental evidence. In addition, the association of the determined costs with experimentally observed growth patterns suggests that metabolite synthesis costs are involved in shaping regulation of plant growth. Finally, we find that simultaneous uptake of both nitrogen sources can lead to efficient utilization of energy source, which may be the result of evolutionary optimization.

A model of proteostatic energy cost and its use in analysis of proteome trends and sequence evolution

A model of proteome-associated chemical energetic costs of cells is derived from protein-turnover kinetics and protein folding. Minimization of the proteostatic maintenance cost can explain a range of trends of proteomes and combines both protein function, stability, size, proteostatic cost, temperature, resource availability, and turnover rates in one simple framework. We then explore the ansatz that the chemical energy remaining after proteostatic maintenance is available for reproduction (or cell division) and thus, proportional to organism fitness. Selection for lower proteostatic costs is then shown to be significant vs. typical effective population sizes of yeast. The model explains and quantifies evolutionary conservation of highly abundant proteins as arising both from functional mutations and from changes in other properties such as stability, cost, or turnover rates. We show that typical hypomorphic mutations can be selected against due to increased cost of compensatory protein expression (both in the mutated gene and in related genes, i.e. epistasis) rather than compromised function itself, although this compensation depends on the protein's importance. Such mutations exhibit larger selective disadvantage in abundant, large, synthetically costly, and/or short-lived proteins. Selection against increased turnover costs of less stable proteins rather than misfolding toxicity per se can explain equilibrium protein stability distributions, in agreement with recent findings in E. coli. The proteostatic selection pressure is stronger at low metabolic rates (i.e. scarce environments) and in hot habitats, explaining proteome adaptations towards rough environments as a question of energy. The model may also explain several trade-offs observed in protein evolution and suggests how protein properties can coevolve to maintain low proteostatic cost.

Influence of hydrophilic amino acids and GC-content on expression of recombinant proteins used in vaccines against foot-and-mouth disease virus in Escherichia coli

Epitope-based protein expression in Escherichia coli can be improved by adjusting its amino acid composition and encoding genes. To that end, we analyzed 24 recombinant epitope proteins (rEPs) that carry multiple epitopes derived from VP1 protein of foot-and-mouth disease virus. High level expression of the rEPs was attributed to a high content of Arg, Asn, Asp and Thr, a low content of Gln, Pro and Lys, a high content of hydrophilic amino acids and a higher isoelectric point value resulting from abundant Arg. It is also attributed to the appropriate guanine and cytosine content in the encoding genes. The data provide a reference for adjusting the amino acid composition in designing epitope-based proteins used in vaccines and for adjusting the synonymous codons to improve their expressions in E. coli.

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.

Metabolic costs of amino acid and protein production in Escherichia coli

Escherichia coli is the most popular microorganism for the production of recombinant proteins and is gaining increasing importance for the production of low-molecular weight compounds such as amino acids. The metabolic cost associated with the production of amino acids and (recombinant) proteins from glucose, glycerol and acetate was determined using three different computational techniques to identify those amino acids that put the highest burden on the biosynthetic machinery of E. coli. Comparing the costs of individual amino acids, we find that methionine is the most expensive amino acid in terms of consumed mol of ATP per molecule produced, while leucine is the most expensive amino acid when taking into account the cellular abundances of amino acids. Moreover, we show that the biosynthesis of a large number of amino acids from glucose and particularly from glycerol provides a surplus of energy, which can be used to balance the high energetic cost of amino acid polymerization.

Gene expression levels are correlated with synonymous codon usage, amino acid composition, and gene architecture in the red flour beetle, Tribolium castaneum

Gene expression levels correlate with multiple aspects of gene sequence and gene structure in phylogenetically diverse taxa, suggesting an important role of gene expression levels in the evolution of protein-coding genes. Here we present results of a genome-wide study of the influence of gene expression on synonymous codon usage, amino acid composition, and gene structure in the red flour beetle, Tribolium castaneum. Consistent with the action of translational selection, we find that synonymous codon usage bias increases with gene expression. However, the correspondence between tRNA gene copy number and optimal codons is weak. At the amino acid level, translational selection is suggested by the positive correlation between tRNA gene numbers and amino acid usage, which is stronger for highly expressed genes. In addition, there is a clear trend for increased use of metabolically cheaper, less complex amino acids as gene expression increases. tRNA gene numbers also correlate negatively with amino acid size/complexity (S/C) score indicating the coupling between translational selection and selection to minimize the use of large/complex amino acids. Interestingly, the analysis of 10 additional genomes suggests that the correlation between tRNA gene numbers and amino acid S/C score is widespread and might be explained by selection against negative consequences of protein misfolding. At the level of gene structure, three major trends are detected: 1) complete coding region length increases across low and intermediate expression levels but decreases in highly expressed genes; 2) the average intron size shows the opposite trend, first decreasing with expression, followed by a slight increase in highly expressed genes; and 3) intron density remains nearly constant across all expression levels. These changes in gene architecture are only in partial agreement with selection favoring reduced cost of biosynthesis.

Analytic markovian rates for generalized protein structure evolution

A general understanding of the complex phenomenon of protein evolution requires the accurate description of the constraints that define the sub-space of proteins with mutations that do not appreciably reduce the fitness of the organism. Such constraints can have multiple origins, in this work we present a model for constrained evolutionary trajectories represented by a Markovian process throughout a set of protein-like structures artificially constructed to be topological intermediates between the structure of two natural occurring proteins. The number and type of intermediate steps defines how constrained the total evolutionary process is. By using a coarse-grained representation for the protein structures, we derive an analytic formulation of the transition rates between each of the intermediate structures. The results indicate that compact structures with a high number of hydrogen bonds are more probable and have a higher likelihood to arise during evolution. Knowledge of the transition rates allows for the study of complex evolutionary pathways represented by trajectories through a set of intermediate structures.

Metabolic and translational efficiency in microbial organisms

Metabolic efficiency, as a selective force shaping proteomes, has been shown to exist in Escherichia coli and Bacillus subtilis and in a small number of organisms with photoautotrophic and thermophilic lifestyles. Earlier attempts at larger-scale analyses have utilized proxies (such as molecular weight) for biosynthetic cost, and did not consider lifestyle or auxotrophy. This study extends the analysis to all currently sequenced microbial organisms that are amenable to these analyses while utilizing lifestyle specific amino acid biosynthesis pathways (where possible) to determine protein production costs and compensating for auxotrophy. The tendency for highly expressed proteins (with adherence to codon usage bias as a proxy for expressivity) to utilize less biosynthetically expensive amino acids is taken as evidence of cost selection. A comprehensive analysis of sequenced genomes to identify those that exhibit strong translational efficiency bias (389 out of 1,700 sequenced organisms) is also presented.

A genetic optimization approach for isolating translational efficiency bias

The study of codon usage bias is an important research area that contributes to our understanding of molecular evolution, phylogenetic relationships, respiratory lifestyle, and other characteristics. Translational efficiency bias is perhaps the most well-studied codon usage bias, as it is frequently utilized to predict relative protein expression levels. We present a novel approach to isolating translational efficiency bias in microbial genomes. There are several existent methods for isolating translational efficiency bias. Previous approaches are susceptible to the confounding influences of other potentially dominant biases. Additionally, existing approaches to identifying translational efficiency bias generally require both genomic sequence information and prior knowledge of a set of highly expressed genes. This novel approach provides more accurate results from sequence information alone by resisting the confounding effects of other biases. We validate this increase in accuracy in isolating translational efficiency bias on 10 microbial genomes, five of which have proven particularly difficult for existing approaches due to the presence of strong confounding biases.

A novel approach for determining environment-specific protein costs: the case of Arabidopsis thaliana

Motivation: Comprehensive understanding of cellular processes requires development of approaches which consider the energetic balances in the cell. The existing approaches that address this problem are based on defining energy-equivalent costs which do not include the effects of a changing environment. By incorporating these effects, one could provide a framework for integrating ‘omics’ data from various levels of the system in order to provide interpretations with respect to the energy state and to elicit conclusions about putative global energy-related response mechanisms in the cell.

Results: Here we define a cost measure for amino acid synthesis based on flux balance analysis of a genome-scale metabolic network, and develop methods for its integration with proteomics and metabolomics data. This is a first measure which accounts for the effect of different environmental conditions. We applied this approach to a genome-scale network of Arabidopsis thaliana and calculated the costs for all amino acids and proteins present in the network under light and dark conditions. Integration of function and process ontology terms in the analysis of protein abundances and their costs indicates that, during the night, the cell favors cheaper proteins compared with the light environment. However, this does not imply that there is squandering of resources during the day. The results from the association analysis between the costs, levels and well-defined expenses of amino acid synthesis, indicate that our approach not only captures the adjustment made at the switch of conditions, but also could explain the anticipation of resource usage via a global energy-related regulatory mechanism of amino acid and protein synthesis.

Contact:nikoloski@mpimp-golm.mpg.de

Supplementary information:Supplementary data are available at Bioinformatics online.

Economical evolution: microbes reduce the synthetic cost of extracellular proteins

Protein evolution is not simply a race toward improved function. Because organisms compete for limited resources, fitness is also affected by the relative economy of an organism’s proteome. Indeed, many abundant proteins contain relatively high percentages of amino acids that are metabolically less taxing for the cell to make, thus reducing cellular cost. However, not all abundant proteins are economical, and many economical proteins are not particularly abundant. Here we examined protein composition and found that the relative synthetic cost of amino acids constrains the composition of microbial extracellular proteins. In Escherichia coli, extracellular proteins contain, on average, fewer energetically expensive amino acids independent of their abundance, length, function, or structure. Economic pressures have strategically shaped the amino acid composition of multicomponent surface appendages, such as flagella, curli, and type I pili, and extracellular enzymes, including type III effector proteins and secreted serine proteases. Furthermore, in silico analysis of Pseudomonas syringae, Mycobacterium tuberculosis, Saccharomyces cerevisiae, and over 25 other microbes spanning a wide range of GC content revealed a broad bias toward more economical amino acids in extracellular proteins. The synthesis of any protein, especially those rich in expensive aromatic amino acids, represents a significant investment. Because extracellular proteins are lost to the environment and not recycled like other cellular proteins, they present a greater burden on the cell, as their amino acids cannot be reutilized during translation. We hypothesize that evolution has optimized extracellular proteins to reduce their synthetic burden on the cell.

Microbes secrete proteins to perform essential interactions with their environment, such as motility, pathogenesis, biofilm formation, and resource acquisition. However, because microbes generally lack protein import systems, secretion is often a one-way street. Consequently, secreted proteins are less likely to be recycled by the cell due to environmental loss. We demonstrate that evolution has in turn selected these extracellular proteins for increased economy at the level of their amino acid composition. Compared to their cellular counterparts, extracellular proteins have fewer synthetically expensive amino acids and more inexpensive amino acids. The resulting bias lessens the loss of cellular resources due to secretion. Furthermore, this economical bias was observed regardless of the abundance, length, structure, or function of extracellular proteins. Thus, it appears that economy may address the compositional bias seen in many extracellular proteins and deliver further insight into the forces driving their evolution.

Evolutionary systems biology of amino acid biosynthetic cost in yeast

Every protein has a biosynthetic cost to the cell based on the synthesis of its constituent amino acids. In order to optimise growth and reproduction, natural selection is expected, where possible, to favour the use of proteins whose constituents are cheaper to produce, as reduced biosynthetic cost may confer a fitness advantage to the organism. Quantifying the cost of amino acid biosynthesis presents challenges, since energetic requirements may change across different cellular and environmental conditions. We developed a systems biology approach to estimate the cost of amino acid synthesis based on genome-scale metabolic models and investigated the effects of the cost of amino acid synthesis on Saccharomyces cerevisiae gene expression and protein evolution. First, we used our two new and six previously reported measures of amino acid cost in conjunction with codon usage bias, tRNA gene number and atomic composition to identify which of these factors best predict transcript and protein levels. Second, we compared amino acid cost with rates of amino acid substitution across four species in the genus Saccharomyces. Regardless of which cost measure is used, amino acid biosynthetic cost is weakly associated with transcript and protein levels. In contrast, we find that biosynthetic cost and amino acid substitution rates show a negative correlation, but for only a subset of cost measures. In the economy of the yeast cell, we find that the cost of amino acid synthesis plays a limited role in shaping transcript and protein expression levels compared to that of translational optimisation. Biosynthetic cost does, however, appear to affect rates of amino acid evolution in Saccharomyces, suggesting that expensive amino acids may only be used when they have specific structural or functional roles in protein sequences. However, as there appears to be no single currency to compute the cost of amino acid synthesis across all cellular and environmental conditions, we conclude that a systems approach is necessary to unravel the full effects of amino acid biosynthetic cost in complex biological systems.

Temperature adaptation at homologous sites in proteins from nine thermophile-mesophile species pairs

Whether particular amino acids are favored by selection at high temperatures over others has long been an open question in protein evolution. One way to approach this question is to compare homologous sites in proteins from one thermophile and a closely related mesophile; asymmetrical substitution patterns have been taken as evidence for selection favoring certain amino acids over others. However, most pairs of prokaryotic species that differ in optimum temperature also differ in genome-wide GC content, and amino acid content is known to be associated with GC content. Here, I compare homologous sites in nine thermophilic prokaryotes and their mesophilic relatives, all with complete published genome sequences. After adjusting for the effects of differing GC content with logistic regression, 139 of the 190 pairs of amino acids show significant substitutional asymmetry, evidence of widespread adaptive amino acid substitution. The patterns are fairly consistent across the nine pairs of species (after taking the effects of differing GC content into account), suggesting that much of the asymmetry results from adaptation to temperature. Some amino acids in some species pairs deviate from the overall pattern in ways indicating that adaptation to other environmental or physiological differences between the species may also play a role. The property that is best correlated with the patterns of substitutional asymmetry is transfer free energy, a measure of hydrophobicity, with more hydrophobic amino acids favored at higher temperatures. The correlation of asymmetry and hydrophobicity is fairly weak, suggesting that other properties may also be important.

Novel SNPs of butyrophilin (BTN1A1) and milk fat globule epidermal growth factor (EGF) 8 (MFG-E8) are associated with milk traits in dairy goat

Butyrophilin (BTN1A1) and milk fat globule epidermal growth factor (EGF) 8 (MFG-E8) genes are both milk fat globule membrane proteins. BTN1A1 plays a key role in the secretion of milk lipid and production which has effects on performance traits, while the MFG-E8 is vital for the development of the mammary gland and phagocytic clearance of apoptotic cells. Therefore, BTN1A1 and MFG-E8 gene are candidate genes for quantitative traits in mammalian animals with respect to milk performance traits. The objective of this study is to investigate variations in goat BTN1A1 and MFG-E8 gene and analyze their associations with growth trait and milk performance. In this study, the goat BTN1A1 gene showed a novel single-nucleotide polymorphism (SNP): XM_001494179:g.8659C>T, resulting in a missense mutation: CTT (Leu)>TTT (Phe) at position 377 aa of the BTN1A1 (526 aa); the goat MFG-E8 gene showed four novel SNPs: NC_007319: g.843delA, 6417delC, 14892T>C and 14996A>C, only the 14892T>C result in a synonymous mutation. The associations between genotypes and production traits were analyzed. Significant statistical results implied that HinfI locus of BTN1A1 gene is associated with milk fat yield (P=0.004), total solid (P=0.002), solid-non fat (P=0.018) and first milk yield (P=0.030). The DA and EcoRV loci of MFG-E8 gene are associated with milk fat yield (DA locus: P=0.000; EcoRV locus: P=0.033) and total solid (DA locus: P=0.002; EcoRV locus: P=0.015) in the Xinong Saanen dairy goat.

Acquisition of nutrients by Chlamydiae: unique challenges of living in an intracellular compartment

The Chlamydiae are obligate intracellular pathogens that replicate within a membrane-bound vacuole, termed the 'inclusion'. From this compartment, bacteria acquire essential nutrients by selectively redirecting transport vesicles and hijacking intracellular organelles. Rerouting is achieved by several mechanisms including proteolysis-mediated fragmentation of the Golgi apparatus, recruitment of Rab GTPases and SNAREs, and translocation of cytoplasmic organelles into the inclusion lumen. Given Chlamydiae's extended coevolution with eukaryotic cells, it is likely that co-option of multiple cellular pathways is a strategy to provide redundancy in the acquisition of essential nutrients from the host and has contributed to the success of these highly adapted pathogens.

Do amino acid biosynthetic costs constrain protein evolution in Saccharomyces cerevisiae?

Prokaryotic organisms preferentially utilize less energetically costly amino acids in highly expressed genes. Studies have shown that the proteome of Saccharomyces cerevisiae also exhibits this behavior, but only in broad terms. This study examines the question of metabolic efficiency as a proteome-shaping force at a finer scale, examining whether trends consistent with cost minimization as an evolutionary force are present independent of protein function and amino acid physicochemical property, and consistently with respect to amino acid biosynthetic costs. Inverse correlations between the average amino acid biosynthetic cost of the protein product and the levels of gene expression in S. cerevisiae are consistent with natural selection to minimize costs. There are, however, patterns of amino acid usage that raise questions about the strength (and possibly the universality) of this selective force in shaping S. cerevisiae's proteome.

Low contents of carbon and nitrogen in highly abundant proteins: evidence of selection for the economy of atomic composition

Proteins that assimilate particular elements were found to avoid using amino acids containing the element, which indicates that the metabolic constraints of amino acids may influence the evolution of proteins. We suspected that low contents of carbon, nitrogen, and sulfur may also be selected for economy in highly abundant proteins that consume large amounts of the resources of cells. By analyzing recently available proteomic data in Escherichia coli, Saccharomyces cerevisiae, and Schizosaccharomyces pombe, we found that at least the carbon and nitrogen contents in amino acid side chains are negatively correlated with protein abundance. An amino acid with a high number of carbon atoms in its side chain generally requires relatively more energy for its synthesis. Thus, it may be selected against in highly abundant proteins either because of economy in building blocks or because of economy in energy. Previous studies showed that highly abundant proteins preferentially use cheap (in terms of energy) amino acids. We found that the carbon content is still negatively correlated with protein abundance after controlling for the energetic cost of the amino acids. However, the negative correlation between protein abundance and energetic cost disappeared after controlling for carbon content. Building blocks seem to be more restricted than energy. It seems that the amino acid sequences of highly abundant proteins have to compromise between optimization for their biological functions and reducing the consumption of limiting resources. By contrast, the amino acid sequences of weakly expressed proteins are more likely to be optimized for their biological functions.