Using big sequencing data to identify chronic SARS-Coronavirus-2 infections.

The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2.

Dual anti-viral treatment for persistent COVID-19 in immunocompromised hemato-oncological patients is associated with a favorable prognosis and minor side effects.

In hemato-oncological patients, COVID-19 can present as a persistent infection with ongoing symptoms and viral replication over a prolonged period of time. Data are scarce on the preferred treatment options for these patients. We describe our experience with a five-day course of dual anti-viral treatment with remdesivir and nirmatrelvir/ritonavir for hemato-oncological immunocompromised patients with persistent COVID-19. Fifteen patients with a history of lymphoma, CLL, and MM were included. Eight were male, median age was 74. All patients had an immediate clinical and virological response. In 73 % of patients, PCR for SARS-CoV-2 became negative at the end of treatment and the rest had an increase in PCR cycle threshold (CT) values, with a median increase of 6 cycles. After a follow-up of three months, 60 % of patients remained in full clinical and virological remission. None required invasive mechanical ventilation or died. The side effects we observed, neutropenia, lactatemia and elevated transaminases, were mild and almost all transient in nature. We conclude that dual anti-viral treatment appears to be a valid treatment option for persistent COVID-19.

Mutation rate, selection, and epistasis inferred from RNA virus haplotypes via neural posterior estimation.

RNA viruses are particularly notorious for their high levels of genetic diversity, which is generated through the forces of mutation and natural selection. However, disentangling these two forces is a considerable challenge, and this may lead to widely divergent estimates of viral mutation rates, as well as difficulties in inferring the fitness effects of mutations. Here, we develop, test, and apply an approach aimed at inferring the mutation rate and key parameters that govern natural selection, from haplotype sequences covering full-length genomes of an evolving virus population. Our approach employs neural posterior estimation, a computational technique that applies simulation-based inference with neural networks to jointly infer multiple model parameters. We first tested our approach on synthetic data simulated using different mutation rates and selection parameters while accounting for sequencing errors. Reassuringly, the inferred parameter estimates were accurate and unbiased. We then applied our approach to haplotype sequencing data from a serial passaging experiment with the MS2 bacteriophage, a virus that parasites Escherichia coli. We estimated that the mutation rate of this phage is around 0.2 mutations per genome per replication cycle (95% highest density interval: 0.051-0.56). We validated this finding with two different approaches based on single-locus models that gave similar estimates but with much broader posterior distributions. Furthermore, we found evidence for reciprocal sign epistasis between four strongly beneficial mutations that all reside in an RNA stem loop that controls the expression of the viral lysis protein, responsible for lysing host cells and viral egress. We surmise that there is a fine balance between over- and underexpression of lysis that leads to this pattern of epistasis. To recap, we have developed an approach for joint inference of the mutation rate and selection parameters from full haplotype data with sequencing errors and used it to reveal features governing MS2 evolution.

Deciphering microbial gene function using natural language processing.

Revealing the function of uncharacterized genes is a fundamental challenge in an era of ever-increasing volumes of sequencing data. Here, we present a concept for tackling this challenge using deep learning methodologies adopted from natural language processing (NLP). We repurpose NLP algorithms to model "gene semantics" based on a biological corpus of more than 360 million microbial genes within their genomic context. We use the language models to predict functional categories for 56,617 genes and find that out of 1369 genes associated with recently discovered defense systems, 98% are inferred correctly. We then systematically evaluate the "discovery potential" of different functional categories, pinpointing those with the most genes yet to be characterized. Finally, we demonstrate our method's ability to discover systems associated with microbial interaction and defense. Our results highlight that combining microbial genomics and language models is a promising avenue for revealing gene functions in microbes.

In vivo engineered B cells secrete high titers of broadly neutralizing anti-HIV antibodies in mice.

Transplantation of B cells engineered ex vivo to secrete broadly neutralizing antibodies (bNAbs) has shown efficacy in disease models. However, clinical translation of this approach would require specialized medical centers, technically demanding protocols and major histocompatibility complex compatibility of donor cells and recipients. Here we report in vivo B cell engineering using two adeno-associated viral vectors, with one coding for Staphylococcus aureus Cas9 (saCas9) and the other for 3BNC117, an anti-HIV bNAb. After intravenously injecting the vectors into mice, we observe successful editing of B cells leading to memory retention and bNAb secretion at neutralizing titers of up to 6.8 µg ml-1. We observed minimal clustered regularly interspaced palindromic repeats (CRISPR)-Cas9 off-target cleavage as detected by unbiased CHANGE-sequencing analysis, whereas on-target cleavage in undesired tissues is reduced by expressing saCas9 from a B cell-specific promoter. In vivo B cell engineering to express therapeutic antibodies is a safe, potent and scalable method, which may be applicable not only to infectious diseases but also in the treatment of noncommunicable conditions, such as cancer and autoimmune disease.

Drivers of adaptive evolution during chronic SARS-CoV-2 infections.

In some immunocompromised patients with chronic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, considerable adaptive evolution occurs. Some substitutions found in chronic infections are lineage-defining mutations in variants of concern (VOCs), which has led to the hypothesis that VOCs emerged from chronic infections. In this study, we searched for drivers of VOC-like emergence by consolidating sequencing results from a set of 27 chronic infections. Most substitutions in this set reflected lineage-defining VOC mutations; however, a subset of mutations associated with successful global transmission was absent from chronic infections. We further tested the ability to associate antibody evasion mutations with patient-specific and virus-specific features and found that viral rebound is strongly correlated with the emergence of antibody evasion. We found evidence for dynamic polymorphic viral populations in most patients, suggesting that a compromised immune system selects for antibody evasion in particular niches in a patient's body. We suggest that a tradeoff exists between antibody evasion and transmissibility and that extensive monitoring of chronic infections is necessary to further understanding of VOC emergence.

Inferring Protein Function in an Emerging Virus: Detection of the Nucleoprotein in Tilapia Lake Virus.

Emerging viruses impose global threats to animal and human populations and may bear novel genes with limited homology to known sequences, necessitating the development of novel approaches to infer and test protein functions. This challenge is dramatically evident in tilapia lake virus (TiLV), an emerging "orthomyxo-like" virus that threatens the global tilapia aquaculture and food security of millions of people. The majority of TiLV proteins have no homology to known sequences, impeding functionality assessments. Using a novel bioinformatics approach, we predicted that TiLV's Protein 4 encodes the nucleoprotein, a factor essential for viral RNA replication. Multiple methodologies revealed the expected properties of orthomyxoviral nucleoproteins. A modified yeast three-hybrid assay detected Protein 4-RNA interactions, which were independent of the RNA sequence, and identified specific positively charged residues involved. Protein 4-RNA interactions were uncovered by R-DeeP and XRNAX methodologies. Immunoelectron microscopy found that multiple Protein 4 copies localized along enriched ribonucleoproteins. TiLV RNA from cells and virions coimmunoprecipitated with Protein 4. Immunofluorescence microscopy detected Protein 4 in the cytoplasm and nuclei, and nuclear Protein 4 increased upon CRM1 inhibition, suggesting CRM1-dependent nuclear export of TiLV RNA. Together, these data reveal TiLV's nucleoprotein and highlight the ability to infer protein functionality, including novel RNA-binding proteins, in emerging pathogens. These are important in light of the expected discovery of many unknown viruses and the zoonotic potential of such pathogens. IMPORTANCE Tilapia is an important source of dietary protein, especially in developing countries. Massive losses of tilapia were identified worldwide, risking the food security of millions of people. Tilapia lake virus (TiLV) is an emerging pathogen responsible for these disease outbreaks. TiLV's genome encodes 10 major proteins, 9 of which show no homology to other known viral or cellular proteins, hindering functionality assessment of these proteins. Here, we describe a novel bioinformatics approach to infer the functionality of TiLV proteins, which predicted Protein 4 as the nucleoprotein, a factor essential for viral RNA replication. We provided experimental support for this prediction by applying multiple molecular, biochemical, and imaging approaches. Overall, we illustrate a strategy for functional analyses in viral discovery. The strategy is important in light of the expected discovery of many unknown viruses and the zoonotic potential of such pathogens.

Unraveling a Nosocomial Outbreak of COVID-19: The Role of Whole-Genome Sequence Analysis.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic poses many epidemiological challenges. The investigation of nosocomial transmission is usually performed via thorough investigation of an index case and subsequent contact tracing. Notably, this approach has a subjective component, and there is accumulating evidence that whole-genome sequencing of the virus may provide more objective insight. METHODS: We report a large nosocomial outbreak in 1 of the medicine departments in our institution. Following intensive epidemiological investigation, we discovered that 1 of the patients involved was suffering from persistent COVID-19 while initially thought to be a recovering patient. She was therefore deemed to be the most likely source of the outbreak. We then performed whole-genome sequencing of the virus of 14 infected individuals involved in the outbreak. RESULTS: Surprisingly, the results of whole-genome sequencing refuted our initial hypothesis. A phylogenetic tree of the samples showed multiple introductions of the virus into the ward, 1 of which led to a cluster of 10 of the infected individuals. Importantly, the results pointed in the direction of a specific index patient that was different from the 1 that arose from our initial investigation. CONCLUSIONS: These results underscore the important added value of using whole-genome sequencing in epidemiological investigations as it may reveal unexpected connections between cases and aid in understanding transmission dynamics, especially in the setting of a pandemic where multiple possible index cases exist simultaneously.

Evidence for increased breakthrough rates of SARS-CoV-2 variants of concern in BNT162b2-mRNA-vaccinated individuals.

The BNT162b2 mRNA vaccine is highly effective against SARS-CoV-2. However, apprehension exists that variants of concern (VOCs) may evade vaccine protection, due to evidence of reduced neutralization of the VOCs B.1.1.7 and B.1.351 by vaccine sera in laboratory assays. We performed a matched cohort study to examine the distribution of VOCs in infections of BNT162b2 mRNA vaccinees from Clalit Health Services (Israel) using viral genomic sequencing, and hypothesized that if vaccine effectiveness against a VOC is reduced, its proportion among breakthrough cases would be higher than in unvaccinated controls. Analyzing 813 viral genome sequences from nasopharyngeal swabs, we showed that vaccinees who tested positive at least 7 days after the second dose were disproportionally infected with B.1.351, compared with controls. Those who tested positive between 2 weeks after the first dose and 6 days after the second dose were disproportionally infected by B.1.1.7. These findings suggest reduced vaccine effectiveness against both VOCs within particular time windows. Our results emphasize the importance of rigorously tracking viral variants, and of increasing vaccination to prevent the spread of VOCs.

Globally defining the effects of mutations in a picornavirus capsid.

The capsids of non-enveloped viruses are highly multimeric and multifunctional protein assemblies that play key roles in viral biology and pathogenesis. Despite their importance, a comprehensive understanding of how mutations affect viral fitness across different structural and functional attributes of the capsid is lacking. To address this limitation, we globally define the effects of mutations across the capsid of a human picornavirus. Using this resource, we identify structural and sequence determinants that accurately predict mutational fitness effects, refine evolutionary analyses, and define the sequence specificity of key capsid-encoded motifs. Furthermore, capitalizing on the derived sequence requirements for capsid-encoded protease cleavage sites, we implement a bioinformatic approach for identifying novel host proteins targeted by viral proteases. Our findings represent the most comprehensive investigation of mutational fitness effects in a picornavirus capsid to date and illuminate important aspects of viral biology, evolution, and host interactions.

A virus is made up of genetic material that is encased with a protective protein coat called the capsid. The capsid also helps the virus to infect host cells by binding to the host receptor proteins and releasing its genetic material. Inside the cell, the virus hitchhikes the infected cell's machinery to grow or replicate its own genetic material. Viral capsids are the main target of the host's defence system, and therefore, continuously change in an attempt to escape the immune system by introducing alterations (known as mutations) into the genes encoding viral capsid proteins. Mutations occur randomly, and so while some changes to the viral capsid might confer an advantage, others may have no effect at all, or even weaken the virus. To better understand the effect of capsid mutations on the virus' ability to infect host cells, Mattenberger et al. studied the Coxsackievirus B3, which is linked to heart problems and acute heart failure in humans. The researchers analysed around 90% of possible amino acid mutations (over 14,800 mutations) and correlated each mutation to how it influenced the virus' ability to replicate in human cells grown in the laboratory. Based on these results, Mattenberger et al. developed a computer model to predict how a particular mutation might affect the virus. The analysis also identified specific amino acid sequences of capsid proteins that are essential for certain tasks, such as building the capsid. It also included an analysis of sequences in the capsid that allow it to be recognized by another viral protein, which cuts the capsid proteins into the right size from a larger precursor. By looking for similar sequences in human genes, the researchers identified several ones that the virus may attack and inactivate to support its own replication. These findings may help identify potential drug targets to develop new antiviral therapies. For example, proteins of the capsid that are less likely to mutate will provide a better target as they lower the possibility of the virus to become resistant to the treatment. They also highlight new proteins in human cells that could potentially block the virus in cells.

Biased Mutation and Selection in RNA Viruses.

RNA viruses are responsible for some of the worst pandemics known to mankind, including outbreaks of Influenza, Ebola, and COVID-19. One major challenge in tackling RNA viruses is the fact they are extremely genetically diverse. Nevertheless, they share common features that include their dependence on host cells for replication, and high mutation rates. We set out to search for shared evolutionary characteristics that may aid in gaining a broader understanding of RNA virus evolution, and constructed a phylogeny-based data set spanning thousands of sequences from diverse single-stranded RNA viruses of animals. Strikingly, we found that the vast majority of these viruses have a skewed nucleotide composition, manifested as adenine rich (A-rich) coding sequences. In order to test whether A-richness is driven by selection or by biased mutation processes, we harnessed the effects of incomplete purifying selection at the tips of virus phylogenies. Our results revealed consistent mutational biases toward U rather than A in genomes of all viruses. In +ssRNA viruses, we found that this bias is compensated by selection against U and selection for A, which leads to A-rich genomes. In -ssRNA viruses, the genomic mutational bias toward U on the negative strand manifests as A-rich coding sequences, on the positive strand. We investigated possible reasons for the advantage of A-rich sequences including weakened RNA secondary structures, codon usage bias, and selection for a particular amino acid composition, and conclude that host immune pressures may have led to similar biases in coding sequence composition across very divergent RNA viruses.

Drivers of within-host genetic diversity in acute infections of viruses.

Genetic diversity is the fuel of evolution and facilitates adaptation to novel environments. However, our understanding of what drives differences in the genetic diversity during the early stages of viral infection is somewhat limited. Here, we use ultra-deep sequencing to interrogate 43 clinical samples taken from early infections of the human-infecting viruses HIV, RSV and CMV. Hundreds to thousands of virus templates were sequenced per sample, allowing us to reveal dramatic differences in within-host genetic diversity among virus populations. We found that increased diversity was mostly driven by presence of multiple divergent genotypes in HIV and CMV samples, which we suggest reflect multiple transmitted/founder viruses. Conversely, we detected an abundance of low frequency hyper-edited genomes in RSV samples, presumably reflecting defective virus genomes (DVGs). We suggest that RSV is characterized by higher levels of cellular co-infection, which allow for complementation and hence elevated levels of DVGs.

Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel.

Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

Site-Specific Evolutionary Rate Shifts in HIV-1 and SIV.

Site-specific evolutionary rate shifts are defined as protein sites, where the rate of substitution has changed dramatically across the phylogeny. With respect to a given clade, sites may either undergo a rate acceleration or a rate deceleration, reflecting a site that was conserved and became variable, or vice-versa, respectively. Sites displaying such a dramatic evolutionary change may point to a loss or gain of function at the protein site, reflecting adaptation, or they may indicate epistatic interactions among sites. Here, we analyzed full genomes of HIV and SIV-1 and identified 271 rate-shifting sites along the HIV-1/SIV phylogeny. The majority of rate shifts occurred at long branches, often corresponding to cross-species transmission branches. We noted that in most proteins, the number of rate accelerations and decelerations was equal, and we suggest that this reflects epistatic interactions among sites. However, several accessory proteins were enriched for either accelerations or decelerations, and we suggest that this may be a signature of adaptation to new hosts. Interestingly, the non-pandemic HIV-1 group O clade exhibited a substantially higher number of rate-shift events than the pandemic group M clade. We propose that this may be a reflection of the height of the species barrier between gorillas and humans versus chimpanzees and humans. Our results provide a genome-wide view of the constraints operating on proteins of HIV-1 and SIV.

Competition between social cheater viruses is driven by mechanistically different cheating strategies.

Cheater viruses, also known as defective interfering viruses, cannot replicate on their own yet replicate faster than the wild type upon coinfection. While there is growing interest in using cheaters as antiviral therapeutics, the mechanisms underlying cheating have been rarely explored. During experimental evolution of MS2 phage, we observed the parallel emergence of two independent cheater mutants. The first, a point deletion mutant, lacked polymerase activity but was advantageous in viral packaging. The second synonymous mutant cheater displayed a completely different cheating mechanism, involving an altered RNA structure. Continued evolution revealed the demise of the deletion cheater and rise of the synonymous cheater. A mathematical model inferred that while a single cheater is expected to reach an equilibrium with the wild type, cheater demise arises from antagonistic interactions between coinfecting cheaters. These findings highlight layers of parasitism: viruses parasitizing cells, cheaters parasitizing intact viruses, and cheaters may parasitize other cheaters.

Associations between ADHD and emotional problems from childhood to young adulthood: a longitudinal genetically sensitive study.

BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) is associated with emotional problems, and their co-occurrence often leads to worse outcomes. We investigated the developmental associations between ADHD and emotional problems from childhood to early adolescence and examined the genetic and environmental contributions to their developmental link. We further tested whether this developmental association remained across the transition to young adulthood. METHODS: We used data from the Environmental Risk (E-Risk) Longitudinal Twin Study, a cohort of 2,232 British twins. In childhood, ADHD and emotional problems were assessed at ages 5, 7, 10 and 12 with mothers' and teachers' reports. At age 18, we used self-reported symptoms according to DSM-5 criteria for ADHD, and DSM-IV for anxiety and depression. RESULTS: Longitudinal analyses showed that earlier ADHD was associated with later emotional problems consistently across childhood. However, earlier emotional problems were not associated with later ADHD symptoms. The developmental association between ADHD and later emotional problems in childhood was entirely explained by common genetic factors. Consistent with results in childhood, earlier symptoms of ADHD were associated with later emotional problems during the transition to young adulthood. CONCLUSIONS: Our findings demonstrate that ADHD symptoms are predictors of the development of emotional problems, from childhood up to young adulthood, through shared genetic influences. Interventions targeting ADHD symptoms might prevent the development of emotional problems. Clinicians treating youth with ADHD must be aware of their risk for developing emotional problems and ought to assess, monitor and treat emotional problems alongside ADHD symptoms from childhood to adulthood.

A Bayesian Framework for Inferring the Influence of Sequence Context on Point Mutations.

The probability of point mutations is expected to be highly influenced by the flanking nucleotides that surround them, known as the sequence context. This phenomenon may be mainly attributed to the enzyme that modifies or mutates the genetic material, because most enzymes tend to have specific sequence contexts that dictate their activity. Here, we develop a statistical model that allows for the detection and evaluation of the effects of different sequence contexts on mutation rates from deep population sequencing data. This task is computationally challenging, as the complexity of the model increases exponentially as the context size increases. We established our novel Bayesian method based on sparse model selection methods, with the leading assumption that the number of actual sequence contexts that directly influence mutation rates is minuscule compared with the number of possible sequence contexts. We show that our method is highly accurate on simulated data using pentanucleotide contexts, even when accounting for noisy data. We next analyze empirical population sequencing data from polioviruses and HIV-1 and detect a significant enrichment in sequence contexts associated with deamination by the cellular deaminases ADAR 1/2 and APOBEC3G, respectively. In the current era, where next-generation sequencing data are highly abundant, our approach can be used on any population sequencing data to reveal context-dependent base alterations and may assist in the discovery of novel mutable sites or editing sites.

Direct sequencing of RNA with MinION Nanopore: detecting mutations based on associations.

One of the key challenges in the field of genetics is the inference of haplotypes from next generation sequencing data. The MinION Oxford Nanopore sequencer allows sequencing long reads, with the potential of sequencing complete genes, and even complete genomes of viruses, in individual reads. However, MinION suffers from high error rates, rendering the detection of true variants difficult. Here, we propose a new statistical approach named AssociVar, which differentiates between true mutations and sequencing errors from direct RNA/DNA sequencing using MinION. Our strategy relies on the assumption that sequencing errors will be dispersed randomly along sequencing reads, and hence will not be associated with each other, whereas real mutations will display a non-random pattern of association with other mutations. We demonstrate our approach using direct RNA sequencing data from evolved populations of the MS2 bacteriophage, whose small genome makes it ideal for MinION sequencing. AssociVar inferred several mutations in the phage genome, which were corroborated using parallel Illumina sequencing. This allowed us to reconstruct full genome viral haplotypes constituting different strains that were present in the sample. Our approach is applicable to long read sequencing data from any organism for accurate detection of bona fide mutations and inter-strain polymorphisms.

Inferring population genetics parameters of evolving viruses using time-series data.

With the advent of deep sequencing techniques, it is now possible to track the evolution of viruses with ever-increasing detail. Here, we present Flexible Inference from Time-Series (FITS)-a computational tool that allows inference of one of three parameters: the fitness of a specific mutation, the mutation rate or the population size from genomic time-series sequencing data. FITS was designed first and foremost for analysis of either short-term Evolve & Resequence (E&R) experiments or rapidly recombining populations of viruses. We thoroughly explore the performance of FITS on simulated data and highlight its ability to infer the fitness/mutation rate/population size. We further show that FITS can infer meaningful information even when the input parameters are inexact. In particular, FITS is able to successfully categorize a mutation as advantageous or deleterious. We next apply FITS to empirical data from an E&R experiment on poliovirus where parameters were determined experimentally and demonstrate high accuracy in inference.