Evolution of sequence traits of prion-like proteins linked to amyotrophic lateral sclerosis (ALS)

Prions are proteinaceous particles that can propagate an alternative conformation to further copies of the same protein. They have been described in mammals, fungi, bacteria and archaea. Furthermore, across diverse organisms from bacteria to eukaryotes, prion-like proteins that have similar sequence characters are evident. Such prion-like proteins have been linked to pathomechanisms of amyotrophic lateral sclerosis (ALS) in humans, in particular TDP43, FUS, TAF15, EWSR1 and hnRNPA2. Because of the desire to study human disease-linked proteins in model organisms, and to gain insights into the functionally important parts of these proteins and how they have changed across hundreds of millions of years of evolution, we analyzed how the sequence traits of these five proteins have evolved across eukaryotes, including plants and metazoa. We discover that the RNA-binding domain architecture of these proteins is deeply conserved since their emergence. Prion-like regions are also deeply and widely conserved since the origination of the protein families for FUS, TAF15 and EWSR1, and since the last common ancestor of metazoa for TDP43 and hnRNPA2. Prion-like composition is uncommon or weak in any plant orthologs observed, however in TDP43 many plant proteins have equivalent regions rich in other amino acids (namely glycine and tyrosine and/or serine) that may be linked to stress granule recruitment. Deeply conserved low-complexity domains are identified that likely have functional significance.

fLPS 2.0: rapid annotation of compositionally-biased regions in biological sequences

Compositionally-biased (CB) regions in biological sequences are enriched for a subset of sequence residue types. These can be shorter regions with a concentrated bias (i.e., those termed ‘low-complexity’), or longer regions that have a compositional skew. These regions comprise a prominent class of the uncharacterized ‘dark matter’ of the protein universe. Here, I report the latest version of the fLPS package for the annotation of CB regions, which includes added consideration of DNA sequences, to label the eight possible biased regions of DNA. In this version, the user is now able to restrict analysis to a specified subset of residue types, and also to filter for previously annotated domains to enable detection of discontinuous CB regions. A ‘thorough’ option has been added which enables the labelling of subtler biases, typically made from a skew for several residue types. In the output, protein CB regions are now labelled with bias classes reflecting the physico-chemical character of the biasing residues. The fLPS 2.0 package is available from: https://github.com/pmharrison/flps2 or in a Supplemental File of this paper.

Signal, bias, and the role of transcriptome assembly quality in phylogenomic inference

BACKGROUND

Phylogenomic approaches have great power to reconstruct evolutionary histories, however they rely on multi-step processes in which each stage has the potential to affect the accuracy of the final result. Many studies have empirically tested and established methodology for resolving robust phylogenies, including selecting appropriate evolutionary models, identifying orthologs, or isolating partitions with strong phylogenetic signal. However, few have investigated errors that may be initiated at earlier stages of the analysis. Biases introduced during the generation of the phylogenomic dataset itself could produce downstream effects on analyses of evolutionary history. Transcriptomes are widely used in phylogenomics studies, though there is little understanding of how a poor-quality assembly of these datasets could impact the accuracy of phylogenomic hypotheses. Here we examined how transcriptome assembly quality affects phylogenomic inferences by creating independent datasets from the same input data representing high-quality and low-quality transcriptome assembly outcomes.

RESULTS

By studying the performance of phylogenomic datasets derived from alternative high- and low-quality assembly inputs in a controlled experiment, we show that high-quality transcriptomes produce richer phylogenomic datasets with a greater number of unique partitions than low-quality assemblies. High-quality assemblies also give rise to partitions that have lower alignment ambiguity and less compositional bias. In addition, high-quality partitions hold stronger phylogenetic signal than their low-quality transcriptome assembly counterparts in both concatenation- and coalescent-based analyses.

CONCLUSIONS

Our findings demonstrate the importance of transcriptome assembly quality in phylogenomic analyses and suggest that a portion of the uncertainty observed in such studies could be alleviated at the assembly stage.

Deep conservation of prion-like composition in the eukaryotic prion-former Pub1/Tia1 family and its relatives

Pub1 protein is an important RNA-binding protein functional in stress granule assembly in budding yeast Saccharomyces cerevisiae and, as its co-ortholog Tia1, in humans. It is unique among proteins in evidencing prion-like aggregation in both its yeast and human forms. Previously, we noted that Pub1/Tia1 was the only protein linked to human disease that has prion-like character and and has demonstrated such aggregation in both species. Thus, we were motivated to probe further into the evolution of the Pub1/Tia1 family (and its close relative Nam8 and its orthologs) to gain a picture of how such a protein has evolved over deep evolutionary time since the last common ancestor of eukaryotes. Here, we discover that the prion-like composition of this protein family is deeply conserved across eukaryotes, as is the prion-like composition of its close relative Nam8/Ngr1. A sizeable minority of protein orthologs have multiple prion-like domains within their sequences (6-20% depending on criteria). The number of RNA-binding RRM domains is conserved at three copies over >86% of the Pub1 family (>71% of the Nam8 family), but proteins with just one or two RRM domains occur frequently in some clades, indicating that these are not due to annotation errors. Overall, our results indicate that a basic scaffold comprising three RNA-binding domains and at least one prion-like region has been largely conserved since the last common ancestor of eukaryotes, providing further evidence that prion-like aggregation may be a very ancient and conserved phenomenon for certain specific proteins.

Avoidance of recognition sites of restriction-modification systems is a widespread but not universal anti-restriction strategy of prokaryotic viruses

BACKGROUND

Restriction-modification (R-M) systems protect bacteria and archaea from attacks by bacteriophages and archaeal viruses. An R-M system specifically recognizes short sites in foreign DNA and cleaves it, while such sites in the host DNA are protected by methylation. Prokaryotic viruses have developed a number of strategies to overcome this host defense. The simplest anti-restriction strategy is the elimination of recognition sites in the viral genome: no sites, no DNA cleavage. Even a decrease of the number of recognition sites can help a virus to overcome this type of host defense. Recognition site avoidance has been a known anti-restriction strategy of prokaryotic viruses for decades. However, recognition site avoidance has not been systematically studied with the currently available sequence data. We analyzed the complete genomes of almost 4000 prokaryotic viruses with known host species and more than 17,000 restriction endonucleases with known specificities in terms of recognition site avoidance.

RESULTS

We observed considerable limitations of recognition site avoidance as an anti-restriction strategy. Namely, the avoidance of recognition sites is specific for dsDNA and ssDNA prokaryotic viruses. Avoidance is much more pronounced in the genomes of non-temperate bacteriophages than in the genomes of temperate ones. Avoidance is not observed for the sites of Type I and Type IIG systems and is very rarely observed for the sites of Type III systems. The vast majority of avoidance cases concern recognition sites of orthodox Type II restriction-modification systems. Even under these constraints, complete or almost complete elimination of sites is observed for approximately one-tenth of viral genomes and a significant under-representation for approximately one-fourth of them.

CONCLUSIONS

Avoidance of recognition sites of restriction-modification systems is a widespread but not universal anti-restriction strategy of prokaryotic viruses.

Analysis and correction of compositional bias in sparse sequencing count data

BACKGROUND

Count data derived from high-throughput deoxy-ribonucliec acid (DNA) sequencing is frequently used in quantitative molecular assays. Due to properties inherent to the sequencing process, unnormalized count data is compositional, measuring relative and not absolute abundances of the assayed features. This compositional bias confounds inference of absolute abundances. Commonly used count data normalization approaches like library size scaling/rarefaction/subsampling cannot correct for compositional or any other relevant technical bias that is uncorrelated with library size.

RESULTS

We demonstrate that existing techniques for estimating compositional bias fail with sparse metagenomic 16S count data and propose an empirical Bayes normalization approach to overcome this problem. In addition, we clarify the assumptions underlying frequently used scaling normalization methods in light of compositional bias, including scaling methods that were not designed directly to address it.

CONCLUSIONS

Compositional bias, induced by the sequencing machine, confounds inferences of absolute abundances. We present a normalization technique for compositional bias correction in sparse sequencing count data, and demonstrate its improved performance in metagenomic 16s survey data. Based on the distribution of technical bias estimates arising from several publicly available large scale 16s count datasets, we argue that detailed experiments specifically addressing the influence of compositional bias in metagenomics are needed.

Novel metrics for quantifying bacterial genome composition skews

Background

Bacterial genomes have characteristic compositional skews, which are differences in nucleotide frequency between the leading and lagging DNA strands across a segment of a genome. It is thought that these strand asymmetries arise as a result of mutational biases and selective constraints, particularly for energy efficiency. Analysis of compositional skews in a diverse set of bacteria provides a comparative context in which mutational and selective environmental constraints can be studied. These analyses typically require finished and well-annotated genomic sequences.

Results

We present three novel metrics for examining genome composition skews; all three metrics can be computed for unfinished or partially-annotated genomes. The first two metrics, (dot-skew and cross-skew) depend on sequence and gene annotation of a single genome, while the third metric (residual skew) highlights unusual genomes by subtracting a GC content-based model of a library of genome sequences. We applied these metrics to 7738 available bacterial genomes, including partial drafts, and identified outlier species. A phylogenetically diverse set of these outliers (i.e., Borrelia, Ehrlichia, Kinetoplastibacterium, and Phytoplasma) display similar skew patterns but share lifestyle characteristics, such as intracellularity and biosynthetic dependence on their hosts.

Conclusions

Our novel metrics appear to reflect the effects of biosynthetic constraints and adaptations to life within one or more hosts on genome composition. We provide results for each analyzed genome, software and interactive visualizations at http://db.systemsbiology.net/gestalt/skew_metrics.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-4913-5) contains supplementary material, which is available to authorized users.

New insights into the factors affecting synonymous codon usage in human infecting Plasmodium species

Codon usage bias is due to the non-random usage of synonymous codons for coding amino acids. The synonymous sites are under weak selection, and codon usage bias is maintained by the equilibrium in mutational bias, genetic drift and selection pressure. The differential codon usage choices are also relevant to human infecting Plasmodium species. Recently, P. knowlesi switches its natural host, long-tailed macaques, and starts infecting humans. This review focuses on the comparative analysis of codon usage choices among human infecting P. falciparum and P. vivax along with P. knowlesi species taking their coding sequence data. The variation in GC content, amino acid frequencies, effective number of codons and other factors plays a crucial role in determining synonymous codon choices. Within species codon choices are more similar for P. vivax and P. knowlesi in comparison with P. falciparum species. This study suggests that synonymous codon choice modulates the gene expression level, mRNA stability, ribosome speed, protein folding, translation efficiency and its accuracy in Plasmodium species, and provides a valuable information regarding the codon usage pattern to facilitate gene cloning as well as expression and transfection studies for malaria causing species.

Genome-wide characterization and prediction of Arabidopsis thaliana replication origins

Identification of replication origins is crucial for the faithful duplication of genomic DNA. The frequencies of single nucleotides and dinucleotides, GC/AT bias and GC/AT profile in the vicinity of Arabidopsis thaliana replication origins were analyzed in the present work. The guanine content or cytosine content is higher in origin of replication (Ori) than in non-Ori. The SS (S=G or C) dinucleotides are favoured in Ori whereas WW (W=A or T) dinucleotides are favoured in non-Ori. GC/AT bias and GC/AT profile in Ori are significantly different from that in non-Ori. Furthermore, by inputting DNA sequence features into support vector machine, we distinguished between the Ori and non-Ori regions in A. thaliana. The total prediction accuracy is about 69.5% as evaluated by the 10-fold cross-validation. This result suggested that apart from DNA sequence, deciphering the selection of replication origin must integrate many other factors including nucleosome positioning, DNA methylation, histone modification, etc. In addition, by comparing predictive performance we found that the predictive accuracy of SVM using sequence features on the context of WS language is significantly better than that of RY language. Furthermore, the same conclusion was also obtained in S. cerevisiae and D. melanogaster.

Ribosomal proteins: toward a next generation standard for prokaryotic systematics?

The seminal work of Carl Woese and co-workers has contributed to promote the RNA component of the small subunit of the ribosome (SSU rRNA) as a "gold standard" of modern prokaryotic taxonomy and systematics, and an essential tool to explore microbial diversity. Yet, this marker has a limited resolving power, especially at deep phylogenetic depth and can lead to strongly biased trees. The ever-larger number of available complete genomes now calls for a novel standard dataset of robust protein markers that may complement SSU rRNA. In this respect, concatenation of ribosomal proteins (r-proteins) is being growingly used to reconstruct large-scale prokaryotic phylogenies, but their suitability for systematic and/or taxonomic purposes has not been specifically addressed. Using Proteobacteria as a case study, we show that amino acid and nucleic acid r-protein sequences contain a reliable phylogenetic signal at a wide range of taxonomic depths, which has not been totally blurred by mutational saturation or horizontal gene transfer. The use of accurate evolutionary models and reconstruction methods allows overcoming most tree reconstruction artefacts resulting from compositional biases and/or fast evolutionary rates. The inferred phylogenies allow clarifying the relationships among most proteobacterial orders and families, along with the position of several unclassified lineages, suggesting some possible revisions of the current classification. In addition, we investigate the root of the Proteobacteria by considering the time-variation of nucleic acid composition of r-protein sequences and the information carried by horizontal gene transfers, two approaches that do not require the use of an outgroup and limit tree reconstruction artefacts. Altogether, our analyses indicate that r-proteins may represent a promising standard for prokaryotic taxonomy and systematics.