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People with HIV-1 (PWH) on antiretroviral therapy (ART) can maintain undetectable virus levels, but a small pool of infected cells persists. This pool is largely comprised of defective proviruses that may produce HIV-1 proteins but are incapable of making infectious virus, with only a fraction (~10%) of these cells harboring intact viral genomes, some of which produce infectious virus following ex vivo stimulation (i.e. inducible intact proviruses). A majority of the inducible proviruses that persist on ART are formed near the time of therapy initiation. Here we compared proviral DNA (assessed here as 3' half genomes amplified from total cellular DNA) and inducible replication competent viruses in the pool of infected cells that persists during ART to determine if the original infection of these cells occurred at comparable times prior to therapy initiation. Overall, the average percent of proviruses that formed late (i.e. around the time of ART initiation, 60%) did not differ from the average percent of replication competent inducible viruses that formed late (69%), and this was also true for proviral DNA that was hypermutated (57%). Further, there was no evidence that entry into the long-lived infected cell pool was impeded by the ability to use the CXCR4 coreceptor, nor was the formation of long-lived infected cells enhanced during primary infection, when viral loads are exceptionally high. We observed that infection of cells that transitioned to be long-lived was enhanced among people with a lower nadir CD4+ T cell count. Together these data suggest that the timing of infection of cells that become long-lived is impacted more by biological processes associated with immunodeficiency before ART than the replication competency and/or cellular tropism of the infecting virus or the intactness of the provirus. Further research is needed to determine the mechanistic link between immunodeficiency and the timing of infected cells transitioning to the long-lived pool, particularly whether this is due to differences in infected cell clearance, turnover rates and/or homeostatic proliferation before and after ART.

The N1 neuraminidases (NAs) of avian and pandemic human influenza viruses contain tyrosine and asparagine, respectively, at position 347 on the rim of the catalytic site; the biological significance of this difference is not clear. Here, we used molecular dynamics simulation to model the effects of amino acid 347 on N1 NA interactions with sialyllacto-N-tetraoses 6’SLN-LC and 3’SLN-LC, which represent NA substrates in humans and birds, respectively. Our analysis predicted that Y347 plays an important role in the NA preference for the avian-type substrates. The Y347N substitution facilitates hydrolysis of human-type substrates by resolving steric conflicts of the Neu5Ac2–6Gal moiety with the bulky side chain of Y347, decreasing the free energy of substrate binding, and increasing the solvation of the Neu5Ac2–6Gal bond. Y347 was conserved in all N1 NA sequences of avian influenza viruses in the GISAID EpiFlu database with two exceptions. First, the Y347F substitution was present in the NA of a specific H6N1 poultry virus lineage and was associated with the substitutions G228S and/or E190V/L in the receptor-binding site (RBS) of the hemagglutinin (HA). Second, the highly pathogenic avian H5N1 viruses of the Gs/Gd lineage contained sporadic variants with the NA substitutions Y347H/D, which were frequently associated with substitutions in the HA RBS. The Y347N substitution occurred following the introductions of avian precursors into humans and pigs with N/D347 conserved during virus circulation in these hosts. Comparative evolutionary analysis of site 347 revealed episodic positive selection across the entire tree and negative selection within most host-specific groups of viruses, suggesting that substitutions at NA position 347 occurred during host switches and remained under pervasive purifying selection thereafter. Our results elucidate the role of amino acid 347 in NA recognition of sialoglycan substrates and emphasize the significance of substitutions at position 347 as a marker of host range and adaptive evolution of influenza viruses.

The hemagglutinin (HA) of A/H3N2 pandemic influenza viruses (IAVs) of 1968 differed from its inferred avian precursor by eight amino acid substitutions. To determine their phenotypic effects, we studied recombinant variants of A/Hong Kong/1/1968 virus containing either human-type or avian-type amino acids in the corresponding positions of HA. The precursor HA displayed receptor binding profile and high conformational stability typical for duck IAVs. Substitutions Q226L and G228S, in addition to their known effects on receptor specificity and replication, marginally decreased HA stability. Substitutions R62I, D63N, D81N and N193S reduced HA binding avidity. Substitutions R62I, D81N and A144G promoted viral replication in human airway epithelial cultures. Analysis of HA sequences revealed that substitutions D63N and D81N accompanied by the addition of N-glycans represent common markers of avian H3 HA adaptation to mammals. Our results advance understanding of genotypic and phenotypic changes in IAV HA required for avian-to-human adaptation and pandemic emergence.

The current state of much of the Wuhan pneumonia virus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) research shows a regrettable lack of data sharing and considerable analytical obfuscation. This impedes global research cooperation, which is essential for tackling public health emergencies and requires unimpeded access to data, analysis tools, and computational infrastructure. Here, we show that community efforts in developing open analytical software tools over the past 10 years, combined with national investments into scientific computational infrastructure, can overcome these deficiencies and provide an accessible platform for tackling global health emergencies in an open and transparent manner. Specifically, we use all SARS-CoV-2 genomic data available in the public domain so far to (1) underscore the importance of access to raw data and (2) demonstrate that existing community efforts in curation and deployment of biomedical software can reliably support rapid, reproducible research during global health crises. All our analyses are fully documented at https://github.com/galaxyproject/SARS-CoV-2.

Markov models of codon substitution are powerful inferential tools for studying biological processes such as natural selection and preferences in amino acid substitution. The equilibrium character distributions of these models are almost always estimated using nucleotide frequencies observed in a sequence alignment, primarily as a matter of historical convention. In this note, we demonstrate that a popular class of such estimators are biased, and that this bias has an adverse effect on goodness of fit and estimates of substitution rates. We propose a "corrected" empirical estimator that begins with observed nucleotide counts, but accounts for the nucleotide composition of stop codons. We show via simulation that the corrected estimates outperform the de facto standard estimates not just by providing better estimates of the frequencies themselves, but also by leading to improved estimation of other parameters in the evolutionary models. On a curated collection of sequence alignments, our estimators show a significant improvement in goodness of fit compared to the approach. Maximum likelihood estimation of the frequency parameters appears to be warranted in many cases, albeit at a greater computational cost. Our results demonstrate that there is little justification, either statistical or computational, for continued use of the -style estimators.

The single rate codon model of non-synonymous substitution is ubiquitous in phylogenetic modeling. Indeed, the use of a non-synonymous to synonymous substitution rate ratio parameter has facilitated the interpretation of selection pressure on genomes. Although the single rate model has achieved wide acceptance, we argue that the assumption of a single rate of non-synonymous substitution is biologically unreasonable, given observed differences in substitution rates evident from empirical amino acid models. Some have attempted to incorporate amino acid substitution biases into models of codon evolution and have shown improved model performance versus the single rate model. Here, we show that the single rate model of non-synonymous substitution is easily outperformed by a model with multiple non-synonymous rate classes, yet in which amino acid substitution pairs are assigned randomly to these classes. We argue that, since the single rate model is so easy to improve upon, new codon models should not be validated entirely on the basis of improved model fit over this model. Rather, we should strive to both improve on the single rate model and to approximate the general time-reversible model of codon substitution, with as few parameters as possible, so as to reduce model over-fitting. We hint at how this can be achieved with a Genetic Algorithm approach in which rate classes are assigned on the basis of sequence information content.

Coding of multiple proteins by overlapping reading frames is not a feature one would associate with eukaryotic genes. Indeed, codependency between codons of overlapping protein-coding regions imposes a unique set of evolutionary constraints, making it a costly arrangement. Yet in cases of tightly coexpressed interacting proteins, dual coding may be advantageous. Here we show that although dual coding is nearly impossible by chance, a number of human transcripts contain overlapping coding regions. Using newly developed statistical techniques, we identified 40 candidate genes with evolutionarily conserved overlapping coding regions. Because our approach is conservative, we expect mammals to possess more dual-coding genes. Our results emphasize that the skepticism surrounding eukaryotic dual coding is unwarranted: rather than being artifacts, overlapping reading frames are often hallmarks of fascinating biology.

AbstractPhylogeography can improve the understanding of local and worldwide HIV epidemics, including the migration of subepidemics across national borders. We analyzed HIV-1 sequences sampled from Mexico and San Diego, California to determine the relatedness of these epidemics. We sampled the HIV epidemics in (1) Mexico by downloading all publicly available HIV-1 polsequences from antiretroviral-naive individuals in GenBank (n= 100) and generating similar sequences from cohorts of injection drug users and female sex workers in Tijuana, Mexico (n= 27) and (2) in San Diego, California by polsequencing well-characterized primary (n= 395) and chronic (n= 267) HIV infection cohorts. Estimates of population structure (FST), genetic distance cluster analysis, and a cladistic measure of migration events (Slatkin–Maddison test) were used to assess the relatedness of the epidemics. Both a test of population differentiation (FST= 0.06; p< 0.01) and a cladistic estimate of migration events (84 migrations, p< 0.01) indicated that the Tijuana and San Diego epidemics were not freely mixing. A conservative cluster analysis identified 72 clusters (two or more sequences), with two clusters containing both Mexican and San Diego sequences (permutation p< 0.01). Analysis of this very large dataset of HIV-1 sequences suggested that the HIV-1 epidemics in San Diego, California and Tijuana, Mexico are distinct. Larger epidemiological studies are needed to quantify the magnitude and associations of cross-border mixing. [ABSTRACT FROM AUTHOR]

AbstractCurrent methods to detect intraclade HIV dual infection are poorly suited for determining its prevalence in large cohorts. To investigate the potential of ultra-deep sequencing to screen for dual infection, we compared it to bulk sequence-based synonymous mixture index and the current standard of single genome sequencing. The synonymous mixture index identified samples likely to harbor dual infection, while ultra-deep sequencing captured more intra-host viral diversity than single genome sequencing at approximately 40% of the cost and 20% of the laboratory and analysis time. The synonymous mixture index and ultra-deep sequencing are promising methods for rapid and cost-effective systematic identification of HIV dual infection. [ABSTRACT FROM AUTHOR]