Significant variations have been observed in viral copies generated during SARS-CoV-2 infections. However, the factors that impact viral copies and infection dynamics are not fully understood, and may be inherently dependent upon different viral and host factors. Here, we conducted virus whole genome sequencing and measured viral copies using RT-qPCR from 9,902 SARS-CoV-2 infections over a 2-year period to examine the impact of virus genetic variation on changes in viral copies adjusted for host age and vaccination status. Using a genome-wide association study (GWAS) approach, we identified multiple single-nucleotide polymorphisms (SNPs) corresponding to amino acid changes in the SARS-CoV-2 genome associated with variations in viral copies. We further applied a marginal epistasis test to detect interactions among SNPs and identified multiple pairs of substitutions located in the spike gene that have non-linear effects on viral copies. We also analyzed the temporal patterns and found that SNPs associated with increased viral copies were predominantly observed in Delta and Omicron BA.2/BA.4/BA.5/XBB infections, whereas those associated with decreased viral copies were only observed in infections with Omicron BA.1 variants. Our work showcases how GWAS can be a useful tool for probing phenotypes related to SNPs in viral genomes that are worth further exploration. We argue that this approach can be used more broadly across pathogens to characterize emerging variants and monitor therapeutic interventions.

My first exposure to visibly fluorescent proteins (FPs) was near the end of my time as a faculty member at the University of California, Berkeley.Prof. Alexander Glazer, a friend and colleague there, was the world's expert on phycobiliproteins, the brilliantly colored and intensely fluorescent proteins that serve as light-harvesting antennae for the photosynthetic apparatus of blue-green algae or cyanobacteria.One day, probably around 1987-88, Glazer told me that his lab had cloned the gene for one of the phycobiliproteins.Furthermore, he said, the apoprotein produced from this gene became fluorescent when mixed with its chromophore, a small molecule cofactor that could be extracted from dried cyanobacteria under conditions that cleaved its bond to the phycobiliprotein.I remember becoming very excited about the prospect that an arbitrary protein could be fluorescently tagged in situ by genetically fusing it to the phycobiliprotein, then administering the chromophore, which I hoped would be able to cross membranes and get inside cells.Unfortunately, Glazer's lab then found out that the spontaneous reaction between the apoprotein and the chromophore produced the "wrong" product, whose fluorescence was red-shifted and five-fold lower than that of the native phycobiliprotein [1][2][3] .An enzyme from the cyanobacteria was required to insert the chromophore correctly into the apoprotein.This enzyme was a heterodimer of two gene products, so at least three cyanobacterial genes would have to be introduced into any other organism, not counting any gene products needed to synthesize the chromophore 4 .Meanwhile fluorescence imaging of the second messenger cAMP (cyclic adenosine 3',5'-monophosphate) had become one of my main research goals by 1988.I reasoned that the best way to create a fluorescent sensor to detect cAMP with the necessary affinity and selectivity inside cells would be to hijack a natural cAMP-binding protein.After much consideration of the various candidates known at the time, I chose cAMP-dependent protein kinase, now more commonly abbreviated PKA.PKA contains two types of

As we wrote previously [1], science benefits when we share not only our insights and discoveries but also the tools and approaches that we develop.This sharing improves reproducibility and reuse, and it enables others to build on our work.These tools and approaches are research accelerants and are the focus of 2 key sections in our journal: Methods and Software.Since its founding in 2005, PLOS Computational Biology has been the home of exceptional computational research and cutting-edge methodological advances.We publish papers that use computational techniques to generate new biological insight, and we publish papers that describe software or methods that many other researchers in the field can use independently to generate new biological insight.There is great merit in using existing techniques to advance biological understanding, and there is great merit in developing and sharing new techniques that will result in further advances.The field of Computational Biology supports both.In 2013, the journal introduced the Methods section and the Software section to encourage more researchers to publish methodological advancements.From the inception of these sections, there were dedicated Methods Editors and Software Editors on the Editorial Board responsible for the papers submitted to each section.Through the end of 2022, those editors have guided the publication of 647 Methods papers and 286 Software papers, from among the 1,790 and 646 manuscripts submitted, respectively.The standard of those papers has been high, with many excellent and impactful papers published, covering methods for microbiome data analysis [2], deep learning to predict molecular interactions [3], and quantitative analysis of live-cell imaging [4], and software tools ranging from Bayesian Evolutionary Analysis [5] to multiomics integration and feature selection [6], and genome assembly [7].At the outset, the concept of having separate Methods-specific and Software-specific editors was seen as beneficial to being able to set appropriate criteria for the review and publication of these papers, for example, making clear that new work that facilitated original research was publishable, even if the paper itself did not include original research findings.By creating and stewarding a clear standard for these papers, it has been possible to maintain and even increase the high standard of methods and software papers published in our journal.By all metrics, the Methods and Software sections have been a massive success and continue to grow.More than half of all submissions received since 2013 in these sections have come in the last 3 years (Fig 1).Our editors made a Herculean effort to meet this demand, but it became harder to keep up.Clearly, we needed to spread the handling of these papers across more people.

On March 30, 2021, a new code sharing policy was introduced at PLOS Computational Biology [1].This policy requires any code supporting a publication to be shared unless there are ethical or legal restrictions that prevent sharing.The policy was introduced in response to community desire for a stronger position on code sharing to reflect the fact that the majority of the community already voluntarily share code [2,3].This community-driven support for open science practices aligns well with the PLOS mission, and, therefore, the implementation of the new policy was a logical progression for the journal.The policy focuses on increasing code sAU : Pleasenotet haring as its primary aim, which, in turn, will support reproducibility, and so is not prescriptive to authors about how or where to share their code.The policy (https://journals.plos.org/ ploscompbiol/s/code-availability) allows authors to comply in ways which work for them.By the end of the first year of the policy, we expected to see an increase in code sharing rates (the percentage of published research articles that share code) without any negative impact on the publishing demographics or the author, editor, and journal staff experiences.This Editorial reports on the impact of policy over the first 12 months, provides a longitudinal view of code sharing in the journal since 2019, and articulates how this effort can move forward to enhance further sharing, reproducibility, and openness.

Reported COVID-19 cases and deaths provide a delayed and incomplete picture of SARS-CoV-2 infections in the United States (US). Accurate estimates of both the timing and magnitude of infections are needed to characterize viral transmission dynamics and better understand COVID-19 disease burden. We estimated time trends in SARS-CoV-2 transmission and other COVID-19 outcomes for every county in the US, from the first reported COVID-19 case in January 13, 2020 through January 1, 2021. To do so we employed a Bayesian modeling approach that explicitly accounts for reporting delays and variation in case ascertainment, and generates daily estimates of incident SARS-CoV-2 infections on the basis of reported COVID-19 cases and deaths. The model is freely available as the covidestim R package. Nationally, we estimated there had been 49 million symptomatic COVID-19 cases and 404,214 COVID-19 deaths by the end of 2020, and that 28% of the US population had been infected. There was county-level variability in the timing and magnitude of incidence, with local epidemiological trends differing substantially from state or regional averages, leading to large differences in the estimated proportion of the population infected by the end of 2020. Our estimates of true COVID-19 related deaths are consistent with independent estimates of excess mortality, and our estimated trends in cumulative incidence of SARS-CoV-2 infection are consistent with trends in seroprevalence estimates from available antibody testing studies. Reconstructing the underlying incidence of SARS-CoV-2 infections across US counties allows for a more granular understanding of disease trends and the potential impact of epidemiological drivers.

Cohort studies, randomized trials, and post-licensure studies have reported reduced natural and vaccine-derived protection against rotavirus gastroenteritis (RVGE) in low- and middle-income countries. While susceptibility of children to rotavirus is known to vary within and between settings, implications for estimation of immune protection are not well understood. We sought to re-estimate naturally-acquired protection against rotavirus infection and RVGE, and to understand how differences in susceptibility among children impacted estimates. We re-analyzed data from studies conducted in Mexico City, Mexico and Vellore, India. Cumulatively, 573 rotavirus-unvaccinated children experienced 1418 rotavirus infections and 371 episodes of RVGE over 17,636 child-months. We developed a model that characterized susceptibility to rotavirus infection and RVGE among children, accounting for aspects of the natural history of rotavirus and differences in transmission rates between settings. We tested whether model-generated susceptibility measurements were associated with demographic and anthropometric factors, and with the severity of RVGE symptoms. We identified greater variation in susceptibility to rotavirus infection and RVGE in Vellore than in Mexico City. In both cohorts, susceptibility to rotavirus infection and RVGE were associated with male sex, lower birth weight, lower maternal education, and having fewer siblings; within Vellore, susceptibility was also associated with lower socioeconomic status. Children who were more susceptible to rotavirus also experienced higher rates of rotavirus-negative diarrhea, and higher risk of moderate-to-severe symptoms when experiencing RVGE. Simulations suggested that discrepant estimates of naturally-acquired immunity against RVGE can be attributed, in part, to between-setting differences in susceptibility of children, but result primarily from the interaction of transmission rates with age-dependent risk for infections to cause RVGE. We found that more children in Vellore than in Mexico City belong to a high-risk group for rotavirus infection and RVGE, and demonstrate that unmeasured individual- and age-dependent susceptibility may influence estimates of naturally-acquired immune protection against RVGE.