Evolutionary Selection of the Nuclear Localization Signal in the Viral Nucleoprotein Leads to Host Adaptation of the Genus Orthobornavirus.

Adaptation of the viral life cycle to host cells is necessary for efficient viral infection and replication. This evolutionary process has contributed to the mechanism for determining the host range of viruses. Orthobornaviruses, members of the family Bornaviridae, are non-segmented, negative-strand RNA viruses, and several genotypes have been isolated from different vertebrate species. Previous studies revealed that some genotypes isolated from avian species can replicate in mammalian cell lines, suggesting the zoonotic potential of avian orthobornaviruses. However, the mechanism by which the host specificity of orthobornaviruses is determined has not yet been identified. In this study, we found that the infectivity of orthobornaviruses is not determined at the viral entry step, mediated by the viral glycoprotein and matrix protein. Furthermore, we demonstrated that the nuclear localization signal (NLS) sequence in the viral nucleoprotein (N) has evolved under natural selection and determines the host-specific viral polymerase activity. A chimeric mammalian orthobornavirus, which has the NLS sequence of avian orthobornavirus N, exhibited a reduced propagation efficiency in mammalian cells. Our findings indicated that nuclear transport of the viral N is a determinant of the host range of orthobornaviruses, providing insights into the evolution and host adaptation of orthobornaviruses

Contact-number-driven virus evolution : a multi-level modeling framework for the evolution of acute or persistent RNA virus infection

Viruses evolve in infected host populations, and host population dynamics affect viral evolution. RNA viruses with a short duration of infection and a high peak viral load, such as SARS-CoV-2, are maintained in human populations. By contrast, RNA viruses characterized by a long infection duration and a low peak viral load (e.g., borna disease virus) can be maintained in nonhuman populations, and the process of the evolution of persistent viruses has rarely been explored. Here, using a multi-level modeling approach including both individual-level virus infection dynamics and population-scale transmission, we consider virus evolution based on the host environment, specifically, the effect of the contact history of infected hosts. We found that, with a highly dense contact history, viruses with a high virus production rate but low accuracy are likely to be optimal, resulting in a short infectious period with a high peak viral load. In contrast, with a low-density contact history, viral evolution is toward low virus production but high accuracy, resulting in long infection durations with low peak viral load. Our study sheds light on the origin of persistent viruses and why acute viral infections but not persistent virus infection tends to prevail in human society

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution

<p>Supplementary figures and additional graphs described in "Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution". (version 2)</p&gt

Isolation may select for earlier and higher peak viral load but shorter duration in SARS-CoV-2 evolution

Abstract During the COVID-19 pandemic, human behavior change as a result of nonpharmaceutical interventions such as isolation may have induced directional selection for viral evolution. By combining previously published empirical clinical data analysis and multi-level mathematical modeling, we find that the SARS-CoV-2 variants selected for as the virus evolved from the pre-Alpha to the Delta variant had earlier and higher peak in viral load dynamics but a shorter duration of infection. Selection for increased transmissibility shapes the viral load dynamics, and the isolation measure is likely to be a driver of these evolutionary transitions. In addition, we show that a decreased incubation period and an increased proportion of asymptomatic infection are also positively selected for as SARS-CoV-2 mutated to adapt to human behavior (i.e., Omicron variants). The quantitative information and predictions we present here can guide future responses in the potential arms race between pandemic interventions and viral evolution

Risk estimation model for nonalcoholic fatty liver disease in the Japanese using multiple genetic markers

<div><p>The genetic factors affecting the natural history of nonalcoholic fatty liver disease (NAFLD), including the development of nonalcoholic steatohepatitis (NASH) and NASH-derived hepatocellular carcinoma (NASH-HCC), are still unknown. In the current study, we sought to identify genetic factors related to the development of NAFLD, NASH, and NASH-HCC, and to establish risk-estimation models for them. For these purposes, 936 histologically proven NAFLD patients were recruited, and genome-wide association (GWA) studies were conducted for 902, including 476 NASH and 58 NASH-HCC patients, against 7,672 general-population controls. Risk estimations for NAFLD and NASH were then performed using the SNPs identified as having significant associations in the GWA studies. We found that rs2896019 in <i>PNPLA3</i> [<i>p</i> = 2.3x10^-31, OR (95%CI) = 1.85 (1.67–2.05)], rs1260326 in <i>GCKR</i> [<i>p</i> = 9.6x10^-10, OR (95%CI) = 1.38(1.25–1.53)], and rs4808199 in <i>GATAD2A</i> [<i>p</i> = 2.3x10^-8, OR (95%CI) = 1.37 (1.23–1.53)] were significantly associated with NAFLD. Notably, the number of risk alleles in <i>PNPLA3</i> and <i>GATAD2A</i> was much higher in Matteoni type 4 (NASH) patients than in type 1, type 2, and type 3 NAFLD patients. In addition, we newly identified rs17007417 in <i>DYSF</i> [<i>p</i> = 5.2x10^-7, OR (95%CI) = 2.74 (1.84–4.06)] as a SNP associated with NASH-HCC. Rs641738 in <i>TMC4</i>, which showed association with NAFLD in patients of European descent, was not replicated in our study (<i>p</i> = 0.73), although the complicated LD pattern in the region suggests the necessity for further investigation. The genetic variants of <i>PNPLA3</i>, <i>GCKR</i>, and <i>GATAD2A</i> were then used to estimate the risk for NAFLD. The obtained Polygenic Risk Scores showed that the risk for NAFLD increased with the accumulation of risk alleles [AUC (95%CI) = 0.65 (0.63–0.67)]. Conclusions: We demonstrated that NASH is genetically and clinically different from the other NAFLD subgroups. We also established risk-estimation models for NAFLD and NASH using multiple genetic markers. These models can be used to improve the accuracy of NAFLD diagnosis and to guide treatment decisions for patients.</p></div

SNP markers showing significant association in the GWA studies.

<p>SNP markers showing significant association in the GWA studies.</p

Clinical characteristics of the patient populations according to histological classification and the control population enrolled in the study.

<p>Clinical characteristics of the patient populations according to histological classification and the control population enrolled in the study.</p

Histogram of the odds ratios for genotype distributions between different subgroups.

<p>Each box denotes the odds ratio (OR) comparing the control or patient subgroups shown on the horizontal axes. The <i>p</i>-values, ORs, and 95% confidence intervals are shown in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0185490#pone.0185490.s007" target="_blank">S6 Table</a>.</p

Regional Manhattan plots around the SNP markers showing genome-wide significance in the GWA studies.

<p><i>P</i>-values, gene organization, and linkage disequilibrium (LD) plots according to the chromosomal position of the three significantly genome-wide associated regions for NAFLD (A-C) and the one region for NASH-HCC (D). Each figure spans 200 kb (A, B, and D) or 300 kb (C) in both the 5’ and 3’ directions from the SNP with the strongest association (shown with a red arrow) in the GWA studies. <i>P</i>-values are plotted for both genotyped and imputed SNPs in the upper panels, and previously reported SNPs with genome-wide significance are indicated by green arrows. The colors of the circles representing <i>p</i>-values correspond to the strength of LD (r2) from the most significant SNP in the GWA studies. The brightness of the red color in the LD plots in the lower panels also corresponds to the strength of LD.</p

Risk estimation according to Polygenic Risk Scores for NAFLD patients compared with controls.

<p>The dot and bar denote the odds ratio (OR) and its 95% confidence interval in each quintile compared to the 1st quintile using rs2896019 in <i>PNPLA3</i>, rs1260326 in <i>GCKR</i>, and rs4808199 in <i>GATAD2A</i> (A), and the receiver operating characteristic (ROC) curve from the model (red line), and the ROC curves including previously reported SNPs (green dashed line) or candidate SNPs (p<1x10^-4) identified in our GWA study (black dashed line) (B) are shown.</p