Variation in the strength of selected codon usage bias among bacteria

A model of Hill-Robertson interference caused by purifying selection in a nonrecombining genome

A new approach to modeling the effects of Hill-Robertson interference on levels of adaptation and patterns of variability in a nonrecombining genome or genomic region is described. The model assumes a set of L diallelic sites subject to reversible mutations between beneficial and deleterious alleles, with the same selection coefficient at each site. The assumption of reversibility allows the system to reach a stable statistical equilibrium with respect to the frequencies of deleterious mutations, in contrast to many previous models that assume irreversible mutations to deleterious alleles. The model is therefore appropriate for understanding the long-term properties of nonrecombining genomes such as Y chromosomes, and is applicable to haploid genomes or to diploid genomes when there is intermediate dominance with respect to the effects of mutations on fitness. Approximations are derived for the equilibrium frequencies of deleterious mutations, the effective population size that controls the fixation probabilities of mutations at sites under selection, the nucleotide site diversity at neutral sites located within the nonrecombining region, and the site frequency spectrum for segregating neutral variants. The approximations take into account the effects of linkage disequilibrium on the genetic variance at sites under selection. Comparisons with published and new computer simulation results show that the approximations are sufficiently accurate to be useful, and can provide insights into a wider range of parameter sets than is accessible by simulation. The relevance of the findings to data on nonrecombining genome regions is discussed.

Pangenomes suggest ecological-evolutionary responses to experimental soil warming

Below-ground carbon transformations that contribute to healthy soils represent a natural climate change mitigation, but newly acquired traits adaptive to climate stress may alter microbial feedback mechanisms. To better define microbial evolutionary responses to long-term climate warming, we study microorganisms from an ongoing in situ soil warming experiment where, for over three decades, temperate forest soils are continuously heated at 5°C above ambient. We hypothesize that across generations of chronic warming, genomic signatures within diverse bacterial lineages reflect adaptations related to growth and carbon utilization. From our bacterial culture collection isolated from experimental heated and control plots, we sequenced genomes representing dominant taxa sensitive to warming, including lineages of Actinobacteria, Alphaproteobacteria, and Betaproteobacteria. We investigated genomic attributes and functional gene content to identify signatures of adaptation. Comparative pangenomics revealed accessory gene clusters related to central metabolism, competition, and carbon substrate degradation, with few functional annotations explicitly associated with long-term warming. Trends in functional gene patterns suggest genomes from heated plots were relatively enriched in central carbohydrate and nitrogen metabolism pathways, while genomes from control plots were relatively enriched in amino acid and fatty acid metabolism pathways. We observed that genomes from heated plots had less codon bias, suggesting potential adaptive traits related to growth or growth efficiency. Codon usage bias varied for organisms with similar 16S rrn operon copy number, suggesting that these organisms experience different selective pressures on growth efficiency. Our work suggests the emergence of lineage-specific trends as well as common ecological-evolutionary microbial responses to climate change.IMPORTANCEAnthropogenic climate change threatens soil ecosystem health in part by altering below-ground carbon cycling carried out by microbes. Microbial evolutionary responses are often overshadowed by community-level ecological responses, but adaptive responses represent potential changes in traits and functional potential that may alter ecosystem function. We predict that microbes are adapting to climate change stressors like soil warming. To test this, we analyzed the genomes of bacteria from a soil warming experiment where soil plots have been experimentally heated 5°C above ambient for over 30 years. While genomic attributes were unchanged by long-term warming, we observed trends in functional gene content related to carbon and nitrogen usage and genomic indicators of growth efficiency. These responses may represent new parameters in how soil ecosystems feedback to the climate system.

Macroevolutionary changes in natural selection on codon usage reflects evolution of the tRNA pool across a budding yeast subphylum

Across taxonomical domains, synonymous codons of an amino acid are found to be used at unequal frequencies within genes. This codon usage bias (CUB) is highly variable across species. Genome-wide CUB reflects a balance between adaptive and non-adaptive microevolutionary processes within a species. Variation in microevolutionary processes results in across-species variation in CUB. As CUB is tightly linked to important molecular and biophysical processes, it is critical to understand how changes to these processes are linked to changes in microevolutionary processes. We employed a population genetics model to quantify natural selection and mutation biases on a per-codon basis across the Saccharomycotina budding yeast subphylum. We found that the strength of natural selection and mutation biases varied significantly between closely related yeasts. Across-species variation in natural selection reflected the evolution of tRNA gene copy number. Additionally, we found evidence that changes to tRNA modification expression can contribute to changes in natural selection across species independent of changes to tGCN. Both lines of evidence support the link between the evolution of the tRNA pool and natural selection in codon usage through changes in the translation efficiency of a codon. Furthermore, we found that changes to tGCN often reflected changes to genome-wide GC%, suggesting changes to the tRNA pool reflect changes to mutation bias. Our work establishes how changes in microevolutionary processes impact changes in molecular mechanisms, ultimately shaping the macroevolutionary variation of a trait.

Significance statement

Codon usage bias (CUB) - the non-uniform usage of synonymous codons - is a feature of all genomes and varies across closely related species. Differences in CUB imply differences in the underlying microevolutionary processes (natural selection, mutation bias) driving CUB. CUB is hypothesized to be tightly linked to key molecular processes, particularly mRNA translation. We used a population genetics model to quantify natural selection and mutation bias on a per-codon basis across 327 budding yeasts. We found high variability in the microevolution of CUB and showed that changes in natural selection were correlated with the evolution of the tRNA pool. Our work establishes how variation in molecular mechanisms relates to variation in microevolution, shaping variation in a trait across species.

Variation in the fitness impact of translationally optimal codons among animals

Early studies in invertebrate model organisms (fruit flies, nematodes) showed that their synonymous codon usage is under selective pressure to optimize translation efficiency in highly expressed genes (a process called translational selection). In contrast, mammals show little evidence of selection for translationally optimal codons. To understand this difference, we examined the use of synonymous codons in 223 metazoan species, covering a wide range of animal clades. For each species, we predicted the set of optimal codons based on the pool of tRNA genes present in its genome, and we analyzed how the frequency of optimal codons correlates with gene expression to quantify the intensity of translational selection (S). We observed that few metazoans show clear signs of translational selection. As predicted by the nearly neutral theory, the highest values of S are observed in species with large effective population sizes (N e). Overall, however, N e appears to be a poor predictor of the intensity of translational selection, suggesting important differences in the fitness effect of synonymous codon usage across taxa. We propose that the few animal taxa that are clearly affected by translational selection correspond to organisms with strong constraints for a very rapid growth rate.

Decoding Codon Usage Patterns in High-Risk Human Papillomavirus Genomes: A Comprehensive Analysis

Human Papillomavirus (HPV) is a major contributor to various human cancers, particularly cervical cancer. Despite its significant impact, the codon usage bias in high-risk HPV types has not been extensively studied. Understanding this bias, however, could provide valuable insights into the virus itself and inform the optimization of vaccine design. This study explores codon usage bias within the genomes of 17 high-risk HPV types (HPV-16, 18, 26, 31, 33, 35, 39, 45, 51, 52, 53, 56, 58, 59, 66, 68, and 82) through comparative analysis. While overall codon usage preference across these genotypes is not highly significant, a notable trend emerges in the preference for codons ending in A or U, with 24 out of 26 favored codons (Relative Synonymous Codon Usage > 1) ending in A or U. Moreover, no common optimal codons are shared among the 17 genomes. The study also identifies the underrepresentation of CpG and ApA dinucleotides, alongside the overrepresentation of CpA and UpG, which likely contribute to codon usage preferences that may influence viral replication and immune evasion strategies. Integrated analysis further suggests that natural selection is the primary force driving codon usage bias in these high-risk HPV genomes. Additionally, these HPVs exhibit a limited set of favored codons shared with humans, potentially minimizing competition for translation resources. This study offers new insights into codon usage bias in high-risk HPVs and underscores the importance of this understanding for optimizing vaccine design.

Comprehensive Analysis of Codon Usage Bias in Human Papillomavirus Type 51

Human papillomavirus type 51 (HPV-51) is associated with various cancers, including cervical cancer. Examining the codon usage bias of the organism can offer valuable insights into its evolutionary patterns and its relationship with the host. This study comprehensively analyzed codon usage bias in HPV-51 by examining 64 complete genome sequences sourced from the NCBI GenBank database. Our analysis revealed no noteworthy preference for codon usage in HPV-51 overall. However, there was a noticeable bias towards A/T-ending codons, accompanied by GC3s below 32%. Dinucleotide frequency analysis revealed reduced frequencies for ApA, CpG, and TpC dinucleotides, while CpA and TpG dinucleotides were more frequent than others. Relative Synonymous Codon Usage analysis revealed 30 favored codons, primarily concluding with A/T nucleotides. Further analysis using Parity Rule 2, Effective Number of Codons plot, and neutrality plot indicated a balance between mutational pressure and natural selection, with natural selection being the primary force shaping codon usage bias. The Isoacceptor tRNA Pool analysis indicates that HPV-51 has a higher translation efficiency within the human cellular translational system. Moreover, the Codon Adaptation Index and Relative Codon Deoptimization Index analyses suggested a moderate adaptation of HPV-51 to human codon preferences. Our discoveries offer valuable perspectives on how HPV-51 evolves and uses genetic codes, contributing to a deeper comprehension of its endurance and disease-causing potential.

A machine learning approach uncovers principles and determinants of eukaryotic ribosome pausing

Nonuniform local translation speed dictates diverse protein biogenesis outcomes. To unify known and uncover unknown principles governing eukaryotic elongation rate, we developed a machine learning pipeline to analyze RiboSeq datasets. We find that the chemical nature of the incoming amino acid determines how codon optimality influences elongation rate, with hydrophobic residues more dependent on transfer RNA (tRNA) levels than charged residues. Unexpectedly, we find that wobble interactions exert a widespread effect on elongation pausing, with wobble-mediated decoding being slower than Watson-Crick decoding, irrespective of tRNA levels. Applying our ribosome pausing principles to ribosome collisions reveals that disomes arise upon apposition of fast-decoding and slow-decoding signatures. We conclude that codon choice and tRNA pools are evolutionarily constrained to harmonize elongation rate with cotranslational folding while minimizing wobble pairing and deleterious stalling.

The Effect of the Presence and Absence of DNA Repair Genes on the Rate and Pattern of Mutation in Bacteria

Bacteria lose and gain repair genes as they evolve. Here, we investigate the consequences of gain and loss of 11 DNA repair genes across a broad range of bacteria. Using synonymous polymorphisms from bacteria and a set of 50 phylogenetically independent contrasts, we find no evidence that the presence or absence of these 11 genes affects either the overall level of diversity or the pattern of mutation. Using phylogenetic generalized linear squares yields a similar conclusion. It seems likely that the lack of an effect is due to variation in the genetic background and the environment which obscures any effects that the presence or absence of individual genes might have.

Mitogenome selection shaped the terrestrial adaptation of Grapsidae (Decapoda: Brachyura)

The colonization of aquatic to terrestrial habitats by brachyuran crabs requires genetic innovations as well as morphological adaptations to adapt to terrestrial environments. The genetic basis of such adaptive evolution, however, is largely unknown. This study focuses on terrestrialization in Geograpsus (Grapsidae) the only highly terrestrial genus in this family, which represents a notable example of terrestrial adaptive radiation. Here, we sequenced the mitogenomes of two Geograpsus species and used the mitogenomes of 215 representative crabs to construct phylogenetic and time frameworks that we used to infer terrestrial origins and evolution. Using mitochondrial genomic data, we demonstrated that marine crab ancestors began to settle on land during the early Eocene. Ocean acidification, the Paleocene-Eocene Thermal Maximum (PETM), and mangrove expansion at that time may have driven the diversification and ecological expansion of these terrestrial crabs. Evolutionary analyses reveal strong positive selection signals on monophyletic lineages of Grapsidae, especially the terrestrial species of Geograpsus. Positively selected sites in functionally important regions of ND5 and ND4 may imply enhanced energy metabolism in Grapsidae compared to other crabs, and may have played an important role in their terrestrial adaptation. Overall, our work provides valuable resources and opportunities to reveal the adaptation of crabs to complex terrestrial environments.

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder

The nearly neutral theory of molecular evolution posits variation among species in the effectiveness of selection. In an idealized model, the census population size determines both this minimum magnitude of the selection coefficient required for deleterious variants to be reliably purged, and the amount of neutral diversity. Empirically, an 'effective population size' is often estimated from the amount of putatively neutral genetic diversity and is assumed to also capture a species' effectiveness of selection. A potentially more direct measure of the effectiveness of selection is the degree to which selection maintains preferred codons. However, past metrics that compare codon bias across species are confounded by among-species variation in %GC content and/or amino acid composition. Here, we propose a new Codon Adaptation Index of Species (CAIS), based on Kullback-Leibler divergence, that corrects for both confounders. We demonstrate the use of CAIS correlations, as well as the Effective Number of Codons, to show that the protein domains of more highly adapted vertebrate species evolve higher intrinsic structural disorder.

RNA editing sites and triplet usage in exomes of bat RNA virus genomes of the family Paramyxoviridae

Bats contain a diverse spectrum of viral species in their bodies. The RNA virus family Paramyxoviridae tends to infect several vertebrate species, which are accountable for a variety of devastating infections in both humans and animals. Viruses of this kind include measles, mumps, and Hendra. Some synonymous codons are favoured over others in mRNAs during gene-to-protein synthesis process. Such phenomenon is termed as codon usage bias (CUB). Our research emphasized many aspects that shape the CUB of genes in the Paramyxoviridae family found in bats. Here, the nitrogenous base A occurred the most. AT was found to be abundant in the coding sequences of the Paramyxoviridae family. RSCU data revealed that A or T ending codons occurred more frequently than predicted. Furthermore, 3 overrepresented codons (CAT, AGA, and GCA) and 7 underrepresented codons (CCG, TCG, CGC, CGG, CGT, GCG and ACG) were detected in the viral genomes. Correspondence analysis, neutrality plot, and parity plots highlight the combined impact of mutational pressure and natural selection on CUB. The neutrality plot of GC12 against GC3 yielded a regression coefficient value of 0.366, indicating that natural selection had a significant (63.4 %) impact. Moreover, RNA editing analysis was done, which revealed the highest frequency of C to T mutations. The results of our research revealed the pattern of codon usage and RNA editing sites in Paramyxoviridae genomes.

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis

Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-expression and low-expression genes in several bacteria. In this article, we have extended codon usage study considering gene essentiality as a feature. Using machine learning (ML) based approaches, we have analysed Relative Synonymous Codon Usage (RSCU) values between essential and non-essential genes in Escherichia coli and thirty-four other bacterial genomes whose gene essentiality features were available in public databases. We observed significant differences in codon usage patterns between essential and non-essential genes for majority of the bacterial genomes and accordingly, ML based classifiers achieved high area under curve (AUC) scores, with a minimum score of 70.0 across twenty-eight organisms. Further, importance of the codons towards classifying genes found to differ among the codons in each genome. Arg codon CGT and Gly codon GGT were observed to be the most preferred codons among essential genes in Escherichia coli. Interestingly, some of the codons like CGT, ATA, GGT and GGG observed to be contributing consistently towards classifying essential genes across thirty-five bacteria genomes studied. In other hand, codons TGY and CAY encoding amino acids Cys and His respectively were among the least contributing codons towards classification among all these bacteria. This study demonstrates the gene essentiality based differences in synonymous codon usage in bacteria genomes and presents a common codon usage pattern across bacteria.

Complete mitogenome and intra-family comparative mitogenomics showed distinct position of Pama Croaker Otolithoides pama

The Pama Croaker, Otolithoides pama, is an economically important fish species in Bangladesh. Intra-family similarities in morphology and typical barcode sequences of cox1 create ambiguities in its identification. Therefore, morphology and the complete mitochondrial genome of O. pama, and comparative mitogenomics within the family Sciaenidae have been studied. Extracted genomic DNA was subjected to Illumina-based short read sequencing for De-Novo mitogenome assembly. The complete mitogenome of O. pama (Accession: OQ784575.1) was 16,513 bp, with strong AC biasness and strand asymmetry. Relative synonymous codon usage (RSCU) among 13 protein-coding genes (PCGs) of O. pama was also analyzed. The studied mitogenomes including O. pama exhibited consistent sizes and gene orders, except for the genus Johnius which possessed notably longer mitogenomes with unique gene rearrangements. Different genetic distance metrics across 30 species of Sciaenidae family demonstrated 12S rRNA and the control region (CR) as the most conserved and variable regions, respectively, while most of the PCGs undergone a purifying selection. Different phylogenetic trees were congruent with one another, where O. pama was distinctly placed. This study would contribute to distinguishing closely related fish species of Sciaenidae family and can be instrumental in conserving the genetic diversity of O. pama.

The Patterns of Codon Usage between Chordates and Arthropods are Different but Co-evolving with Mutational Biases

Different frequencies amongst codons that encode the same amino acid (i.e. synonymous codons) have been observed in multiple species. Studies focused on uncovering the forces that drive such codon usage showed that a combined effect of mutational biases and translational selection works to produce different frequencies of synonymous codons. However, only few have been able to measure and distinguish between these forces that may leave similar traces on the coding regions. Here, we have developed a codon model that allows the disentangling of mutation, selection on amino acids and synonymous codons, and GC-biased gene conversion (gBGC) which we employed on an extensive dataset of 415 chordates and 191 arthropods. We found that chordates need 15 more synonymous codon categories than arthropods to explain the empirical codon frequencies, which suggests that the extent of codon usage can vary greatly between animal phyla. Moreover, methylation at CpG sites seems to partially explain these patterns of codon usage in chordates but not in arthropods. Despite the differences between the two phyla, our findings demonstrate that in both, GC-rich codons are disfavored when mutations are GC-biased, and the opposite is true when mutations are AT-biased. This indicates that selection on the genomic coding regions might act primarily to stabilize its GC/AT content on a genome-wide level. Our study shows that the degree of synonymous codon usage varies considerably among animals, but is likely governed by a common underlying dynamic.

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder

The nearly neutral theory of molecular evolution posits variation among species in the effectiveness of selection. In an idealized model, the census population size determines both this minimum magnitude of the selection coefficient required for deleterious variants to be reliably purged, and the amount of neutral diversity. Empirically, an "effective population size" is often estimated from the amount of putatively neutral genetic diversity and is assumed to also capture a species' effectiveness of selection. A potentially more direct measure of the effectiveness of selection is the degree to which selection maintains preferred codons. However, past metrics that compare codon bias across species are confounded by among-species variation in %GC content and/or amino acid composition. Here we propose a new Codon Adaptation Index of Species (CAIS), based on Kullback-Leibler divergence, that corrects for both confounders. We demonstrate the use of CAIS correlations, as well as the Effective Number of Codons, to show that the protein domains of more highly adapted vertebrate species evolve higher intrinsic structural disorder.

Differential Selection for Translation Efficiency Shapes Translation Machineries in Bacterial Species

Different bacterial species have dramatically different generation times, from 20-30 min in Escherichia coli to about two weeks in Mycobacterium leprae. The translation machinery in a cell needs to synthesize all proteins for a new cell in each generation. The three subprocesses of translation, i.e., initiation, elongation, and termination, are expected to be under stronger selection pressure to optimize in short-generation bacteria (SGB) such as Vibrio natriegens than in the long-generation Mycobacterium leprae. The initiation efficiency depends on the start codon decoded by the initiation tRNA, the optimal Shine-Dalgarno (SD) decoded by the anti-SD (aSD) sequence on small subunit rRNA, and the secondary structure that may embed the initiation signals and prevent them from being decoded. The elongation efficiency depends on the tRNA pool and codon usage. The termination efficiency in bacteria depends mainly on the nature of the stop codon and the nucleotide immediately downstream of the stop codon. By contrasting SGB with long-generation bacteria (LGB), we predict (1) SGB to have more ribosome RNA operons to produce ribosomes, and more tRNA genes for carrying amino acids to ribosomes, (2) SGB to have a higher percentage of genes using AUG as the start codon and UAA as the stop codon than LGB, (3) SGB to exhibit better codon and anticodon adaptation than LGB, and (4) SGB to have a weaker secondary structure near the translation initiation signals than LGB. These differences between SGB and LGB should be more pronounced in highly expressed genes than the rest of the genes. We present empirical evidence in support of these predictions.

Large-scale duplication events underpin population-level flexibility in tRNA gene copy number in Pseudomonas fluorescens SBW25

The complement of tRNA genes within a genome is typically considered to be a (relatively) stable characteristic of an organism. Here, we demonstrate that bacterial tRNA gene set composition can be more flexible than previously appreciated, particularly regarding tRNA gene copy number. We report the high-rate occurrence of spontaneous, large-scale, tandem duplication events in laboratory populations of the bacterium Pseudomonas fluorescens SBW25. The identified duplications are up to ∼1 Mb in size (∼15% of the wildtype genome) and are predicted to change the copy number of up to 917 genes, including several tRNA genes. The observed duplications are inherently unstable: they occur, and are subsequently lost, at extremely high rates. We propose that this unusually plastic type of mutation provides a mechanism by which tRNA gene set diversity can be rapidly generated, while simultaneously preserving the underlying tRNA gene set in the absence of continued selection. That is, if a tRNA set variant provides no fitness advantage, then high-rate segregation of the duplication ensures the maintenance of the original tRNA gene set. However, if a tRNA gene set variant is beneficial, the underlying duplication fragment(s) may persist for longer and provide raw material for further, more stable, evolutionary change.

Codon usage bias and phylogenetic analysis of chloroplast genome in 36 gracilariaceae species

Gracilariaceae is a group of marine large red algae and main source of agar with important economic and ecological value. The codon usage patterns of chloroplast genomes in 36 species from Graciliaceae show that GC range from 0.284 to 0.335, the average GC3 range from 0.135 to 0.243 and the value of ENC range from 35.098 to 42.327, which indicates these genomes are rich in AT and prefer to use codons ending with AT in these species. Nc plot, PR2 plot, neutrality plot analyses and correlation analysis indicate that these biases may be caused by multiple factors, such as natural selection and mutation pressure, but prolonged natural selection is the main driving force influencing codon usage preference. The cluster analysis and phylogenetic analysis show that the differentiation relationship of them is different and indicate that codons with weak or unbiased preferences may also play an irreplaceable role in these species' evolution. In addition, we identified 26 common high-frequency codons and 8-18 optimal codons all ending in A/U in these 36 species. Our results will not only contribute to carrying out transgenic work in Gracilariaceae species to maximize the protein yield in the future, but also lay a theoretical foundation for further exploring systematic classification of them.

Fitness benefits of a synonymous substitution in an ancient EF-Tu gene depend on the genetic background

Synonymous mutations are changes to DNA sequence, which occur within translated genes but which do not affect the protein sequence. Although often referred to as silent mutations, evidence suggests that synonymous mutations can affect gene expression, mRNA stability, and even translation efficiency. A collection of both experimental and bioinformatic data has shown that synonymous mutations can impact cell phenotype, yet less is known about the molecular mechanisms and potential of beneficial or adaptive effects of such changes within evolved populations. Here, we report a beneficial synonymous mutation acquired via experimental evolution in an essential gene variant encoding the translation elongation factor protein EF-Tu. We demonstrate that this particular synonymous mutation increases EF-Tu mRNA and protein levels as well as global polysome abundance on RNA transcripts. Although presence of the synonymous mutation is clearly causative of such changes, we also demonstrate that fitness benefits are highly contingent on other potentiating mutations present within the genetic background in which the mutation arose. Our results underscore the importance of beneficial synonymous mutations, especially those that affect levels of proteins that are key for cellular processes.IMPORTANCEThis study explores the degree to which synonymous mutations in essential genes can influence adaptation in bacteria. An experimental system whereby an Escherichia coli strain harboring an engineered translation protein elongation factor-Tu (EF-Tu) was subjected to laboratory evolution. We find that a synonymous mutation acquired on the gene encoding for EF-Tu is conditionally beneficial for bacterial fitness. Our findings provide insight into the importance of the genetic background when a synonymous substitution is favored by natural selection and how such changes have the potential to impact evolution when critical cellular processes are involved.

Pseudogenes act as a neutral reference for detecting selection in prokaryotic pangenomes

A long-standing question is to what degree genetic drift and selection drive the divergence in rare accessory gene content between closely related bacteria. Rare genes, including singletons, make up a large proportion of pangenomes (all genes in a set of genomes), but it remains unclear how many such genes are adaptive, deleterious or neutral to their host genome. Estimates of species' effective population sizes (Ne) are positively associated with pangenome size and fluidity, which has independently been interpreted as evidence for both neutral and adaptive pangenome models. We hypothesized that pseudogenes, used as a neutral reference, could be used to distinguish these models. We find that most functional categories are depleted for rare pseudogenes when a genome encodes only a single intact copy of a gene family. In contrast, transposons are enriched in pseudogenes, suggesting they are mostly neutral or deleterious to the host genome. Thus, even if individual rare accessory genes vary in their effects on host fitness, we can confidently reject a model of entirely neutral or deleterious rare genes. We also define the ratio of singleton intact genes to singleton pseudogenes (si/sp) within a pangenome, compare this measure across 668 prokaryotic species and detect a signal consistent with the adaptive value of many rare accessory genes. Taken together, our work demonstrates that comparing with pseudogenes can improve inferences of the evolutionary forces driving pangenome variation.

No Evidence that Selection on Synonymous Codon Usage Affects Patterns of Protein Evolution in Bacteria

Bias in synonymous codon usage has been reported across all kingdoms of life. Evidence suggests that codon usage bias is often driven by selective pressures, typically for translational efficiency. These selective pressures have been shown to depress the rate at which synonymous sites evolve. We hypothesize that selection on synonymous codon use could also slow the rate of protein evolution if a non-synonymous mutation changes the codon from being preferred to unpreferred. We test this hypothesis by looking at patterns of protein evolution using polymorphism and substitution data in two bacterial species, Escherichia coli and Streptococcus pneumoniae. We find no evidence that non-synonymous mutations that change a codon from being unpreferred to preferred are more common than the opposite. Overall, selection on codon bias seems to have little influence over non-synonymous polymorphism or substitution patterns.

Codon usage bias in chloroplast genes implicate adaptive evolution of four ginger species

Codon usage bias (CUB) refers to different codons exhibiting varying frequencies of usage in the genome. Studying CUB is crucial for understanding genome structure, function, and evolutionary processes. Herein, we investigated the codon usage patterns and influencing factors of protein-coding genes in the chloroplast genomes of four sister genera (monophyletic Roscoea and Cautleya, and monophyletic Pommereschea and Rhynchanthus) from the Zingiberaceae family with contrasting habitats in southwestern China. These genera exhibit distinct habitats, providing a unique opportunity to explore the adaptive evolution of codon usage. We conducted a comprehensive analysis of nucleotide composition and codon usage on protein-coding genes in the chloroplast genomes. The study focused on understanding the relationship between codon usage and environmental adaptation, with a particular emphasis on genes associated with photosynthesis. Nucleotide composition analysis revealed that the overall G/C content of the coding genes was ˂ 48%, indicating an enrichment of A/T bases. Additionally, synonymous and optimal codons were biased toward ending with A/U bases. Natural selection is the primary factor influencing CUB characteristics, particularly photosynthesis-associated genes. We observed differential gene expressions related to light adaptation among sister genera inhabiting different environments. Certain codons were favored under specific conditions, possibly contributing to gene expression regulation in particular environments. This study provides insights into the adaptive evolution of these sister genera by analyzing CUB and offers theoretical assistance for understanding gene expression and regulation. In addition, the data support the relationship between RNA editing and CUB, and the findings shed light on potential research directions for investigating adaptive evolution.

Bacteria can maintain rRNA operons solely on plasmids for hundreds of millions of years

It is generally assumed that all bacteria must have at least one rRNA operon (rrn operon) on the chromosome, but some strains of the genera Aureimonas and Oecophyllibacter carry their sole rrn operon on a plasmid. However, other related strains and species have chromosomal rrn loci, suggesting that the exclusive presence of rrn operons on a plasmid is rare and unlikely to be stably maintained over long evolutionary periods. Here, we report the results of a systematic search for additional bacteria without chromosomal rrn operons. We find that at least four bacterial clades in the phyla Bacteroidota, Spirochaetota, and Pseudomonadota (Proteobacteria) lost chromosomal rrn operons independently. Remarkably, Persicobacteraceae have apparently maintained this peculiar genome organization for hundreds of millions of years. In our study, all the rrn-carrying plasmids in bacteria lacking chromosomal rrn loci possess replication initiator genes of the Rep_3 family. Furthermore, the lack of chromosomal rrn operons is associated with differences in copy numbers of rrn operons, plasmids, and chromosomal tRNA genes. Thus, our findings indicate that the absence of rrn loci in bacterial chromosomes can be stably maintained over long evolutionary periods.

Genes for highly abundant proteins in Escherichia coli avoid 5' codons that promote ribosomal initiation

In many species highly expressed genes (HEGs) over-employ the synonymous codons that match the more abundant iso-acceptor tRNAs. Bacterial transgene codon randomization experiments report, however, that enrichment with such "translationally optimal" codons has little to no effect on the resultant protein level. By contrast, consistent with the view that ribosomal initiation is rate limiting, synonymous codon usage following the 5' ATG greatly influences protein levels, at least in part by modifying RNA stability. For the design of bacterial transgenes, for simple codon based in silico inference of protein levels and for understanding selection on synonymous mutations, it would be valuable to computationally determine initiation optimality (IO) scores for codons for any given species. One attractive approach is to characterize the 5' codon enrichment of HEGs compared with the most lowly expressed genes, just as translational optimality scores of codons have been similarly defined employing the full gene body. Here we determine the viability of this approach employing a unique opportunity: for Escherichia coli there is both the most extensive protein abundance data for native genes and a unique large-scale transgene codon randomization experiment enabling objective definition of the 5' codons that cause, rather than just correlate with, high protein abundance (that we equate with initiation optimality, broadly defined). Surprisingly, the 5' ends of native genes that specify highly abundant proteins avoid such initiation optimal codons. We find that this is probably owing to conflicting selection pressures particular to native HEGs, including selection favouring low initiation rates, this potentially enabling high efficiency of ribosomal usage and low noise. While the classical HEG enrichment approach does not work, rendering simple prediction of native protein abundance from 5' codon content futile, we report evidence that initiation optimality scores derived from the transgene experiment may hold relevance for in silico transgene design for a broad spectrum of bacteria.

Codon usage pattern of the ancestor of green plants revealed through Rhodophyta

Rhodophyta are among the closest known relatives of green plants. Studying the codons of their genomes can help us understand the codon usage pattern and characteristics of the ancestor of green plants. By studying the codon usage pattern of all available red algae, it was found that although there are some differences among species, high-bias genes in most red algae prefer codons ending with GC. Correlation analysis, Nc-GC3s plots, parity rule 2 plots, neutrality plot analysis, differential protein region analysis and comparison of the nucleotide content of introns and flanking sequences showed that the bias phenomenon is likely to be influenced by local mutation pressure and natural selection, the latter of which is the dominant factor in terms of translation accuracy and efficiency. It is worth noting that selection on translation accuracy could even be detected in the low-bias genes of individual species. In addition, we identified 15 common optimal codons in seven red algae except for G. sulphuraria for the first time, most of which were found to be complementary and bound to the tRNA genes with the highest copy number. Interestingly, tRNA modification was found for the highly degenerate amino acids of all multicellular red algae and individual unicellular red algae, which indicates that highly biased genes tend to use modified tRNA in translation. Our research not only lays a foundation for exploring the characteristics of codon usage of the red algae as green plant ancestors, but will also facilitate the design and performance of transgenic work in some economic red algae in the future.

A beneficial synonymous substitution in EF-Tu is contingent on genetic background

Synonymous mutations are changes to DNA sequence that occur within translated genes but which do not affect the protein sequence. Although often referred to as silent mutations, evidence suggests that synonymous mutations can affect gene expression, mRNA stability, and even translation efficiency. A collection of both experimental and bioinformatic data has shown that synonymous mutations can impact cell phenotype, yet less is known about the molecular mechanisms and potential of beneficial or adaptive effects of such changes within evolved populations. Here, we report a beneficial synonymous mutation acquired via experimental evolution in an essential gene variant encoding the translation Elongation Factor protein EF-Tu. We demonstrate that this particular synonymous mutation increases EF-Tu mRNA and protein levels, as well as the polysome abundance on global transcripts. Although presence of the synonymous mutation is clearly causative of such changes, we also demonstrate that fitness benefits are highly contingent on other potentiating mutations present within the genetic background in which the mutation arose. Our results underscore the importance of beneficial synonymous mutations, especially those that affect levels of proteins that are key for cellular processes.

The First Five Mitochondrial Genomes for the Family Nidulariaceae Reveal Novel Gene Rearrangements, Intron Dynamics, and Phylogeny of Agaricales

The family Nidulariaceae, consisting of five genera including Cyathus, is a unique group of mushrooms commonly referred to as bird's nest fungi due to their striking resemblance to bird's nests. These mushrooms are considered medicinal mushrooms in Chinese medicine and have received attention in recent years for their anti-neurodegenerative properties. However, despite the interest in these mushrooms, very little is known about their mitochondrial genomes (mitogenomes). This study is the first comprehensive investigation of the mitogenomes of five Nidulariaceae species with circular genome structures ranging in size from 114,236 bp to 129,263 bp. Comparative analyses based on gene content, gene length, tRNA, and codon usage indicate convergence within the family Nidulariaceae and heterogeneity within the order Agaricales. Phylogenetic analysis based on a combined mitochondrial conserved protein dataset provides a well-supported phylogenetic tree for the Basidiomycetes, which clearly demonstrates the evolutionary relationships between Nidulariaceae and other members of Agaricales. Furthermore, phylogenetic inferences based on four different gene sets reveal the stability and proximity of evolutionary relationships within Agaricales. These results reveal the uniqueness of the family Nidulariaceae and its similarity to other members of Agaricales; provide valuable insights into the origin, evolution, and genetics of Nidulariaceae species; and enrich the fungal mitogenome resource. This study will help to expand the knowledge and understanding of the mitogenomes in mushrooms.

gtAI: an improved species-specific tRNA adaptation index using the genetic algorithm

The tRNA adaptation index (tAI) is a translation efficiency metric that considers weighted values (S _ij values) for codon-tRNA wobble interaction efficiencies. The initial implementation of the tAI had significant flaws. For instance, generated S _ij weights were optimized based on gene expression in Saccharomyces cerevisiae, which is expected to vary among different species. Consequently, a species-specific approach (stAI) was developed to overcome those limitations. However, the stAI method employed a hill climbing algorithm to optimize the S _ij weights, which is not ideal for obtaining the best set of S _ij weights because it could struggle to find the global maximum given a complex search space, even after using different starting positions. In addition, it did not perform well in computing the tAI of fungal genomes in comparison with the original implementation. We developed a novel approach named genetic tAI (gtAI) implemented as a Python package (https://github.com/AliYoussef96/gtAI), which employs a genetic algorithm to obtain the best set of S _ij weights and follows a new codon usage-based workflow that better computes the tAI of genomes from the three domains of life. The gtAI has significantly improved the correlation with the codon adaptation index (CAI) and the prediction of protein abundance (empirical data) compared to the stAI.

Deafness gene screening based on a multilevel cascaded BPNN model

Sudden sensorineural hearing loss is a common and frequently occurring condition in otolaryngology. Existing studies have shown that sudden sensorineural hearing loss is closely associated with mutations in genes for inherited deafness. To identify these genes associated with deafness, researchers have mostly used biological experiments, which are accurate but time-consuming and laborious. In this paper, we proposed a computational method based on machine learning to predict deafness-associated genes. The model is based on several basic backpropagation neural networks (BPNNs), which were cascaded as multiple-level BPNN models. The cascaded BPNN model showed a stronger ability for screening deafness-associated genes than the conventional BPNN. A total of 211 of 214 deafness-associated genes from the deafness variant database (DVD v9.0) were used as positive data, and 2110 genes extracted from chromosomes were used as negative data to train our model. The test achieved a mean AUC higher than 0.98. Furthermore, to illustrate the predictive performance of the model for suspected deafness-associated genes, we analyzed the remaining 17,711 genes in the human genome and screened the 20 genes with the highest scores as highly suspected deafness-associated genes. Among these 20 predicted genes, three genes were mentioned as deafness-associated genes in the literature. The analysis showed that our approach has the potential to screen out highly suspected deafness-associated genes from a large number of genes, and our predictions could be valuable for future research and discovery of deafness-associated genes.

Genomic Legacies of Ancient Adaptation Illuminate GC-Content Evolution in Bacteria

Bacterial evolution is characterized by strong purifying selection as well as rapid adaptive evolution in changing environments. In this context, the genomic GC content (genomic GC) varies greatly but presents some level of phylogenetic stability, making it challenging to explain based on current hypotheses. To illuminate the evolutionary mechanisms of the genomic GC, we analyzed the base composition and functional inventory of 11,083 representative genomes. A phylogenetically constrained bimodal distribution of the genomic GC, which mainly originated from parallel divergences in the early evolution, was demonstrated. Such variation of the genomic GC can be well explained by DNA replication and repair (DRR), in which multiple pathways correlate with the genomic GC. Furthermore, the biased conservation of various stress-related genes, especially the DRR-related ones, implies distinct adaptive processes in the ancestral lineages of high- or low-GC clades which are likely induced by major environmental changes. Our findings support that the mutational biases resulting from these legacies of ancient adaptation have changed the course of adaptive evolution and generated great variation in the genomic GC. This highlights the importance of indirect effects of natural selection, which indicates a new model for bacterial evolution. IMPORTANCE GC content has been shown to be an important factor in microbial ecology and evolution, and the genomic GC of bacteria can be characterized by great intergenomic heterogeneity, high intragenomic homogeneity, and strong phylogenetic inertia, as well as being associated with the environment. Current hypotheses concerning direct selection or mutational biases cannot well explain these features simultaneously. Our findings of the genomic GC showing that ancient adaptations have transformed the DRR system and that the resulting mutational biases further contributed to a bimodal distribution of it offer a more reasonable scenario for the mechanism. This would imply that, when thinking about the evolution of life, diverse processes of adaptation exist, and combined effects of natural selection should be considered.

Biofilm forming multidrug resistant Staphylococcus aureus of dairy origin: molecular and evolutionary perspectives

Background

Biofilm production by Staphylococcus aureus is a prevailing cause of multidrug resistance. The evolutionary mechanisms of adaption with host and pathogenicity are poorly understood.

Aims

The present study aimed to investigate the biofilm-forming potential, associated multidrug resistance, and the evolutionary analysis of S. aureus isolated from bovine subclinical mastitis.

Methods

122 S. aureus isolates were subjected to Congo red agar method (CRA), microtitre plate method (MTP), and PCR to check the biofilm-forming potential. The Kirby-Bauer disk diffusion method was used to evaluate the antibiotic resistance pattern. The icaA gene of isolates was subjected to molecular and evolutionary analysis using different bioinformatics tools.

Results

The results showed that 63.93% of S. aureus isolates carried the icaA gene and the detection rate of CRA was higher (36.07%) compared to the MTP test (24.59%). A total of 78.21% and 56.41% of biofilm-positive isolates were methicillin-resistant S. aureus (MRSA) and vancomycin-resistant S. aureus (VRSA), respectively. All S. aureus isolates (100%) showed multidrug resistance. The molecular analysis showed an evolutionary link between isolates and revealed a strong codon bias, three different recombination events, and positive selection in some residues of the semi-conserved segments of the icaA gene.

Conclusion

The study concluded that biofilm-positive isolates have a high tendency to exhibit methicillin, vancomycin, and multidrug resistance. The findings suggest that mutation and selection are the most likely causes of codon bias in the icaA gene sequences. The variations led by recombination events and positive selection are suggestive of bacterial strategy to combat antimicrobial effects and to escape the host's immune surveillance.

Markerless Genome Editing in Competent Streptococci

Selective markers employed in classical mutagenesis methods using natural genetic transformation can affect gene expression, risk phenotypic effects, and accumulate as unwanted genes during successive mutagenesis cycles. In this chapter, we present a protocol for markerless genome editing in Streptococcus mutans and Streptococcus pneumoniae achieved with an efficient method for natural transformation. High yields of transformants are obtained by combining the unimodal state of competence developed after treatment of S. mutans with sigX-inducing peptide pheromone (XIP) in a chemically defined medium (CDM) or of S. pneumoniae with the competence-stimulating peptide (CSP) together with use of a donor amplicon carrying extensive flanking homology. This combination ensures efficient and precise integration of a new allele by the recombination machinery present in competent cells.

Evaluation of machine learning classifiers for predicting essential genes in Mycobacterium tuberculosis strains

Accurate investigation and prediction of essential genes from bacterial genome is very important as it might be explored in effective targets for antimicrobial drugs and understanding biological mechanism of a cell. A subset of key features data obtained from 14 genome sequence-based features of 20 strains of Mycobacterium tuberculosis bacteria whose essential gene information was downloaded from ePath and NCBI database for mapping and matching essential genes by using a genome extraction program. The selection of key features was performed by using Genetic Algorithm. For each of three classifiers, 80%, 10% and 10% of subset key features were used for training, validation and testing, respectively. Experimental results (10-f-cv) illustrated that DNN (proposed), DT, and SVM achieved AUC of 0.98, 0.88 and 0.82, respectively. DNN (proposed) outperformed DT and SVM. The higher prediction accuracy of classifiers was observed because of using only key features which also justified better generalizability of classifiers and efficiency of key features related to gene essentiality. Besides, DNN (proposed) also showed best prediction performance while compared with other predictors used in previous studies. The genome extraction program was developed for mapping and matching of essential genes between ePath and NCBI database.

Unsupervised Machine Learning Organization of the Functional Dark Proteome of Gram-Negative "Superbugs": Six Protein Clusters Amenable for Distinct Scientific Applications

Uncharacterized proteins have been underutilized as targets for the development of novel therapeutics for difficult-to-treat bacterial infections. To facilitate the exploration of these proteins, 2819 predicted, uncharacterized proteins (19.1% of the total) from reference strains of multidrug Acinetobacter baumannii, Klebsiella pneumoniae, and Pseudomonas aeruginosa species were organized using an unsupervised k-means machine learning algorithm. Classification using normalized values for protein length, pI, hydrophobicity, degree of conservation, structural disorder, and %AT of the coding gene rendered six natural clusters. Cluster proteins showed different trends regarding operon membership, expression, presence of unknown function domains, and interactomic relevance. Clusters 2, 4, and 5 were enriched with highly disordered proteins, nonworkable membrane proteins, and likely spurious proteins, respectively. Clusters 1, 3, and 6 showed closer distances to known antigens, antibiotic targets, and virulence factors. Up to 21.8% of proteins in these clusters were structurally covered by modeling, which allowed assessment of druggability and discontinuous B-cell epitopes. Five proteins (4 in Cluster 1) were potential druggable targets for antibiotherapy. Eighteen proteins (11 in Cluster 6) were strong B-cell and T-cell immunogen candidates for vaccine development. Conclusively, we provide a feature-based schema to fractionate the functional dark proteome of critical pathogens for fundamental and biomedical purposes.

Analysis of the Compositional Features and Codon Usage Pattern of Genes Involved in Human Autophagy

Autophagy plays an intricate role in paradigmatic human pathologies such as cancer, and neurodegenerative, cardiovascular, and autoimmune disorders. Autophagy regulation is performed by a set of autophagy-related (ATG) genes, first recognized in yeast genome and subsequently identified in other species, including humans. Several other genes have been identified to be involved in the process of autophagy either directly or indirectly. Studying the codon usage bias (CUB) of genes is crucial for understanding their genome biology and molecular evolution. Here, we examined the usage pattern of nucleotide and synonymous codons and the influence of evolutionary forces in genes involved in human autophagy. The coding sequences (CDS) of the protein coding human autophagy genes were retrieved from the NCBI nucleotide database and analyzed using various web tools and software to understand their nucleotide composition and codon usage pattern. The effective number of codons (ENC) in all genes involved in human autophagy ranges between 33.26 and 54.6 with a mean value of 45.05, indicating an overall low CUB. The nucleotide composition analysis of the autophagy genes revealed that the genes were marginally rich in GC content that significantly influenced the codon usage pattern. The relative synonymous codon usage (RSCU) revealed 3 over-represented and 10 under-represented codons. Both natural selection and mutational pressure were the key forces influencing the codon usage pattern of the genes involved in human autophagy.

Bioinformatic Assessment of Factors Affecting the Correlation between Protein Abundance and Elongation Efficiency in Prokaryotes

Protein abundance is crucial for the majority of genetically regulated cell functions to act properly in prokaryotic organisms. Therefore, developing bioinformatic methods for assessing the efficiency of different stages of gene expression is of great importance for predicting the actual protein abundance. One of these steps is the evaluation of translation elongation efficiency based on mRNA sequence features, such as codon usage bias and mRNA secondary structure properties. In this study, we have evaluated correlation coefficients between experimentally measured protein abundance and predicted elongation efficiency characteristics for 26 prokaryotes, including non-model organisms, belonging to diverse taxonomic groups The algorithm for assessing elongation efficiency takes into account not only codon bias, but also number and energy of secondary structures in mRNA if those demonstrate an impact on predicted elongation efficiency of the ribosomal protein genes. The results show that, for a number of organisms, secondary structures are a better predictor of protein abundance than codon usage bias. The bioinformatic analysis has revealed several factors associated with the value of the correlation coefficient. The first factor is the elongation efficiency optimization type-the organisms whose genomes are optimized for codon usage only have significantly higher correlation coefficients. The second factor is taxonomical identity-bacteria that belong to the class Bacilli tend to have higher correlation coefficients among the analyzed set. The third is growth rate, which is shown to be higher for the organisms with higher correlation coefficients between protein abundance and predicted translation elongation efficiency. The obtained results can be useful for further improvement of methods for protein abundance prediction.

Translation Comes First: Ancient and Convergent Selection of Codon Usage Bias Across Prokaryotic Genomes

Codon usage is the outcome of different evolutionary processes and can inform us about the conditions in which organisms live and evolve. Here, we present R_ENC', which is an improvement to the original S index developed by dos Reis et al. (2004). Our index is less sensitive to G+C content, which greatly affects synonymous codon usage in prokaryotes, making it better suited to detect selection acting on codon usage. We used R_ENC' to estimate the extent of selected codon usage bias in 1800 genomes representing 26 prokaryotic phyla. We found that Gammaproteobacteria, Betaproteobacteria, Actinobacteria, and Firmicutes are the phyla/subphyla showing more genomes with selected codon usage bias. In particular, we found that several lineages within Gammaproteobacteria and Firmicutes show a similar set of functional terms enriched in genes under selected codon usage bias, indicating convergent evolution. We also show that selected codon usage bias tends to evolve in genes coding for the translation machinery before other functional GO terms. Finally, we discuss the possibility to use R_ENC' to predict whether lineages evolved in copiotrophic or oligotrophic environments.

Pseudocitrobacter corydidari sp. nov., isolated from the Asian emerald cockroach Corydidarum magnifica

A Gram-negative bacterial strain, G163CMT, was isolated from the gut of the Asian emerald cockroach Corydidarum magnifica. The 16S rRNA gene sequence (1416 bp) of strain G163CMT showed 99.22% similarity to Pseudocitrobacter faecalis CCM 8479T and Pseudocitrobacter vendiensis CPO20170097T. The average nucleotide identity, digital DNA–DNA hybridization and average amino acid identity values of strain G163CMT were 92.4, 48.8 and 95.7% to P. faecalis CCM 8479T, and 93.3, 52.4 and 95.7% to P. vendiensis CPO20170097T. This strongly supports the designation of G163CMT as representing a new species in the genus Pseudocitrobacter . Phylogenetic trees based on the alignment of 16S rRNA, multilocus sequence analysis of six single-copy genes (fusA, pyrG, leuS, rpoB, recN and mnmE) and 107 core protein sequences consistently showed G163CMT to be a member of the genus Pseudocitrobacter , closely related to P. vendiensis CPO20170097T. In contrast to P. faecalis CCM 8479T and P. vendiensis CPO20170097T, the genome of G163CMT did not encode for proteins conferring resistance to antibiotics. However, all three genomes encoded a similar number of virulence factors and specialized metabolite biosynthetic proteins. The major fatty acids of strain G163CMT were C16:0 (31.5 %), C18:1 ω7c (22.6 %), C17:0 cyclo (15.3 %) and C14:0 (6.5 %). Based on the polyphasic results, we conclude that strain G163CMT represents a novel species of the genus Pseudocitrobacter and we propose the name Pseudocitrobacter corydidari sp. nov. with the type strain G163CMT (=DSM 112648T=CCM 9160T).

Whole genome sequencing and comparative genomic analyses of Pseudomonas aeruginosa strain isolated from arable soil reveal novel insights into heavy metal resistance and codon biology

Elevated concentration of non-essential persistent heavy metals and metalloids in the soil is detrimental to essential soil microbes and plants, resulting in diminished diversity and biomass. Thus, isolation, screening, and whole genomic analysis of potent strains of bacteria from arable lands with inherent capabilities of heavy metal resistance and plant growth promotion hold the key for bio remedial applications. This study is an attempt to do the same. In this study, a potent strain of Pseudomonas aeruginosa was isolated from paddy fields, followed by metabolic profiling using FTIR, metal uptake analysis employing ICP-MS, whole genome sequencing and comparative codon usage analysis. ICP-MS study provided insights into a high degree of Cd uptake during the exponential phase of growth under cumulative metal stress to Cd, Zn and Co, which was further corroborated by the detection of cadA gene along with czcCBA operon in the genome upon performing whole-genome sequencing. This potent strain of Pseudomonas aeruginosa also harboured genes, such as copA, chrA, znuA, mgtE, corA, and others conferring resistance against different heavy metals, such as Cd, Zn, Co, Cu, Cr, etc. A comparative codon usage bias analysis at the genomic and genic level, whereby several heavy metal resistant genes were considered in the backdrop of two housekeeping genes among 40 Pseudomonas spp. indicated the presence of a relatively strong codon usage bias in the studied strain. With this work, an effort was made to explore heavy metal-resistant bacteria (isolated from arable soil) and whole genome sequence analysis to get insight into metal resistance for future bio remedial applications.

Intragenomic variation in non-adaptive nucleotide biases causes underestimation of selection on synonymous codon usage

Patterns of non-uniform usage of synonymous codons vary across genes in an organism and between species across all domains of life. This codon usage bias (CUB) is due to a combination of non-adaptive (e.g. mutation biases) and adaptive (e.g. natural selection for translation efficiency/accuracy) evolutionary forces. Most models quantify the effects of mutation bias and selection on CUB assuming uniform mutational and other non-adaptive forces across the genome. However, non-adaptive nucleotide biases can vary within a genome due to processes such as biased gene conversion (BGC), potentially obfuscating signals of selection on codon usage. Moreover, genome-wide estimates of non-adaptive nucleotide biases are lacking for non-model organisms. We combine an unsupervised learning method with a population genetics model of synonymous coding sequence evolution to assess the impact of intragenomic variation in non-adaptive nucleotide bias on quantification of natural selection on synonymous codon usage across 49 Saccharomycotina yeasts. We find that in the absence of a priori information, unsupervised learning can be used to identify genes evolving under different non-adaptive nucleotide biases. We find that the impact of intragenomic variation in non-adaptive nucleotide bias varies widely, even among closely-related species. We show that the overall strength and direction of translational selection can be underestimated by failing to account for intragenomic variation in non-adaptive nucleotide biases. Interestingly, genes falling into clusters identified by machine learning are also physically clustered across chromosomes. Our results indicate the need for more nuanced models of sequence evolution that systematically incorporate the effects of variable non-adaptive nucleotide biases on codon frequencies.

Establishment of Genome Based Criteria for Classification of the Family Desulfovibrionaceae and Proposal of Two Novel Genera, Alkalidesulfovibrio gen. nov. and Salidesulfovibrio gen. nov

Bacteria in the Desulfovibrionaceae family, which contribute to S element turnover as sulfate-reducing bacteria (SRB) and disproportionation of partially oxidized sulfoxy anions, have been extensively investigated since the importance of the sulfur cycle emerged. Novel species belonging to this taxon are frequently reported, because they exist in various environments and are easy to culture using established methods. Due to the rapid expansion of the taxon, correction and reclassification have been conducted. The development of high-throughput sequencing facilitated rapid expansion of genome sequence database. Genome-based criteria, based on these databases, proved to be potential classification standard by overcoming the limitations of 16S rRNA-based phylogeny. Although standards methods for taxogenomics are being established, the addition of a novel genus requires extensive calculations with taxa, including many species, such as Desulfovibrionaceae. Thus, the genome-based criteria for classification of Desulfovibrionaceae were established and validated in this study. The average amino-acid identity (AAI) cut-off value, 63.43 ± 0.01, was calculated to be an appropriate criterion for genus delineation of the family Desulfovibrionaceae. By applying the AAI cut-off value, 88 genomes of the Desulfovibrionaceae were divided into 27 genera, which follows the core gene phylogeny results. In this process, two novel genera (Alkalidesulfovibrio and Salidesulfovibrio) and one former invalid genus (“Psychrodesulfovibrio”) were officially proposed. Further, by applying the 95–96% average nucleotide identity (ANI) standard and the 70% digital DNA–DNA hybridization standard values for species delineation of strains that were classified as the same species, five strains have the potential to be newly classified. After verifying that the classification was appropriately performed through relative synonymous codon usage analysis, common characteristics were listed by group. In addition, by detecting metal resistance related genes via in silico analysis, it was confirmed that most strains display metal tolerance.

The First Mitochondrial Genome of Ciborinia camelliae and Its Position in the Sclerotiniaceae Family

Ciborinia camelliae is the causal agent of camellia flower blight (CFB). It is a hemibiotrophic pathogen, inoperculate Discomycete of the family Sclerotiniaceae. It shows host and organ specificity infecting only flowers of species belonging to the genus Camellia, causing serious damage to the ornamental component of the plant. In this work, the first mitochondrial genome of Ciborinia camellia is reported. The mitogenome was obtained by combining Illumina short read and Nanopore long read technology. To resolve repetitive elements, specific primers were designed and used for Sanger sequencing. The manually curated mitochondrial DNA (mtDNA) of the Italian strain DSM 112729 is a circular sequence of 114,660 bp, with 29.6% of GC content. It contains two ribosomal RNA genes, 33 transfer RNAs, one RNase P gene, and 62 protein-coding genes. The latter include one gene coding for a ribosomal protein (rps3) and the 14 typical proteins involved in the oxidative metabolism. Moreover, a partial mtDNA assembled from a contig list was obtained from the deposited genome assembly of a New Zealand strain of C. camelliae. The present study contributes to understanding the mitogenome arrangement and the evolution of this phytopathogenic fungus in comparison to other Sclerotiniaceae species and confirms the usefulness of mitochondrial analysis to define phylogenetic positioning of this newly sequenced species.

Context-Dependent Mutation Dynamics, Not Selection, Explains the Codon Usage Bias of Most Angiosperm Chloroplast Genes

Two competing proposals about the degree to which selection affects codon usage of angiosperm chloroplast genes are examined. The first, based on observations that codon usage does not match expectations under the naïve assumption that base composition will be identical at all neutral sites, is that selection plays a significant role. The second is that codon usage is determined almost solely by mutation bias and drift, with selection influencing only one or two highly expressed genes, in particular psbA. First it is shown that, as a result of an influence of neighboring base composition on mutation dynamics, compositional biases are expected to be widely divergent at different sites in the absence of selection. The observed mutation properties are then used to predict expected neutral codon usage biases and to show that observed deviations from the naïve expectations are in fact expected given the context-dependent mutational dynamics. It is also shown that there is a match between the observed and expected codon usage when context effects are taken into consideration, with psbA being a notable exception. Overall, the data support the model that selection is not a widespread factor affecting the codon usage of angiosperm chloroplast genes and highlight the need to have an accurate model of mutational dynamics.

Intragenic codon usage in proteobacteria: Translational selection, IS expansion and genomic shrinkage

This manuscript presents a method to systematically study intragenic variations in codon usage using correspondence analysis and the effective number of codons. The method is applied to >1100 proteobacteria. Codon usage biases (measured as inertia) increases with genome size, the same is true for the percentage of inertia explained by the first axis. It is shown that there is often a relaxed or more uniform codon usage near the gene termini. Ithis is not seen n small genomes, notably those of intracellular organisms like Buchnera aphidicola or Rickettsia prowazekii where translational selection plays less of a role. When genes from E. coli, for which translational selection is well described, are split into low, intermediate and high expression, respectively, it is shown that the intragenic codon usage pattern with more uniform usage at termini exist across all three expression groups. Furthermore, the correspondence analysis reveals a unique pattern in Bordetella pertussis due to IS expansion. This study thus shows that translational selection, genome shrinkage and IS expansion result in characteristic patterns in intragenic codon usage.

Codon usage in the flatworm Schistosoma mansoni is shaped by the mutational bias towards A+T and translational selection, which increases GC-ending codons in highly expressed genes

Schistosoma mansoni is a trematode flatworm that parasitizes humans and produces a disease called bilharzia. At the genomic level, it is characterized by a low genomic GC content and an "isochore-like" structure, where GC-richest regions, mainly placed at the extremes of the chromosomes, are interspersed with low GC-regions. Furthermore, the GC-richest regions are at the same time the gene-richest, and where the most heavily expressed genes are placed. Taking these features into account, we decided to reanalyze the codon usage of this flatworm. Our results show that a) when all genes are considered together, the strong mutational bias towards A + T leads to a predominance of A/T-ending codons, b) a multivariate analysis discriminates between highly and lowly expressed genes, c) the sequences expressed at highest levels display a significant increase in G/C-ending codons, d) when comparing the molecular distances with a closely related species the synonymous distance in highly expressed genes is significantly lower than in lowly expressed sequences. Therefore, we conclude that despite previous results, which were performed with a small sample of genes, codon usage in S. mansoni is the result of two forces that operate in opposite directions: while mutational bias leads to a predominance of A/T codons, translational selection, working at the level of speed, increment G/C ending triplets.

Evidence from simulation studies for selective constraints on the codon usage of the Angiosperm psbA gene

The codon usage of the Angiosperm psbA gene is atypical for flowering plant chloroplast genes but similar to the codon usage observed in highly expressed plastid genes from some other Plantae, particularly Chlorobionta, lineages. The pattern of codon bias in these genes is suggestive of selection for a set of translationally optimal codons but the degree of bias towards these optimal codons is much weaker in the flowering plant psbA gene than in high expression plastid genes from lineages such as certain green algal groups. Two scenarios have been proposed to explain these observations. One is that the flowering plant psbA gene is currently under weak selective constraints for translation efficiency, the other is that there are no current selective constraints and we are observing the remnants of an ancestral codon adaptation that is decaying under mutational pressure. We test these two models using simulations studies that incorporate the context-dependent mutational properties of plant chloroplast DNA. We first reconstruct ancestral sequences and then simulate their evolution in the absence of selection on codon usage by using mutation dynamics estimated from intergenic regions. The results show that psbA has a significantly higher level of codon adaptation than expected while other chloroplast genes are within the range predicted by the simulations. These results suggest that there have been selective constraints on the codon usage of the flowering plant psbA gene during Angiosperm evolution.

We simulated the evolution of four photosynthesis genes that are coded by the chloroplast genome of flowering plants in order to investigate the role of natural selection. In particular we were interested in whether or not selection can influence the evolution of features that do not affect the protein coded by the gene but that do affect the expression of the gene. We developed a model of mutation and then assume that there is no selection in simulations to generate expected patterns of evolution. Importantly, our mutation model accounts for a complex feature of chloroplast DNA which is that nucleotides near the site mutating affect the type of mutation that occurs. We found that one gene in particular has not evolved as predicted by our simulations but, rather, has evolved in a manner that suggests that mutations which affect the level of gene expression have been under natural selection.

The Dynamic Codon Biaser: calculating prokaryotic codon usage biases

Bacterial genomes often reflect a bias in the usage of codons. These biases are often most notable within highly expressed genes. While deviations in codon usage can be attributed to selection or mutational biases, they can also be functional, for example controlling gene expression or guiding protein structure. Several different metrics have been developed to identify biases in codon usage. Previously we released a database, CBDB: The Codon Bias Database, in which users could retrieve precalculated codon bias data for bacterial RefSeq genomes. With the increase of bacterial genome sequence data since its release a new tool was needed. Here we present the Dynamic Codon Biaser (DCB) tool, a web application that dynamically calculates the codon usage bias statistics of prokaryotic genomes. DCB bases these calculations on 40 different highly expressed genes (HEGs) that are highly conserved across different prokaryotic species. A user can either specify an NCBI accession number or upload their own sequence. DCB returns both the bias statistics and the genome’s HEG sequences. These calculations have several downstream applications, such as evolutionary studies and phage–host predictions. The source code is freely available, and the website is hosted at www.cbdb.info.

Estimating maximal microbial growth rates from cultures, metagenomes, and single cells via codon usage patterns

Despite the wide perception that microbes have rapid growth rates, many environments like seawater and soil are often dominated by microorganisms that can only grow very slowly. Our knowledge about growth is necessarily biased toward easily culturable organisms, which tend to be those that grow fast, because microbial growth rates have traditionally been measured using laboratory growth experiments. However, how are potential growth rates distributed in nature? Using genomic data, we predicted the growth rates of over 200,000 organisms, including many as yet uncultivated species. These data reveal how current culture collections are strongly biased toward fast-growing organisms. Finally, we noticed a bimodal distribution of maximal growth rates, suggesting a natural division of microbial growth strategies into two classes.

Maximal growth rate is a basic parameter of microbial lifestyle that varies over several orders of magnitude, with doubling times ranging from a matter of minutes to multiple days. Growth rates are typically measured using laboratory culture experiments. Yet, we lack sufficient understanding of the physiology of most microbes to design appropriate culture conditions for them, severely limiting our ability to assess the global diversity of microbial growth rates. Genomic estimators of maximal growth rate provide a practical solution to survey the distribution of microbial growth potential, regardless of cultivation status. We developed an improved maximal growth rate estimator and predicted maximal growth rates from over 200,000 genomes, metagenome-assembled genomes, and single-cell amplified genomes to survey growth potential across the range of prokaryotic diversity; extensions allow estimates from 16S rRNA sequences alone as well as weighted community estimates from metagenomes. We compared the growth rates of cultivated and uncultivated organisms to illustrate how culture collections are strongly biased toward organisms capable of rapid growth. Finally, we found that organisms naturally group into two growth classes and observed a bias in growth predictions for extremely slow-growing organisms. These observations ultimately led us to suggest evolutionary definitions of oligotrophy and copiotrophy based on the selective regime an organism occupies. We found that these growth classes are associated with distinct selective regimes and genomic functional potentials.