Viral Sequence Data Research Articles

Local patterns of spread of influenza A H3N2 virus in coastal Kenya over a 1-year period revealed through virus sequence data.

The patterns of spread of influenza A viruses in local populations in tropical and sub-tropical regions are unclear due to sparsity of representative spatiotemporal sequence data. We sequenced and analyzed 58 influenza A(H3N2) virus genomes sampled between December 2015 and December 2016 from nine health facilities within the Kilifi Health and Demographic Surveillance System (KHDSS), a predominantly rural region, covering approximately 891km2 along the Kenyan coastline. The genomes were compared with 1571 contemporaneous global sequences from 75 countries. We observed at least five independent introductions of A(H3N2) viruses into the region during the one-year period, with the importations originating from Africa, Europe, and North America. We also inferred 23 virus location transition events between the nine facilities included in the study. International virus imports into the study area were captured at the facilities of Chasimba, Matsangoni, Mtondia, and Mavueni, while all four exports from the region were captured from the Chasimba facility, all occurring to Africa destinations. A strong spatial clustering of virus strains at all locations was observed associated with local evolution. Our study shows that influenza A(H3N2) virus epidemics in local populations appear to be characterized by limited introductions followed by significant local spread and evolution. Knowledge of the viral lineages that circulate within specific populations in understudied tropical and subtropical regions is required to understand the full diversity and global ecology of influenza viruses and to inform vaccination strategies within these populations.

Human Orthohantavirus disease prevalence and genotype distribution in the U.S., 2008–2020: a retrospective observational study

In the United States (U.S.), hantavirus pulmonary syndrome (HPS) and non-HPS hantavirus infection are nationally notifiable diseases. Criteria for identifying human cases are based on clinical symptoms (HPS or non-HPS) and acute diagnostic results (IgM+, rising IgG+ titers, RT-PCR+, or immunohistochemistry (IHC)+). Here we provide an overview of diagnostic testing and summarize human Hantavirus disease occurrence and genotype distribution in the U.S. from 2008 to2020. Epidemiological data from the national hantavirus registry was merged with laboratory diagnostic testing results performed at the CDC. Residual hantavirus-positive specimens were sequenced, and the available epidemiological and genetic data sets were linked to conduct a genomic epidemiological study of hantavirus disease in the U.S. From 1993 to 2020, 833 human hantavirus cases have been identified, and from 2008 to 2020, 335 human cases have occurred. Among New World (NW) hantavirus cases detected at the CDC diagnostic laboratory (representing 29.2% of total cases), most (85.0%) were detected during acute disease, however, some convalescent cases were detected in states not traditionally associated with hantavirus infections (Connecticut, Missouri, New Jersey, Pennsylvania, Tennessee, and Vermont). From 1993 to 2020, 94.9% (745/785) of U.S. hantaviruses cases were detected west of the Mississippi with 45.7% (359/785) in the Four Corners region of the U.S. From 2008 to 2020, 67.7% of NW hantavirus cases were detected between the months of March and August. Sequencing of RT-PCR-positive cases demonstrates a geographic separation of Orthohantavirus sinnombreense species [Sin Nombre virus (SNV), New York virus, and Monongahela virus]; however, there is a large gap in viral sequence data from the Northwestern and Central U.S. Finally, these data indicate that commercial IgM assays are not concordant with CDC-developed assays, and that "concordant positive" (i.e., commercial IgM+ and CDC IgM+ results) specimens exhibit clinical characteristics of hantavirus disease. Hantaviral disease is broadly distributed in the contiguous U.S, viral variants are localised to specific geographic regions, and hantaviral disease infrequently detected in most Southeastern states. Discordant results between two diagnostic detection methods highlight the need for an improved standardised testing plan in the U.S. Hantavirus surveillance and detection will continue to improve with clearly defined, systematic reporting methods, as well as explicit guidelines for clinical characterization and diagnostic criteria. This work was funded by core funds provided to the Viral Special Pathogens Branch at CDC.

A Systematic Bioinformatics Approach for Mapping the Minimal Set of a Viral Peptidome

AbstractSequence changes in viral genomes generate protein sequence diversity that enables viruses to evade the host immune system, hindering the development of effective preventive and therapeutic interventions. The massive proliferation of sequence data provides unprecedented opportunities to study viral adaptation and evolution. An alignment‐free approach removes various restrictions posed by an alignment‐dependent approach for studying sequence diversity. The publicly available tool, UNIQmin, offers an alignment‐free approach for studying viral sequence diversity at any given rank of taxonomy lineage and is big data ready. The tool performs an exhaustive search to determine the minimal set of sequences required to capture the peptidome diversity within a given dataset. This compression is possible through the removal of identical sequences and unique sequences that do not contribute effectively to the peptidome diversity pool. Herein, we describe a detailed four‐part protocol utilizing UNIQmin to generate the minimal set for the purpose of viral diversity analyses, alignment‐free at any rank of the taxonomy lineage, using the recent global public health threat Monkeypox virus (MPX) sequence data as a case study. The protocol enables a systematic bioinformatics approach to study sequence diversity across taxonomic lineages, which is crucial for our future preparedness against viral epidemics. This is particularly important when data are abundant, freely available, and alignment is not an option. © 2024 Wiley Periodicals LLC.Basic Protocol 1: Tool installation and input file preparationBasic Protocol 2: Generation of a minimal set of sequences for a given datasetBasic Protocol 3: Comparative minimal set analysis across taxonomic lineage ranksBasic Protocol 4: Factors affecting the minimal set of sequences

A hepatitis B virus (HBV) sequence variation graph improves alignment and sample-specific consensus sequence construction.

Nearly 300 million individuals live with chronic hepatitis B virus (HBV) infection (CHB), for which no curative therapy is available. As viral diversity is associated with pathogenesis and immunological control of infection, improved methods to characterize this diversity could aid drug development efforts. Conventionally, viral sequencing data are mapped/aligned to a reference genome, and only the aligned sequences are retained for analysis. Thus, reference selection is critical, yet selecting the most representative reference a priori remains difficult. We investigate an alternative pangenome approach which can combine multiple reference sequences into a graph which can be used during alignment. Using simulated short-read sequencing data generated from publicly available HBV genomes and real sequencing data from an individual living with CHB, we demonstrate alignment to a phylogenetically representative 'genome graph' can improve alignment, avoid issues of reference ambiguity, and facilitate the construction of sample-specific consensus sequences more genetically similar to the individual's infection. Graph-based methods can, therefore, improve efforts to characterize the genetics of viral pathogens, including HBV, and have broader implications in host-pathogen research.

Genome-resolved metagenomics identifies novel active microbes in biogeochemical cycling within methanol-enriched soil.

Metagenome assembled genomes (MAGs), generated from sequenced 13C-labelled DNA from 13C-methanol enriched soils, were binned using an ensemble approach. This method produced a significantly larger number of higher-quality MAGs compared to direct binning approaches. These MAGs represent both the primary methanol utilizers and the secondary utilizers labelled via cross-feeding and predation on the labelled methylotrophs, including numerous uncultivated taxa. Analysis of these MAGs enabled the identification of multiple metabolic pathways within these active taxa that have climatic relevance relating to nitrogen, sulfur and trace gas metabolism. This includes denitrification, dissimilatory nitrate reduction to ammonium, ammonia oxidation and metabolism of organic sulfur species. The binning of viral sequence data also yielded extensive viral MAGs, identifying active viral replication by both lytic and lysogenic phages within the methanol-enriched soils. These MAGs represent a valuable resource for characterizing biogeochemical cycling within terrestrial environments.

Prevalence of persistent SARS-CoV-2 in a large community surveillance study.

Persistent SARS-CoV-2 infections may act as viral reservoirs that could seed future outbreaks1-5, give rise to highly divergent lineages6-8 and contribute to cases with post-acute COVID-19 sequelae (long COVID)9,10. However, the population prevalence of persistent infections, their viral load kinetics and evolutionary dynamics over the course of infections remain largely unknown. Here, using viral sequence data collected as part of a national infection survey, we identified 381 individuals with SARS-CoV-2 RNA at high titre persisting for at least 30 days, of which 54 had viral RNA persisting at least 60 days. We refer to these as 'persistent infections' as available evidence suggests that they represent ongoing viral replication, although the persistence of non-replicating RNA cannot be ruled out in all. Individuals with persistent infection had more than 50% higher odds of self-reporting long COVID than individuals with non-persistent infection. We estimate that 0.1-0.5% of infections may become persistent with typically rebounding high viral loads and last for at least 60 days. In some individuals, we identified many viral amino acid substitutions, indicating periods of strong positive selection, whereas others had no consensus change in the sequences for prolonged periods, consistent with weak selection. Substitutions included mutations that are lineage defining for SARS-CoV-2 variants, at target sites for monoclonal antibodies and/or are commonly found in immunocompromised people11-14. This work has profound implications for understanding and characterizing SARS-CoV-2 infection, epidemiology and evolution.

Inferring HIV transmission patterns from viral deep-sequence data via latent typed point processes.

Viral deep-sequencing data play a crucial role toward understanding disease transmission network flows, providing higher resolution compared to standard Sanger sequencing. To more fully utilize these rich data and account for the uncertainties in outcomes from phylogenetic analyses, we propose a spatial Poisson process model to uncover human immunodeficiency virus (HIV) transmission flow patterns at the population level. We represent pairings of individuals with viral sequence data as typed points, with coordinates representing covariates such as gender and age and point types representing the unobserved transmission statuses (linkage and direction). Points are associated with observed scores on the strength of evidence for each transmission status that are obtained through standard deep-sequence phylogenetic analysis. Our method is able to jointly infer the latent transmission statuses for all pairings and the transmission flow surface on the source-recipient covariate space. In contrast to existing methods, our framework does not require preclassification of the transmission statuses of data points, and instead learns them probabilistically through a fully Bayesian inference scheme. By directly modeling continuous spatial processes with smooth densities, our method enjoys significant computational advantages compared to previous methods that rely on discretization of the covariate space. We demonstrate that our framework can capture age structures in HIV transmission at high resolution, bringing valuable insights in a case study on viral deep-sequencing data from Southern Uganda.

Quantifying the HIV reservoir with dilution assays and deep viral sequencing.

People living with HIV on antiretroviral therapy often have undetectable virus levels by standard assays, but "latent" HIV still persists in viral reservoirs. Eliminating these reservoirs is the goal of HIV cure research. The quantitative viral outgrowth assay (QVOA) is commonly used to estimate the reservoir size, that is, the infectious units per million (IUPM) of HIV-persistent resting CD4+ T cells. A new variation of the QVOA, the ultra deep sequencing assay of the outgrowth virus (UDSA), was recently developed that further quantifies the number of viral lineages within a subset of infected wells. Performing the UDSA on a subset of wells provides additional information that can improve IUPM estimation. This paper considers statistical inference about the IUPM from combined dilution assay (QVOA) and deep viral sequencing (UDSA) data, even when some deep sequencing data are missing. Methods are proposed to accommodate assays with wells sequenced at multiple dilution levels and with imperfect sensitivity and specificity, and a novel bias-corrected estimator is included for small samples. The proposed methods are evaluated in a simulation study, applied to data from the University of North Carolina HIV Cure Center, and implemented in the open-source R package SLDeepAssay.

Diversities and interactions of phages and bacteria in deep-sea sediments as revealed by metagenomics.

Phages are found virtually everywhere, even in extreme environments, and are extremely diverse both in their virion structures and in their genomic content. They are thought to shape the taxonomic and functional composition of microbial communities as well as their stability. A number of studies on laboratory culture and viral metagenomic research provide deeper insights into the abundance, diversity, distribution, and interaction with hosts of phages across a wide range of ecosystems. Although most of these studies focus on easily accessible samples, such as soils, lakes, and shallow oceans, little is known about bathypelagic phages. In this study, through analyzing the 16S rRNA sequencing and viral metagenomic sequencing data of 25 samples collected from five different bathypelagic ecosystems, we detected a high diversity of bacteria and phages, particularly in the cold seep and hydrothermal vent ecosystems, which have stable chemical energy. The relative abundance of phages in these ecosystems was higher than in other three abyssal ecosystems. The low phage/host ratios obtained from host prediction were different from shallow ecosystems and indicated the prevalence of prophages, suggesting the complexity of phage-bacteria interactions in abyssal ecosystems. In the correlation analysis, we revealed several phages-bacteria interaction networks of potential ecological relevance. Our study contributes to a better understanding of the interactions between bathypelagic bacteria and their phages.

Genetic consequences of effective and suboptimal dosing with mutagenic drugs in a hamster model of SARS-CoV-2 infection.

Mutagenic antiviral drugs have shown promise against multiple viruses, but concerns have been raised about whether their use might promote the emergence of new and harmful viral variants. Recently, genetic signatures associated with molnupiravir use have been identified in the global SARS-COV-2 population. Here, we examine the consequences of using favipiravir and molnupiravir to treat SARS-CoV-2 infection in a hamster model, comparing viral genome sequence data collected from (1) untreated hamsters, and (2) from hamsters receiving effective and suboptimal doses of treatment. We identify a broadly linear relationship between drug dose and the extent of variation in treated viral populations, with a high proportion of this variation being composed of variants at frequencies of less than 1 per cent, below typical thresholds for variant calling. Treatment with an effective dose of antiviral drug was associated with a gain of between 7 and 10 variants per viral genome relative to drug-free controls: even after a short period of treatment a population founded by a transmitted virus could contain multiple sequence differences to that of the original host. Treatment with a suboptimal dose of drug showed intermediate gains of variants. No dose-dependent signal was identified in the numbers of single-nucleotide variants reaching frequencies in excess of 5 per cent. We did not find evidence to support the emergence of drug resistance or of novel immune phenotypes. Our study suggests that where onward transmission occurs, a short period of treatment with mutagenic drugs may be sufficient to generate a significant increase in the number of viral variants transmitted.

Elevated HIV Viral Load is Associated with Higher Recombination Rate In Vivo.

HIV's exceptionally high recombination rate drives its intrahost diversification, enabling immune escape and multidrug resistance within people living with HIV. While we know that HIV's recombination rate varies by genomic position, we have little understanding of how recombination varies throughout infection or between individuals as a function of the rate of cellular coinfection. We hypothesize that denser intrahost populations may have higher rates of coinfection and therefore recombination. To test this hypothesis, we develop a new approach (recombination analysis via time series linkage decay or RATS-LD) to quantify recombination using autocorrelation of linkage between mutations across time points. We validate RATS-LD on simulated data under short read sequencing conditions and then apply it to longitudinal, high-throughput intrahost viral sequencing data, stratifying populations by viral load (a proxy for density). Among sampled viral populations with the lowest viral loads (<26,800 copies/mL), we estimate a recombination rate of 1.5×10-5 events/bp/generation (95% CI: 7×10-6 to 2.9×10-5), similar to existing estimates. However, among samples with the highest viral loads (>82,000 copies/mL), our median estimate is approximately 6 times higher. In addition to co-varying across individuals, we also find that recombination rate and viral load are associated within single individuals across different time points. Our findings suggest that rather than acting as a constant, uniform force, recombination can vary dynamically and drastically across intrahost viral populations and within them over time. More broadly, we hypothesize that this phenomenon may affect other facultatively asexual populations where spatial co-localization varies.

Easing genomic surveillance: A comprehensive performance evaluation of long-read assemblers across multi-strain mixture data of HIV-1 and Other pathogenic viruses for constructing a user-friendly bioinformatic pipeline.

Determining the appropriate computational requirements and software performance is essential for efficient genomic surveillance. The lack of standardized benchmarking complicates software selection, especially with limited resources. We developed a containerized benchmarking pipeline to evaluate seven long-read assemblers-Canu, GoldRush, MetaFlye, Strainline, HaploDMF, iGDA, and RVHaplo-for viral haplotype reconstruction, using both simulated and experimental Oxford Nanopore sequencing data of HIV-1 and other viruses. Benchmarking was conducted on three computational systems to assess each assembler's performance, utilizing QUAST and BLASTN for quality assessment. Our findings show that assembler choice significantly impacts assembly time, with CPU and memory usage having minimal effect. Assembler selection also influences the size of the contigs, with a minimum read length of 2,000 nucleotides required for quality assembly. A 4,000-nucleotide read length improves quality further. Canu was efficient among de novo assemblers but not suitable for multi-strain mixtures, while GoldRush produced only consensus assemblies. Strainline and MetaFlye were suitable for metagenomic sequencing data, with Strainline requiring high memory and MetaFlye operable on low-specification machines. Among reference-based assemblers, iGDA had high error rates, RVHaplo showed the best runtime and accuracy but became ineffective with similar sequences, and HaploDMF, utilizing machine learning, had fewer errors with a slightly longer runtime. The HIV-64148 pipeline, containerized using Docker, facilitates easy deployment and offers flexibility to select from a range of assemblers to match computational systems or study requirements. This tool aids in genome assembly and provides valuable information on HIV-1 sequences, enhancing viral evolution monitoring and understanding.

Transmission Bottleneck Size Estimation from De Novo Viral Genetic Variation.

Sequencing of viral infections has become increasingly common over the last decade. Deep sequencing data in particular have proven useful in characterizing the roles that genetic drift and natural selection play in shaping within-host viral populations. They have also been used to estimate transmission bottleneck sizes from identified donor-recipient pairs. These bottleneck sizes quantify the number of viral particles that establish genetic lineages in the recipient host and are important to estimate due to their impact on viral evolution. Current approaches for estimating bottleneck sizes exclusively consider the subset of viral sites that are observed as polymorphic in the donor individual. However, these approaches have the potential to substantially underestimate true transmission bottleneck sizes. Here, we present a new statistical approach for instead estimating bottleneck sizes using patterns of viral genetic variation that arise de novo within a recipient individual. Specifically, our approach makes use of the number of clonal viral variants observed in a transmission pair, defined as the number of viral sites that are monomorphic in both the donor and the recipient but carry different alleles. We first test our approach on a simulated dataset and then apply it to both influenza A virus sequence data and SARS-CoV-2 sequence data from identified transmission pairs. Our results confirm the existence of extremely tight transmission bottlenecks for these 2 respiratory viruses.

Genomic Surveillance of SARS-CoV-2 Sequence Variants at Universities in Southwest Idaho.

Although the impact of the SARS-CoV-2 pandemic on major metropolitan areas is broadly reported and readily available, regions with lower populations and more remote areas in the United States are understudied. The objective of this study is to determine the progression of SARS-CoV-2 sequence variants in a frontier and remote intermountain west state among university-associated communities. This study was conducted at two intermountain west universities from 2020 to 2022. Positive SARS-CoV-2 samples were confirmed by quantitative real-time reverse transcription-polymerase chain reaction and variants were identified by the next-generation sequencing of viral genomes. Positive results were obtained for 5355 samples, representing a positivity rate of 3.5% overall. The median age was 22 years. Viral genomic sequence data were analyzed for 1717 samples and phylogeny was presented. Associations between viral variants, age, sex, and reported symptoms among 1522 samples indicated a significant association between age and the Delta variant (B 1.167.2), consistent with the findings for other regions. An outbreak event of AY122 was detected August-October 2021. A 2-month delay was observed with respect to the timing of the first documented viral infection within this region compared to major metropolitan regions of the US.

Environmental surface monitoring as a noninvasive method for SARS-CoV-2 surveillance in community settings: Lessons from a university campus study

Environmental testing of high-touch objects is a potential noninvasive approach for monitoring population-level trends of SARS-CoV-2 and other respiratory viruses within a defined setting. We aimed to determine the association between SARS-CoV-2 contamination on high-touch environmental surfaces, community level case incidence, and university student health data. Environmental swabs were collected from January 2022 to November 2022 from high-touch objects and surfaces from five locations on a large university campus in Florida, USA. RT-qPCR was used to detect and quantify viral RNA, and a subset of positive samples was analyzed by viral genome sequencing to identify circulating lineages. During the study period, we detected SARS-CoV-2 viral RNA on 90.7 % of 162 tested samples. Levels of environmental viral RNA correlated with trends in community-level activity and case reports from the student health center. A significant positive correlation was observed between the estimated viral gene copy number in environmental samples and the weekly confirmed cases at the university. Viral sequencing data from environmental samples identified lineages concurrently circulating in the local community and state based on genomic surveillance data. Further, we detected emerging variants in environmental samples prior to their identification by clinical genomic surveillance. Our results demonstrate the utility of viral monitoring on high-touch environmental surfaces for SARS-CoV-2 surveillance at a community level. In communities with delayed or limited testing facilities, immediate environmental surface testing may considerably inform epidemic dynamics.

HostNet: improved sequence representation in deep neural networks for virus-host prediction

BackgroundThe escalation of viruses over the past decade has highlighted the need to determine their respective hosts, particularly for emerging ones that pose a potential menace to the welfare of both human and animal life. Yet, the traditional means of ascertaining the host range of viruses, which involves field surveillance and laboratory experiments, is a laborious and demanding undertaking. A computational tool with the capability to reliably predict host ranges for novel viruses can provide timely responses in the prevention and control of emerging infectious diseases. The intricate nature of viral-host prediction involves issues such as data imbalance and deficiency. Therefore, developing highly accurate computational tools capable of predicting virus-host associations is a challenging and pressing demand.ResultsTo overcome the challenges of virus-host prediction, we present HostNet, a deep learning framework that utilizes a Transformer-CNN-BiGRU architecture and two enhanced sequence representation modules. The first module, k-mer to vector, pre-trains a background vector representation of k-mers from a broad range of virus sequences to address the issue of data deficiency. The second module, an adaptive sliding window, truncates virus sequences of various lengths to create a uniform number of informative and distinct samples for each sequence to address the issue of data imbalance. We assess HostNet's performance on a benchmark dataset of “Rabies lyssavirus” and an in-house dataset of “Flavivirus”. Our results show that HostNet surpasses the state-of-the-art deep learning-based method in host-prediction accuracies and F1 score. The enhanced sequence representation modules, significantly improve HostNet's training generalization, performance in challenging classes, and stability.ConclusionHostNet is a promising framework for predicting virus hosts from genomic sequences, addressing challenges posed by sparse and varying-length virus sequence data. Our results demonstrate its potential as a valuable tool for virus-host prediction in various biological contexts. Virus-host prediction based on genomic sequences using deep neural networks is a promising approach to identifying their potential hosts accurately and efficiently, with significant impacts on public health, disease prevention, and vaccine development.

Evaluation ofrecombination detection methods forviral sequencing.

Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods (RDMs) have been developed over the past two decades; however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed eight RDMs: PhiPack (Profile), 3SEQ, GENECONV, recombination detection program (RDP) (OpenRDP), MaxChi (OpenRDP), Chimaera (OpenRDP), UCHIME (VSEARCH), and gmos; to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Furthermore, we provide a practical example for the analysis and validation of empirical data. We find that RDMs need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.

A Snapshot on the Genomic Epidemiology of Turkey Reovirus Infections, Hungary.

Reovirus infections in turkeys are associated with arthritis and lameness. Viral genome sequence data are scarce, which makes an accurate description of the viral evolution and epidemiology difficult. In this study, we isolated and characterized turkey reoviruses from Hungary. The isolates were identified in 2016; these isolates were compared with earlier Hungarian turkey reovirus strains and turkey reoviruses isolated in the 2010s in the United States. Gene-wise sequence and phylogenetic analyses identified the cell-receptor binding protein and the main neutralization antigen, σC, to be the most conserved. The most genetically diverse gene was another surface antigen coding gene, μB. This gene was shown to undergo frequent reassortment among chicken and turkey origin reoviruses. Additional reassortment events were found primarily within members of the homologous turkey reovirus clade. Our data showed evidence for low variability among strains isolated from independent outbreaks, a finding that suggests a common source of turkey reoviruses in Hungarian turkey flocks. Given that commercial vaccines are not available, identification of the source of these founder virus strains would permit a more efficient prevention of disease outbreaks before young birds are settled to fattening facilities.

Bacterial and Bacteriophage Consortia Are Associated with Protective Intestinal Immuno-Modulatory Metabolites in Allogeneic Stem Cell Transplantation Patients

The human microbiome is a predictor of clinical outcome in patients undergoing allogeneic hematopoietic stem cell transplantation (allo-SCT). Besides bacteria, fungi and viruses as well as intestinal microbiota-derived metabolites are involved. However, it is still unclear how dynamic shifts in these three kingdoms contribute to the production of intestinal metabolites, how metabolites are impacted by graft-versus-host disease (GvHD) or antibiotics and whether they are associated with clinical outcome. We report the two-year follow-up of a prospective, observational, longitudinal cohort of allo-SCT patients (n=78) at two German transplantation centers that combined three-kingdom (bacteria, fungi, viruses) analysis of intestinal microbial communities with targeted metabolomics ( Figure 1). Using Multi-omics factor analysis (MOFA), we uncovered a functional microbiome signature of bacteria from the Lachnospiraceae and Oscillospiraceae families and their associated bacteriophages, which correlated with the production of immuno-modulatory metabolites (IMMs) including short-chain fatty acids (SCFAs), branched-chain fatty acids (BCFA), metabolites associated with induction of type-I IFN signaling (IIMs) and immuno-modulatory secondary bile acids ( Figure 2). We established an Immuno-modulatory Metabolite Risk Index (IMM-RI) consisting of five index IMMs, which was associated with improved survival, less transplant-related mortality and reduced relapse rate. Onset of gastrointestinal GvHD and exposure to antibiotics significantly impacted intestinal levels of protective IMMs. Using whole shotgun metagenomic sequencing, we observed that in IMM-RI low-risk patients, sustained production of protective IMMs was associated with a high abundance of microbial SCFA biosynthesis pathways, specifically butyric acid via butyryl-CoA:acetate CoA-transferase (BCoAT). Through genome assembly from viral metagenomic sequencing data, we detected two distinct bacteriophages which encoded BCoAT as an auxiliary metabolic gene. They were more abundant in IMM-RI low-risk patients and positively correlated with intestinal butyric acid levels, suggesting that these bacteriophages may modulate bacterial metabolite biosynthesis. Our study identifies a specific microbiome signature associated with protective IMMs that could improve fecal microbiota transplantation (FMT) donor selection and provides a rationale for the development of engineered metabolite-producing consortia and defined metabolite combination drugs as novel microbiome-based therapies for cancer patients.

In silico prediction of immune-escaping hot spots for future COVID-19 vaccine design

The COVID-19 pandemic has had a widespread impact on a global scale, and the evolution of considerable dominants has already taken place. Some variants contained certain key mutations located on the receptor binding domain (RBD) of spike protein, such as E484K and N501Y. It is increasingly worrying that these variants could impair the efficacy of current vaccines or therapies. Therefore, analyzing and predicting the high-risk mutations of SARS-CoV-2 spike glycoprotein is crucial to design future vaccines against the different variants. In this work, we proposed an in silico approach, immune-escaping score (IES), to predict high-risk immune-escaping hot spots on the receptor-binding domain (RBD), implemented through integrated delta binding free energy measured by computational mutagenesis of spike-antibody complexes and mutation frequency calculated from viral genome sequencing data. We identified 23 potentially immune-escaping mutations on the RBD by using IES, nine of which occurred in omicron variants (R346K, K417N, N440K, L452Q, L452R, S477N, T478K, F490S, and N501Y), despite our dataset being curated before the omicron first appeared. The highest immune-escaping score (IES = 1) was found for E484K, which agrees with recent studies stating that the mutation significantly reduced the efficacy of neutralization antibodies. Furthermore, our predicted delta binding free energy and IES show a high correlation with high-throughput deep mutational scanning data (Pearson’s r = 0.70) and experimentally measured neutralization titers data (mean Pearson’s r = −0.80). In summary, our work presents a new method to identify the potentially immune-escaping mutations on the RBD and provides valuable insights into future COVID-19 vaccine design.