Zachery W. Dickson

This multicentre study investigated the utility of next‐generation sequencing (NGS) to detect and generate hepatitis B virus (HBV) genomes in samples of low viral load (from 0.2 to 6207 IU/mL). 23 HBV DNA‐positive plasma samples of genotypes A‐E and one HBV‐negative control sample were assayed blindly via 9 established NGS methods from 6 European laboratories. Methods included untargeted metagenomics, pre‐enrichment by probe‐capture followed by Illumina sequencing, and HBV‐specific PCR pre‐amplification followed by sequencing with Nanopore or Illumina. Full HBV genomes were obtained only from samples with viral loads > 1000 IU/mL using probe‐capture methods, > 200 IU/mL using PCR‐Illumina methods, > 10 IU/mL using PCR‐Nanopore methods, and in no samples using metagenomic methods. Contamination was observed in the negative control and samples with very low viral loads in PCR‐based methods. Probe‐capture and metagenomic methods detected additional viruses not routinely screened in blood donations, including polyomaviruses and herpesviruses; positive results were confirmed by PCR. In conclusion, NGS may delineate whole‐genome sequences at low viral loads if supported by a PCR pre‐amplification step. Probe‐capture methods also reliably detect HBV without pre‐amplification but show limited genome coverage for samples with low viral loads; they may additionally detect a wide range of blood‐borne viruses.

Epstein–Barr virus (EBV) is a major risk factor for multiple sclerosis (MS), yet the contribution of specific viral variants remains unclear. Complete EBV genome analysis of wild-type variants has not been performed previously. In a pilot-study, targeted EBV sequencing of B cells did not yield adequate coverages due to excess of human DNA. Thus, we developed an ex vivo 6-day leukocyte culture method to enrich EBV DNA into supernatant by stimulation with tetradecanoyl phorbol acetate. Using this method in 20 MS patients and 20 controls, we obtained near-complete EBV genomes in 9 MS patients (average coverage 97%) and in 4 controls (average 85%). We identified 1088 single-nucleotide variants (33% missense variants) outside repetitive regions that met stringent quality criteria. Overall, 94% of the variants were present in three or more subjects, thus unlikely generated during the culture and several were exclusive to MS, worth of further investigation. In silico analysis indicated that variants within the EBNA1 cross-reactive region change human leukocyte antigen (HLA) peptide binding affinity, suggesting altered antigen presentation. These results illustrate a method for enriching EBV genomes and present the first wild-type sequences from MS patients. The observed diversity highlights potential for future studies in EBV-associated diseases and vaccine development.

Repetitive and compositionally biased low-complexity (LC) motifs appear in biological sequences where they interact with the machinery controlling the abundance of their host molecules. They can have significant impacts on physiological function, and act as raw material for evolution of regulatory motifs. The extent to which LC motifs affect abundance is not known. Even definitions of LC sequences are not well established, let alone which motifs exists in LC sequences, and which of those are abundance associated. To fill these knowledge gaps for post-transcriptional impacts of LC motifs, we integrated data from the GTEx project, PaxDb, and the IGSR. We establish definitions for LC motifs in both RNA and protein sequences. We observed that the presence of LC motifs in the 5′ untranslated regions (UTRs) were positively associated with transcript abundance. We present a method to de novo identify abundance associated motifs and identified trinucleotide repeats of (A/C)GG as most strongly abundance associated. We observed that m ¹ A methylation sites were strongly associated with both LC motifs and abundance, an effect which is amplified as methylation signatures from unspecialized RNA-seq increased. Together, our results demonstrate that LC motifs play important roles in regulating gene expression.

Objectives: This multicentre study investigated the utility of next-generation sequencing (NGS) to detect and generate hepatitis B virus (HBV) genomes in samples of low viral load (from 0.2 to 6207 IU/ml). Methods: 23 HBV DNA positive plasma samples of genotypes A-E and one HBV-negative control sample were assayed blindly via 9 established NGS methods from 6 European laboratories. Methods included untargeted metagenomics, pre-enrichment by probe-capture followed by Illumina sequencing, and HBV-specific PCR pre-amplification followed by sequencing with Nanopore or Illumina. Results: Full HBV genomes were obtained only from samples with viral loads >1000 IU/ml using probe-capture methods, >200 IU/ml using PCR-Illumina methods, >10 IU/ml using PCR-Nanopore methods, and in no samples using metagenomic methods. Contamination was observed in the negative control and samples with very low viral loads in all PCR-based methods. Probe-capture and metagenomic methods detected additional viruses not routinely screened in blood donations, including polyomaviruses and herpesviruses; positive results were confirmed by PCR. Conclusions: NGS may delineate whole-genome sequences at low viral loads if supported by a PCR pre-amplification step. Probe-capture methods also reliably detect HBV without pre-amplification but achieve limited genome characterisation at low viral loads; they may additionally detect a wide range of blood-borne viruses.

Protein Protein low complexity regions (LCRs) are compositionally biased amino acid sequences, many of which have significant evolutionary impacts on the proteins which contain them. They are mutationally unstable experiencing higher rates of indels and substitutions than higher complexity regions. LCRs also impact the expression of their proteins, likely through multiple effects along the path from gene transcription, through translation, and eventual protein degradation. It has been observed that proteins which contain LCRs are associated with elevated transcript abundance (TAb), despite having lower protein abundance. We have gathered and integrated human data to investigate the co-evolution of TAb and LCRs through ancestral reconstructions and model inference using an approximate Bayesian calculation based method. We observe that on short evolutionary timescales TAb evolution is significantly impacted by changes in LCR length, with insertions driving TAb down. But in contrast, the observed data is best explained by indel rates in LCRs which are unaffected by shifts in TAb. Our work demonstrates a coupling between LCR and TAb evolution, and the utility of incorporating multiple responses into evolutionary analyses.

Low complexity sequences (LCRs) are well known within coding as well as non-coding sequences. A low complexity region within a protein must be encoded by the underlying DNA sequence. Here, we examine the relationship between the entropy of the protein sequence and that of the DNA sequence which encodes it. We show that they are poorly correlated whether starting with a low complexity region within the protein and comparing it to the corresponding sequence in the DNA or by finding a low complexity region within coding DNA and comparing it to the corresponding sequence in the protein. We show this is the case within the proteomes of five model organisms: Homo sapiens, Saccharomyces cerevisiae, Drosophila melanogaster, Caenorhabditis elegans, and Arabidopsis thaliana. We also report a significant bias against mononucleic codons in LCR encoding sequences. By comparison with simulated proteomes, we show that highly repetitive LCRs may be explained by neutral, slippage-based evolution, but compositionally biased LCRs with cryptic repeats are not. We demonstrate that other biological biases and forces must be acting to create and maintain these LCRs. Uncovering these forces will improve our understanding of protein LCR evolution.

Low Complexity Regions (LCRs) are present in a surprisingly large number of eukaryotic proteins. These highly repetitive and compositionally biased sequences are often structurally disordered, bind promiscuously, and evolve rapidly. Frequently studied in terms of evolutionary dynamics, little is known about how LCRs affect the expression of the proteins which contain them. It would be expected that rapidly evolving LCRs are unlikely to be tolerated in strongly conserved, highly abundant proteins, leading to lower overall abundance in proteins which contain LCRs. To test this hypothesis and examine the associations of protein abundance and transcript abundance with the presence of LCRs, we have integrated high-throughput data from across mammals. We have found that LCRs are indeed associated with reduced protein abundance, but are also associated with elevated transcript abundance. These associations are qualitatively consistent across 12 human tissues and nine mammalian species. The differential impacts of LCRs on abundance at the protein and transcript level are not explained by differences in either protein degradation rates or the inefficiency of translation for LCR containing proteins. We suggest that rapidly evolving LCRs are a source of selective pressure on the regulatory mechanisms which maintain steady-state protein abundance levels.