Codon Adaptation Index Research Articles

Comprehensive analysis of codon bias in 13 Ganoderma mitochondrial genomes.

Codon usage bias is a prevalent phenomenon observed across various species and genes. However, the specific attributes of codon usage in the mitochondrial genome of Ganoderma species remain unknown. In this study, we investigated the codon bias of 12 mitochondrial core protein-coding genes (PCGs) in 9 Ganoderma species, including 13 Ganoderma strains. The codons of all Ganoderma strains showed a preference for ending in A/T. Additionally, correlations between codon base composition and the codon adaptation index (CAI), codon bias index (CBI) and frequency of optimal codons (FOP) were identified, demonstrating the impact of base composition on codon bias. Various base bias indicators were found to vary between or within Ganoderma strains, including GC3s, the CAI, the CBI, and the FOP. The results also revealed that the mitochondrial core PCGs of Ganoderma have an average effective number of codons (ENC) lower than 35, indicating strong bias toward certain codons. Evidence from neutrality plot and PR2-bias plot analysis indicates that natural selection is a major factor affecting codon bias in Ganoderma. Additionally, 11 to 22 optimal codons (ΔRSCU>0.08 and RSCU>1) were identified in 13 Ganoderma strains, with GCA, AUC, and UUC being the most widely used optimal codons in Ganoderma. By analyzing the combined mitochondrial sequences and relative synonymous codon usage (RSCU) values, the genetic relationships between or within Ganoderma strains were determined, indicating variations between them. Nevertheless, RSCU-based analysis illustrated the intra- and interspecies relationships of certain Ganoderma species. This study deepens our insight into the synonymous codon usage characteristics, genetics, and evolution of this important fungal group.

ICOR: improving codon optimization with recurrent neural networks

BackgroundIn protein sequences—as there are 61 sense codons but only 20 standard amino acids—most amino acids are encoded by more than one codon. Although such synonymous codons do not alter the encoded amino acid sequence, their selection can dramatically affect the expression of the resulting protein. Codon optimization of synthetic DNA sequences is important for heterologous expression. However, existing solutions are primarily based on choosing high-frequency codons only, neglecting the important effects of rare codons. In this paper, we propose a novel recurrent-neural-network based codon optimization tool, ICOR, that aims to learn codon usage bias on a genomic dataset of Escherichia coli. We compile a dataset of over 7,000 non-redundant, high-expression, robust genes which are used for deep learning. The model uses a bidirectional long short-term memory-based architecture, allowing for the sequential context of codon usage in genes to be learned. Our tool can predict synonymous codons for synthetic genes toward optimal expression in Escherichia coli.ResultsWe demonstrate that sequential context achieved via RNN may yield codon selection that is more similar to the host genome. Based on computational metrics that predict protein expression, ICOR theoretically optimizes protein expression more than frequency-based approaches. ICOR is evaluated on 1,481 Escherichia coli genes as well as a benchmark set of 40 select DNA sequences whose heterologous expression has been previously characterized. ICOR’s performance is measured across five metrics: the Codon Adaptation Index, GC-content, negative repeat elements, negative cis-regulatory elements, and codon frequency distribution.ConclusionsThe results, based on in silico metrics, indicate that ICOR codon optimization is theoretically more effective in enhancing recombinant expression of proteins over other established codon optimization techniques. Our tool is provided as an open-source software package that includes the benchmark set of sequences used in this study.

A multi-objective butterfly optimization algorithm for protein encoding

The integration of multiple genes to maximize protein expression levels represents an important challenge in synthetic biology. This task relies on the definition of multiple protein-coding sequences, which must be as different as possible to avoid information loss. Proteins can be encoded in different ways, using synonymous codons that translate into the same amino acid. Some codons are better suited to the host than others, thus being preferable the use of the most fitting ones. However, adopting only the most highly adapted codons would lead to very similar coding sequences. An additional criterion is given by the fact that the designed sequences must contain a suitable guanine–cytosine (GC) ratio in accordance with the characteristics of the host organism. Therefore, this biological task requires the simultaneous optimization of several, conflicting objectives. This work proposes a novel multi-objective approach for protein encoding, which tackles the problem according to a new formulation based on three objective functions: codon adaptation index, Hamming distance between sequences, and GC content. Our work extends the recent Butterfly Optimization Algorithm to multi-objective contexts, integrating problem-specific operators to boost solution quality by covering the different aspects required for accurate protein encoding. Two key structures, a taboo list and a best solution list, are defined to conduct improved searches attending to the potential improvements that each solution in the population can promote. Experiments conducted on nine real-world proteins reveal the attainment of relevant solutions from different evaluation perspectives, showing significant improvements over other single and multi-objective methods from the literature.

Analysis of synonymous codon usage patterns in mitochondrial genomes of nine Amanita species.

Codon basis is a common and complex natural phenomenon observed in many kinds of organisms. In the present study, we analyzed the base bias of 12 mitochondrial core protein-coding genes (PCGs) shared by nine Amanita species. The results showed that the codons of all Amanita species tended to end in A/T, demonstrating the preference of mitochondrial codons of Amanita species for a preference for this codon. In addition, we detected the correlation between codon base composition and the codon adaptation index (CAI), codon bias index (CBI), and frequency of optimal codons (FOP) indices, indicating the influence of base composition on codon bias. The average effective number of codons (ENC) of mitochondrial core PCGs of Amanita is 30.81, which is <35, demonstrating the strong codon preference of mitochondrial core PCGs of Amanita. The neutrality plot analysis and PR2-Bias plot analysis further demonstrated that natural selection plays an important role in Amanita codon bias. In addition, we obtained 5-10 optimal codons (ΔRSCU > 0.08 and RSCU > 1) in nine Amanita species, and GCA and AUU were the most widely used optimal codons. Based on the combined mitochondrial sequence and RSCU value, we deduced the genetic relationship between different Amanita species and found large variations between them. This study promoted the understanding of synonymous codon usage characteristics and evolution of this important fungal group.

Codon usage bias of Venezuelan equine encephalitis virus and its host adaption

Venezuelan equine encephalitis virus (VEEV) is an emerging zoonotic virus in the alphavirus genus. It can be transmitted to humans due to spillover from equid-mosquito cycles. The symptoms caused by VEEV include fever, headache, myalgia, nausea, and vomiting. It can also cause encephalitis in severe cases. The evolutionary features of VEEV are largely unknown. In this study, we comprehensively analyzed the codon usage pattern of VEEV by computing a variety of indicators, such as effective number of codons (ENc), codon adaptation index (CAI), relative synonymous codon usage (RSCU), on 130 VEEV coding sequences retrieved from GenBank. The results showed that the codon usage bias of VEEV is relatively low. ENc-GC3s plot, neutrality plot, and CAI-ENc correlation analyses supported that translational selection plays an important role in shaping the codon usage pattern of VEEV whereas the mutation pressure has a minor influence. Analysis of RSCU values showed that most of the preferred codons in VEEV are C/G-ended. Analysis of dinucleotide composition found that all CG- and UA-containing codons are not preferentially used. Phylogenetic analysis showed that VEEV isolates can be clustered into three genera and evolutionary force affects the codon usage pattern. Furthermore, a correspondence analysis (COA) showed that aromaticity and hydrophobicity as well as geographical distribution also have certain effects on the codon usage variation of VEEV, suggesting the possible involvement of translational selection. Overall, the codon usage of VEEV is comparatively slight and translational selection might be the main factor that shapes the codon usage pattern of VEEV. This study will promote our understanding about the evolution of VEEV and its host adaption, and might provide some clues for preventing the cross-species transmission of VEEV.

Prokaryotic Expression and Enzymatic Properties of Lipase from Schizochytrium Pombe

Based on the codon preference of Escherichia coli, the gene of lipase from Schizochytrium pombe was optimized and the codon adaptation index was 0.8 and the gene was expressed in Escherichia coli as a His-tag fusion protein using pET-30a as the expression vector. The recombinant strain with OD600 about 0.6 was induced with 1 mmol/L isopropyl-β-D-thiogalactoside at 30 °C for 12 h. The purified recombinant lipase STGL3 had a molecular mass of 32 kDa, and the purified lipase had a specific enzyme activity of 2.29 μmol/(min•mg), which was 2.3 times higher than before, and a protein content of 650.00 mg/L. The recombinant STGL3 showed the highest hydrolysis activity at 40 °C and pH 7.5, respectively. The effect of different metal ion concentrations on enzyme activity varied, lipase STGL3 was stable in the presence of Ca2+, while Mg2+, Mn2+, Zn2+, Fe2+ and EDTA had an inhibitory effects on lipase activity. Different types of surfactants and organic solvents inhibited the enzymatic activity of recombinant lipase to different extents. The recombinant lipases showed a preference towards pNP-esters with medium and longer acyl-chains. (C12, C14 and C16) and the recombinant lipases showed relatively weak lipase activity towards the shorter carbon chains. The kinetic constants of lipase were 0.29 mmol/L for Km, 2.28 mmol/(L•min-1 ) for Vmax, and 6.19 S-1 for Kcat. The present study not only achieved the expression and characterization of enzymatic properties of lipase from Leptosphaeria japonica, but also provided some basis for the study of enhancing the quality and quality of algal oil by means of genetic engineering, and further exploring the potential industrial applications of Leptosphaeria japonica lipase.

Analysis of 3.5 million SARS-CoV-2 sequences reveals unique mutational trends with consistent nucleotide and codon frequencies

BackgroundSince the onset of the SARS-CoV-2 pandemic, bioinformatic analyses have been performed to understand the nucleotide and synonymous codon usage features and mutational patterns of the virus. However, comparatively few have attempted to perform such analyses on a considerably large cohort of viral genomes while organizing the plethora of available sequence data for a month-by-month analysis to observe changes over time. Here, we aimed to perform sequence composition and mutation analysis of SARS-CoV-2, separating sequences by gene, clade, and timepoints, and contrast the mutational profile of SARS-CoV-2 to other comparable RNA viruses.MethodsUsing a cleaned, filtered, and pre-aligned dataset of over 3.5 million sequences downloaded from the GISAID database, we computed nucleotide and codon usage statistics, including calculation of relative synonymous codon usage values. We then calculated codon adaptation index (CAI) changes and a nonsynonymous/synonymous mutation ratio (dN/dS) over time for our dataset. Finally, we compiled information on the types of mutations occurring for SARS-CoV-2 and other comparable RNA viruses, and generated heatmaps showing codon and nucleotide composition at high entropy positions along the Spike sequence.ResultsWe show that nucleotide and codon usage metrics remain relatively consistent over the 32-month span, though there are significant differences between clades within each gene at various timepoints. CAI and dN/dS values vary substantially between different timepoints and different genes, with Spike gene on average showing both the highest CAI and dN/dS values. Mutational analysis showed that SARS-CoV-2 Spike has a higher proportion of nonsynonymous mutations than analogous genes in other RNA viruses, with nonsynonymous mutations outnumbering synonymous ones by up to 20:1. However, at several specific positions, synonymous mutations were overwhelmingly predominant.ConclusionsOur multifaceted analysis covering both the composition and mutation signature of SARS-CoV-2 gives valuable insight into the nucleotide frequency and codon usage heterogeneity of SARS-CoV-2 over time, and its unique mutational profile compared to other RNA viruses.

Prediction of protein-coding small ORFs in multi-species using integrated sequence-derived features and the random forest model.

Proteins encoded by small open reading frames (sORFs) can serve as functional elements playing important roles in vivo. Such sORFs also constitute the potential pool for facilitating the de novo gene birth, driving evolutionary innovation and species diversity. Therefore, their theoretical and experimental identification has become a critical issue. Herein, we proposed a protein-coding sORFs prediction method merely based on integrative sequence-derived features. Our prediction performance is better or comparable compared with other nine prevalent methods, which shows that our method can provide a relatively reliable research tool for the prediction of protein-coding sORFs. Our method allows users to estimate the potential expression of a queried sORF, which has been demonstrated by the correlation analysis between our possibility estimation and codon adaption index (CAI). Based on the features that we used, we demonstrated that the sequence features of the protein-coding sORFs in the two domains have significant differences implying that it might be a relatively hard task in terms of cross-domain prediction, hence domain-specific models were developed, which allowed users to predict protein-coding sORFs both in eukaryotes and prokaryotes. Finally, a web-server was developed and provided to boost and facilitate the study of the related field, which is freely available at http://guolab.whu.edu.cn/codingCapacity/index.html.

Bioinformatics design of recombinant chimeric protein containing SipD and LptD immunogens and evaluation of its immunogenicity against Salmonella Typhimurium

The growing emergence of resistant bacteria is the current global concern for the humans and animals. Vaccination could be the desirable method to preventing such infectious diseases. Safe and effective vaccines are urgently needed to manage and prevent Salmonella contamination. Subunit vaccines are safe approaches for the protection against Salmonella spp. The bioinformatics methods were performed to determine the gene structure. Gene cassette (rLPSI) was ordered in pET28a (+), and cloned into E.coli BL21 (DE3), and the recombinant protein was expressed using IPTG (1mM). Mice were immunized by subcutaneous administration of recombinant protein. Then, the mice were challenged by oral administration of 100LD50 of live S. Typhimurium. The Codon adaptation index of the chimeric gene was multiplied by 0.92. Validation results showed that >90% of residues lie in the desired or extra allowed area of the Ramachandran plot. The recombinant protein (65.9kDa) was expressed in E.coli. Antibody titers in vaccinated mice were significantly different from those in the control groups. Recombinant protein immunization of the mice provided 90% and 70% protection against 10LD50 and 100LD50 of S. Typhimurium, respectively. In general, the results showed the high efficiency of rLPSI chimeric protein as a protective antigen against S. Typhimurium infection. The rLPSI chimeric protein could be an effective recombinant vaccine candidate against S. Typhimurium infection, but more research is needed.

Revealing determinants of translation efficiency via whole-gene codon randomization and machine learning.

It has beenknown for decades that codon usage contributes to translation efficiency and hence to protein production levels. However, its role in protein synthesis is still only partly understood. This lack of understanding hampers the design of synthetic genes for efficient protein production. In this study, we generated a synonymous codon-randomized library of the complete coding sequence of red fluorescent protein. Protein production levels and the full coding sequences were determined for 1459 gene variants in Escherichia coli. Using different machine learning approaches, these data wereused to reveal correlations between codon usage and protein production. Interestingly, protein production levels can be relatively accurately predicted (Pearson correlation of 0.762) by a Random Forest model that only relies on the sequence information ofthe first eight codons. In this region, close tothe translation initiation site, mRNA secondary structure rather than Codon Adaptation Index (CAI)is the key determinant of protein production. This study clearly demonstratesthe key role of codons at the start of the coding sequence. Furthermore, these results imply that commonly used CAI-based codon optimization of the full coding sequence is not a very effective strategy. One should rather focus on optimizing protein production via reducing mRNA secondary structure formation with the first few codons.

Heterologous Expression of Toxic White Spot Syndrome Virus (WSSV) Protein in Eengineered Escherichia coli Strains.

Aquacultural shrimps suffer economic lost due to the white spot syndrome virus (WSSV) that is the most notorious virus for its fatality and contagion, leading to a 100% death rate on infected shrimps within 7days. However, the infection of mechanism remains a mystery and crucial problem. To elucidate the pathogenesis of WSSV, a high abundance of protein is required to identify and characterize its functions. Therefore, the optimal WSSV355 overexpression was explored in engineered Escherichia coli strains, in particular C43(DE3) as a toxic tolerance strain remedied 40% of cell growth from BL21(DE3). Meanwhile, a trace amount of WSSV355 was observed in both strains. To optimize the codon of WSSV355 using codon adaption index (CAI), an overexpression was observed with 1.32mg/mL in C43(DE3), while the biomass was decreased by 35%. Subsequently, the co-expression with pRARE boosted the target protein up to 1.93mg/mL. Finally, by scaling up production of WSSV355 in the fermenter with sufficient oxygen supplied, the biomass and total and soluble protein were enhanced 67.6%, 44.9%, and 7.8% compared with that in flask condition. Herein, the current approach provides efficacious solutions to produce toxic proteins via codon usage, strain selection, and processing optimization by alleviating the burden and boosting protein production in E. coli.

Utilizing the codon adaptation index to evaluate the susceptibility to HIV-1 and SARS-CoV-2 related coronaviruses in possible target cells in humans.

Comprehensive identification of possible target cells for viruses is crucial for understanding the pathological mechanism of virosis. The susceptibility of cells to viruses depends on many factors. Besides the existence of receptors at the cell surface, effective expression of viral genes is also pivotal for viral infection. The regulation of viral gene expression is a multilevel process including transcription, translational initiation and translational elongation. At the translational elongation level, the translational efficiency of viral mRNAs mainly depends on the match between their codon composition and cellular translational machinery (usually referred to as codon adaptation). Thus, codon adaptation for viral ORFs in different cell types may be related to their susceptibility to viruses. In this study, we selected the codon adaptation index (CAI) which is a common codon adaptation-based indicator for assessing the translational efficiency at the translational elongation level to evaluate the susceptibility to two-pandemic viruses (HIV-1 and SARS-CoV-2) of different human cell types. Compared with previous studies that evaluated the infectivity of viruses based on codon adaptation, the main advantage of our study is that our analysis is refined to the cell-type level. At first, we verified the positive correlation between CAI and translational efficiency and strengthened the rationality of our research method. Then we calculated CAI for ORFs of two viruses in various human cell types. We found that compared to high-expression endogenous genes, the CAIs of viral ORFs are relatively low. This phenomenon implied that two kinds of viruses have not been well adapted to translational regulatory machinery in human cells. Also, we indicated that presumptive susceptibility to viruses according to CAI is usually consistent with the results of experimental research. However, there are still some exceptions. Finally, we found that two viruses have different effects on cellular translational mechanisms. HIV-1 decouples CAI and translational efficiency of endogenous genes in host cells and SARS-CoV-2 exhibits increased CAI for its ORFs in infected cells. Our results implied that at least in cases of HIV-1 and SARS-CoV-2, CAI can be regarded as an auxiliary index to assess cells' susceptibility to viruses but cannot be used as the only evidence to identify viral target cells.

Molecular signatures for gene expression in Mycobacterium leprae: A bioinformatic analysis

Mycobacterium leprae is an obligate intracellular parasite that has been difficult to study due to its inability to grow in artificial media and its late generation time. Differential gene expressions depend upon specific molecular signatures in and around specific genes in the genome. In silico bioinformatic studies can complement the analysis of data generated through wet-lab experiments. We analyzed molecular signatures in highly and lowly expressed genes (HEGs and LEGs) in M. leprae using DAMBE software. Secondary data from Akama et al. (2010) were used to select RNA expression levels of HEGs and LEGs. Minimum folding energies in 5′UTR of HEGs were higher (less negative) than in LEGs, and the Shine Dalgarno binding patterns were more clearly defined for HEGs than in LEGs. There were no differences in relative synonymous codon usage patterns, codon adaptation indices, and indices of translation elongations. Our analysis suggests that the molecular signatures in the 5′UTRs may have a greater role in determining the overall expression of the genes of M. leprae.

Optimization of designing multiple genes encoding the same protein based on NSGA-II for efficient execution on GPUs

&lt;abstract&gt;&lt;p&gt;In synthetic biology, it is a challenge to increase the production of target proteins by maximizing their expression levels. In order to augment expression levels, we need to focus on both homologous recombination and codon adaptation, which are estimated by three objective functions, namely HD (Hamming distance), LRCS (length of repeated or common substring) and CAI (codon adaptation index). Optimizing these objective functions simultaneously becomes a multi-objective optimization problem. The aim is to find satisfying solutions that have high codon adaptation and a low incidence of homologous recombination. However, obtaining satisfactory solutions requires calculating the objective functions multiple times with many cycles and solutions. In this paper, we propose an approach to accelerate the method of designing a set of CDSs (CoDing sequences) based on NSGA-II (non-dominated sorting genetic algorithm II) on NVIDIA GPUs. The implementation accelerated by GPUs improves overall performance by 187.5$ \times $ using $ 100 $ cycles and $ 128 $ solutions. Our implementation allows us to use larger solutions and more cycles, leading to outstanding solution quality. The improved implementation provides much better solutions in a similar amount of time compared to other available methods by 1.22$ \times $ improvements in hypervolume. Furthermore, our approach on GPUs also suggests how to efficiently utilize the latest computational resources in bioinformatics. Finally, we discuss the impacts of the number of cycles and the number of solutions on designing a set of CDSs.&lt;/p&gt;&lt;/abstract&gt;

Evolution of monkeypox virus from 2017 to 2022: In the light of point mutations.

Monkeypox virus (MPXV) causing multi-country outbreak-2022 is related to viruses caused outbreak-2017-2018 in West Africa. Still not fully understood which proteins of the MPXV discovered in Nigeria in 2017 have mutated through different lineages to the extent that it could cause a multi-country outbreak in 2022; similarly, codon usage bias, host adaptation indices, and the role of selection or mutation pressure in the mutated genes are also not fully studied. Here we report that according to the available sequence data this monkeypox virus acquires point mutations in multiple proteins in each period, and these point mutations accumulate and become a virus that can root outbreak-2022. Viruses exported from Nigeria to Singapore, Israel, and the United Kingdom in 2018-2019 were developed as evolutionary ancestors to B.1 viruses (MPXVs causing multi-country outbreak-2022) through MPXV/United States/2021/MD virus. Although these exported viruses have different amino acid mutations in different proteins, amino acid mutations in 10 proteins are common among them. The MPXV-United Kingdom-P2 virus evolved with only mutations in these 10 proteins and further evolved into MPXV/United States/2021/MD with amino acid mutations in 26 (including amino acid mutations in 10 proteins of the MPXV-United States-P2) proteins. It is noteworthy that specific amino acid mutations in these 22/26 (presence in MPXV/United States/2021/MD) proteins are present in B.1 viruses. Further, analysis of Relative Synonymous Codon Usage (RSCU), Synonymous Codon Usage Fraction (SCUF), and Effective Number of Codons (ENc) revealed codon usage bias in genes that exhibited nucleotide mutations in lineage B.1. Also, host adaptation indices analyzes such as Codon Adaptation Index (CAI), Expected-CAI (eCAI), Relative Codon Deoptimization Index (RCDI) and Expected value for the RCDI (eRCDI) analyzes reveal that the genes that demonstrated nucleotide mutations in lineage B.1 are favorable for human adaptation. Similarly, ENc-GC3s plot, Neutrality plot, and Parity Rule 2 (PR2)-bias plot analyzes suggest a major role of selection pressure than mutation pressure in the evolution of genes displaying nucleotide mutations in lineage B.1. Overall, from 2017 to 2022, MPXV's mutation and spread suggests that this virus continues to evolve through point mutation in the genes according to the available sequence data.

Chloroplast gene expression level is negatively correlated with evolutionary rates and selective pressure while positively with codon usage bias in Ophioglossum vulgatum L

BackgroundCharacterization of the key factors determining gene expression level has been of significant interest. Previous studies on the relationship among evolutionary rates, codon usage bias, and expression level mostly focused on either nuclear genes or unicellular/multicellular organisms but few in chloroplast (cp) genes. Ophioglossum vulgatum is a unique fern and has important scientific and medicinal values. In this study, we sequenced its cp genome and transcriptome to estimate the evolutionary rates (dN and dS), selective pressure (dN/dS), gene expression level, codon usage bias, and their correlations.ResultsThe correlation coefficients between dN, dS, and dN/dS, and Transcripts Per Million (TPM) average values were -0.278 (P = 0.027 < 0.05), -0.331 (P = 0.008 < 0.05), and -0.311 (P = 0.013 < 0.05), respectively. The codon adaptation index (CAI) and tRNA adaptation index (tAI) were significantly positively correlated with TPM average values (P < 0.05).ConclusionsOur results indicated that when the gene expression level was higher, the evolutionary rates and selective pressure were lower, but the codon usage bias was stronger. We provided evidence from cp gene data which supported the E-R (E stands for gene expression level and R stands for evolutionary rate) anti-correlation.

Comparison of N2O-reducing abilities and genome features of two nosZ-containing denitrifying bacteria, Pseudomonas veronii DM15 and Pseudomonas frederiksbergensis DM22.

The study systematically compared the N2O-reducing functional performances and the genomic features of two N2O-reducing isolates, aimed to screen out effective N2O-reducing bacteria with strong environmental adaption, and explore the possible regulation. Two N2O reducers, namely, Pseudomonas veronii DM15 (DM15) and Pseudomonas frederiksbergensis DM22 (DM22), isolated from paddy soil were selected. Their N2O-reducing abilities, and nosZ gene transcript abundance were determined under different temperatures (20°C, 30°C, 40°C) and oxygen concentrations (0%, 10%, 21%), and the whole genomes were sequenced by Illumina sequencing. The results showed that both DM15 and DM22 exhibited the strongest N2O reducing activity at 30°C and under anaerobic conditions. In comparison, DM15 generally exhibited significantly higher N2O reducing abilities and nosZ gene expression than DM22 under all tested conditions. In addition, DM15 possessed obviously higher expression potentials (codon adaptation index (CAI) value) of nos genes than DM22, and the nos cluster of the former contained a transcriptional regulator gene of dnr, while the latter did not. The results indicate that DM15 showed obviously stronger N2O-reducing abilities than DM22 under various conditions, which might be closely associated with its dnr transcriptional regulator, and thus promoting the higher transcriptional activities of nos genes. Although anaerobic conditions were the optimal conditions for N2O reduction in both strains, DM15 still reduced a certain amount of N2O even under aerobic conditions.

Cyanobacterial Blooms Are Not a Result of Positive Selection by Freshwater Eutrophication.

Long-standing cyanobacterial harmful algal blooms (CyanoHABs) are known to result from synergistic interaction between elevated nutrients and superior ecophysiology of cyanobacteria. However, it remains to be determined whether CyanoHABs are a result of positive selection by eutrophic waters. To address this, we conducted molecular evolutionary analyses on the genomes of 9 bloom-forming cyanobacteria, combined with pangenomics and metatranscriptomics. The results showed no positive selection by water eutrophication. Instead, all homologous genes in the species are under strong purifying selection based on the ratio of divergence at nonsynonymous and synonymous sites (dN/dS) and phylogeny. The dN/dS < 0.85 (median = 0.3) for all homologous genes are similar between the genes in the pathways driving CyanoHABs and housekeeping functions. Phylogenetic support for non-positive selection comes from the mixed clustering of strains: strains of the same species from diverse geographic origins form the same clusters, while strains from the same origins form different clusters. Further support lies in the codon adaptation index (CAI) and single nucleotide polymorphism (SNP). The CAI ranged from 0.42 to 0.9 (mean = 0.75), which indicates high-level codon usage bias; the pathways for CyanoHABs and housekeeping functions showed a similar CAI. Interestingly, CAI was negatively correlated with gene expression in 3 metatranscriptomes. The numbers of SNPs were concentrated around 5 to 50. As the SNP number increases, the gene expression level decreases. These negative correlations agree with the population-level dN/dS and phylogeny in supporting purifying selection in bloom-forming cyanobacteria. In summary, superior ecophysiology appears to be acquired prior to water eutrophication. IMPORTANCE CyanoHABs are global environmental hazards, and their mechanisms of action are being intensively investigated. On an ecological scale, CyanoHABs are consequences of synergistic interactions between biological functions and elevated nutrients in eutrophic waters. On an evolutionary scale, one important question is how bloom-forming cyanobacteria acquire these superior biological functions. There are several possibilities, including adaptive evolution and horizontal gene transfer. Here, we explored the possibility of positive selection. We reasoned that there are two possible periods for cyanobacteria to acquire these functions: before the onset of water eutrophication or during water eutrophication. Either way, there should be molecular signatures in protein sequences for positive selection. Interestingly, we found no positive selection by water eutrophication, but strong purifying selection instead on nearly all the genes, suggesting these superior functions aiding CyanoHABs are acquired prior to water eutrophication.

Comparative Analysis on the Codon Usage Pattern of the Chloroplast Genomes in Malus Species.

Codon usage bias of coding sequences has been usually used for exploring the evolutionary factors that affect the variation of genes. We took 20 chloroplast genomes of Malus species into account to explore the codon usage patterns, including the composition, relationship between GC3s and effective number of codons, the parity rule two analyses, the relative synonymous codon usage, the codon adaptation index, the frequency of optimal codons, the codon bias index, etc., of their coding genes. The relationship between GC3 and the ENC values showed that when the separate genes are concerned, the distribution of their GC3 contents is relatively concentrated and the distribution of the ENC values are from 35 to 61 or so. The neutrality plot showed that the correlation coefficient between GC12 and GC3 is 0.095, revealing the mutation factor played a weak role in codon pattern formation. Correspondence analysis results revealed that the codon usage patterns in the chloroplast genomes of Malus species are similar. All these results showed that all Malus chloroplast genomes are AT rich ones, the third bases of the codons are affected by the natural selection pressure, the first two nucleotide base of the codon are affected by mutation pressure. Some genes, such as the rsp7, psbA and ycf2 are of lower codon usage divergences, while the rps12, rps16 and ndhD are of higher codon usage divergences. Codon usage bias exists in the Malus genomes could be used for exploring the evolutionary characteristics in chloroplast genomes and for further study on evolutionary phenomenon in other species.

Genome mining reveals abiotic stress resistance genes in plant genomes acquired from microbes via HGT.

Colonization by beneficial microbes can enhance plant tolerance to abiotic stresses. However, there are still many unknown fields regarding the beneficial plant-microbe interactions. In this study, we have assessed the amount or impact of horizontal gene transfer (HGT)-derived genes in plants that have potentials to confer abiotic stress resistance. We have identified a total of 235 gene entries in fourteen high-quality plant genomes belonging to phyla Chlorophyta and Streptophyta that confer resistance against a wide range of abiotic pressures acquired from microbes through independent HGTs. These genes encode proteins contributed to toxic metal resistance (e.g., ChrA, CopA, CorA), osmotic and drought stress resistance (e.g., Na+/proline symporter, potassium/proton antiporter), acid resistance (e.g., PcxA, ArcA, YhdG), heat and cold stress resistance (e.g., DnaJ, Hsp20, CspA), oxidative stress resistance (e.g., GST, PoxA, glutaredoxin), DNA damage resistance (e.g., Rad25, Rad51, UvrD), and organic pollutant resistance (e.g., CytP450, laccase, CbbY). Phylogenetic analyses have supported the HGT inferences as the plant lineages are all clustering closely with distant microbial lineages. Deep-learning-based protein structure prediction and analyses, in combination with expression assessment based on codon adaption index (CAI) further corroborated the functionality and expressivity of the HGT genes in plant genomes. A case-study applying fold comparison and molecular dynamics (MD) of the HGT-driven CytP450 gave a more detailed illustration on the resemblance and evolutionary linkage between the plant recipient and microbial donor sequences. Together, the microbe-originated HGT genes identified in plant genomes and their participation in abiotic pressures resistance indicate a more profound impact of HGT on the adaptive evolution of plants.