Universal evolutionary selection for high dimensional silent patterns of information hidden in the redundancy of viral genetic code

Nucleotide and codon usage biases involved in the evolution of African swine fever virus: A comparative genomics analysis

Since African swine fever virus (ASFV) replication is closely related to its host's machinery, codon usage of viral genome can be subject to selection pressures. A better understanding of codon usage can give new insights into viral evolution. We implemented information entropy and revealed that the nucleotide usage pattern of ASFV is significantly associated with viral isolation factors (region and time), especially the usages of thymine and cytosine. Despite the domination of adenine and thymine in the viral genome, we found that mutation pressure alters the overall codon usage pattern of ASFV, followed by selective forces from natural selection. Moreover, the nucleotide skew index at the gene level indicates that nucleotide usages influencing synonymous codon bias of ASFV are significantly correlated with viral protein hydropathy. Finally, evolutionary plasticity is proved to contribute to the weakness in synonymous codons with A- or T-end serving as optimal codons of ASFV, suggesting that fine-tuning translation selection plays a role in synonymous codon usages of ASFV for adapting host. Taken together, ASFV is subject to evolutionary dynamics on nucleotide selections and synonymous codon usage, and our detailed analysis offers deeper insights into the genetic characteristics of this newly emerging virus around the world.

Evolutionary selection against short nucleotide sequences in viruses and their related hosts

Viruses are under constant evolutionary pressure to effectively interact with the host intracellular factors, while evading its immune system. Understanding how viruses co-evolve with their hosts is a fundamental topic in molecular evolution and may also aid in developing novel viral based applications such as vaccines, oncologic therapies, and anti-bacterial treatments. Here, based on a novel statistical framework and a large-scale genomic analysis of 2,625 viruses from all classes infecting 439 host organisms from all kingdoms of life, we identify short nucleotide sequences that are under-represented in the coding regions of viruses and their hosts. These sequences cannot be explained by the coding regions’ amino acid content, codon, and dinucleotide frequencies. We specifically show that short homooligonucleotide and palindromic sequences tend to be under-represented in many viruses probably due to their effect on gene expression regulation and the interaction with the host immune system. In addition, we show that more sequences tend to be under-represented in dsDNA viruses than in other viral groups. Finally, we demonstrate, based on in vitro and in vivo experiments, how under-represented sequences can be used to attenuated Zika virus strains.

Computational based design and tracking of synthetic variants of Porcine circovirus reveal relations between silent genomic information and viral fitness

Viral genomes not only code the protein content, but also include silent, overlapping codes which are important to the regulation of the viral life cycle and affect its evolution. Due to the high density of these codes, their non-modular nature and the complex intracellular processes they encode, the ability of current approaches to decipher them is very limited. We describe the first computational-experimental pipeline for studying the effects of viral silent and non-silent information on its fitness. The pipeline was implemented to study the Porcine Circovirus type 2 (PCV2), the shortest known eukaryotic virus, and includes the following steps: (1) Based on the analyses of 2100 variants of PCV, suspected silent codes were inferred. (2) Five hundred variants of the PCV2 were designed to include various ‘smart’ silent mutations. (3) Using state of the art synthetic biology approaches, the genomes of these five hundred variants were generated. (4) Competition experiments between the variants were performed in Porcine kidney-15 (PK15) cell-lines. (5) The variant titers were analyzed based on novel next-generation sequencing (NGS) experiments. (6) The features related to the titer of the variants were inferred and their analyses enabled detection of various novel silent functional sequence and structural motifs. Furthermore, we demonstrate that 50 of the silent variants exhibit higher fitness than the wildtype in the analyzed conditions.

Molecules to Microbes

How did life begin on Earth? And is there life elsewhere in the Cosmos? Challenging questions, indeed. The series of conferences established by NoR CEL in 2013 addresses these very questions. This paper comprises a summary report of oral presentations that were delivered by NoR CEL’s network members during the 2018 Athens conference and, as such, disseminates the latest research which they have put forward. More in depth material can be found by consulting the contributors referenced papers. Overall, the outcome of this conspectus on the conference demonstrates a case for the existence of “probable chemistry” during the prebiotic epoch.

Predicting host taxonomic information from viral genomes: A comparison of feature representations

The rise in metagenomics has led to an exponential growth in virus discovery. However, the majority of these new virus sequences have no assigned host. Current machine learning approaches to predicting virus host interactions have a tendency to focus on nucleotide features, ignoring other representations of genomic information. Here we investigate the predictive potential of features generated from four different ‘levels’ of viral genome representation: nucleotide, amino acid, amino acid properties and protein domains. This more fully exploits the biological information present in the virus genomes. Over a hundred and eighty binary datasets for infecting versus non-infecting viruses at all taxonomic ranks of both eukaryote and prokaryote hosts were compiled. The viral genomes were converted into the four different levels of genome representation and twenty feature sets were generated by extracting k-mer compositions and predicted protein domains. We trained and tested Support Vector Machine, SVM, classifiers to compare the predictive capacity of each of these feature sets for each dataset. Our results show that all levels of genome representation are consistently predictive of host taxonomy and that prediction k-mer composition improves with increasing k-mer length for all k-mer based features. Using a phylogenetically aware holdout method, we demonstrate that the predictive feature sets contain signals reflecting both the evolutionary relationship between the viruses infecting related hosts, and host-mimicry. Our results demonstrate that incorporating a range of complementary features, generated purely from virus genome sequences, leads to improved accuracy for a range of virus host prediction tasks enabling computational assignment of host taxonomic information.

Elucidating the host of a newly identified virus species is an important challenge, with applications from knowing the source species of a newly emerged pathogen to understanding the bacteriophage-host relationships within the microbiome of any of earth’s ecosystems. Current high throughput methods used to identify viruses within biological or environmental samples have resulted in an unprecedented increase in virus discovery. However, for the majority of these virus genomes the host species/taxonomic classification remains unknown. To address this gap in our knowledge there is a need for fast, accurate computational methods for the assignment of putative host taxonomic information. Machine learning is an ideal approach but to maximise predictive accuracy the viral genomes need to be represented in a format (sets of features) that makes the discriminative information available to the machine learning algorithm. Here, we compare different types of features derived from the same viral genomes for their ability to predict host information. Our results demonstrate that all these feature sets are predictive of host taxonomy and when combined have the potential to improve accuracy over the use of individual feature sets across many virus host prediction applications.

Widespread non-modular overlapping codes in the coding regions

Messenger RNAs (mRNAs) consist of a coding region (open reading frame (ORF)) and two untranslated regions (UTRs), 5'UTR and 3'UTR. Ribosomes travel along the coding region, translating nucleotide triplets (called codons) to a chain of amino acids. The coding region was long believed to mainly encode the amino acid content of proteins, whereas regulatory signals reside in the UTRs and in other genomic regions. However, in recent years we have learned that the ORF is expansively populated with various regulatory signals, or codes, which are related to all gene expression steps and additional intracellular aspects. In this paper, we review the current knowledge related to overlapping codes inside the coding regions, such as the influence of synonymous codon usage on translation speed (and, in turn, the effect of translation speed on protein folding), ribosomal frameshifting, mRNA stability, methylation, splicing, transcription and more. All these codes come together and overlap in the ORF sequence, ensuring production of the right protein at the right time.

Molecules to Microbes

How did life begin on Earth? And is there life elsewhere in the Cosmos? Challenging questions, indeed. The series of conferences established by NoR CEL in 2013, addresses these very same questions. The basis for this paper is the summary report of oral presentations that were delivered by NoR CEL’s network members during the 2018 Athens conference and, as such, disseminates the latest research which they have put forward. More in depth material can be found by consulting the contributors referenced papers. Overall, the outcome of this conspectus on the conference demonstrates a case for the existence of “probable chemistry” during the prebiotic epoch.

ExtRamp: a novel algorithm for extracting the ramp sequence based on the tRNA adaptation index or relative codon adaptiveness

Different species, genes, and locations within genes use different codons to fine-tune gene expression. Within genes, the ramp sequence assists in ribosome spacing and decreases downstream collisions by incorporating slowly-translated codons at the beginning of a gene. Although previously reported as occurring in some species, no previous attempt at extracting the ramp sequence from specific genes has been published. We present ExtRamp, a software package that quickly extracts ramp sequences from any species using the tRNA adaptation index or relative codon adaptiveness. Different filters facilitate the analysis of codon efficiency and enable identification of genes with a ramp sequence. We validate the existence of a ramp sequence in most species by running ExtRamp on 229 742 339 genes across 23 428 species. We evaluate differences in reported ramp sequences when we use different parameters. Using the strictest ramp sequence cut-off, we show that across most taxonomic groups, ramp sequences are approximately 20–40 codons long and occur in about 10% of gene sequences. We also show that in Drosophila melanogaster as gene expression increases, a higher proportion of genes have ramp sequences. We provide a framework for performing this analysis on other species. ExtRamp is freely available at https://github.com/ridgelab/ExtRamp.

Molecules to Microbes

How did life begin on Earth? And is there life elsewhere in the Cosmos? Challenging questions, indeed. The series of conferences established by NoR CEL in 2013, addresses these very same questions. The basis for this paper is the summary report of oral presentations that were delivered by NoR CEL’s network members during the 2018 Athens conference and, as such, disseminates the latest research which they have put forward. More in depth material can be found by consulting the contributors referenced papers. Overall, the outcome of this conspectus on the conference demonstrates a case for the existence of “probable chemistry” during the prebiotic epoch.

Host Taxon Predictor - A Tool for Predicting Taxon of the Host of a Newly Discovered Virus

Recent advances in metagenomics provided a valuable alternative to culture-based approaches for better sampling viral diversity. However, some of newly identified viruses lack sequence similarity to any of previously sequenced ones, and cannot be easily assigned to their hosts. Here we present a bioinformatic approach to this problem. We developed classifiers capable of distinguishing eukaryotic viruses from the phages achieving almost 95% prediction accuracy. The classifiers are wrapped in Host Taxon Predictor (HTP) software written in Python which is freely available at https://github.com/wojciech-galan/viruses_classifier. HTP’s performance was later demonstrated on a collection of newly identified viral genomes and genome fragments. In summary, HTP is a culture- and alignment-free approach for distinction between phages and eukaryotic viruses. We have also shown that it is possible to further extend our method to go up the evolutionary tree and predict whether a virus can infect narrower taxa.