Pathogen Genomics in Public Health.

Rapid advances in DNA sequencing technology ("next-generation sequencing") have inspired optimism about the potential of human genomics for "precision medicine." Meanwhile, pathogen genomics is already delivering "precision public health" through more effective investigations of outbreaks of foodborne illnesses, better-targeted tuberculosis control, and more timely and granular influenza surveillance to inform the selection of vaccine strains. In this article, we describe how public health agencies have been adopting pathogen genomics to improve their effectiveness in almost all domains of infectious disease. This momentum is likely to continue, given the ongoing development in sequencing and sequencing-related technologies.

Viral deep sequencing needs an adaptive approach: IRMA, the iterative refinement meta-assembler.

BACKGROUND: Deep sequencing makes it possible to observe low-frequency viral variants and sub-populations with greater accuracy and sensitivity than ever before. Existing platforms can be used to multiplex a large number of samples; however, analysis of the resulting data is complex and involves separating barcoded samples and various read manipulation processes ending in final assembly. Many assembly tools were designed with larger genomes and higher fidelity polymerases in mind and do not perform well with reads derived from highly variable viral genomes. Reference-based assemblers may leave gaps in viral assemblies while de novo assemblers may struggle to assemble unique genomes. RESULTS: The IRMA (iterative refinement meta-assembler) pipeline solves the problem of viral variation by the iterative optimization of read gathering and assembly. As with all reference-based assembly, reads are included in assembly when they match consensus template sets; however, IRMA provides for on-the-fly reference editing, correction, and optional elongation without the need for additional reference selection. This increases both read depth and breadth. IRMA also focuses on quality control, error correction, indel reporting, variant calling and variant phasing. In fact, IRMA's ability to detect and phase minor variants is one of its most distinguishing features. We have built modules for influenza and ebolavirus. We demonstrate usage and provide calibration data from mixture experiments. Methods for variant calling, phasing, and error estimation/correction have been redesigned to meet the needs of viral genomic sequencing. CONCLUSION: IRMA provides a robust next-generation sequencing assembly solution that is adapted to the needs and characteristics of viral genomes. The software solves issues related to the genetic diversity of viruses while providing customized variant calling, phasing, and quality control. IRMA is freely available for non-commercial use on Linux and Mac OS X and has been parallelized for high-throughput computing.

Monitoring and reporting the US COVID-19 vaccination effort.

Immunizations are an important tool to reduce the burden of vaccine preventable diseases and improve population health.1 High-quality immunization data is essential to inform clinical and public health interventions and respond to outbreaks of vaccine-preventable diseases. To track COVID-19 vaccines and vaccinations, CDC established an integrated network that included vaccination provider systems, health information exchange systems, immunization information systems, pharmacy and dialysis systems, vaccine ordering systems, electronic health records, and tools to support mass vaccination clinics. All these systems reported data to CDC's COVID-19 response system (either directly or indirectly) where it was processed, analyzed, and disseminated. This unprecedented vaccine tracking effort provided essential information for public health officials that was used to monitor the COVID-19 response and guide decisions. This paper will describe systems, processes, and policies that enabled monitoring and reporting of COVID-19 vaccination efforts and share challenges and lessons learned for future public health emergency responses.

Influenza A Virus Field Surveillance at a Swine-Human Interface.

While working overnight at a swine exhibition, we identified an influenza A virus (IAV) outbreak in swine, Nanopore sequenced 13 IAV genomes from samples we collected, and predicted in real time that these viruses posed a novel risk to humans due to genetic mismatches between the viruses and current prepandemic candidate vaccine viruses (CVVs). We developed and used a portable IAV sequencing and analysis platform called Mia (Mobile Influenza Analysis) to complete and characterize full-length consensus genomes approximately 18 h after unpacking the mobile lab. Exhibition swine are a known source for zoonotic transmission of IAV to humans and pose a potential pandemic risk. Genomic analyses of IAV in swine are critical to understanding this risk, the types of viruses circulating in swine, and whether current vaccines developed for use in humans would be predicted to provide immune protection. Nanopore sequencing technology has enabled genome sequencing in the field at the source of viral outbreaks or at the bedside or pen-side of infected humans and animals. The acquired data, however, have not yet demonstrated real-time, actionable public health responses. The Mia system rapidly identified three genetically distinct swine IAV lineages from three subtypes, A(H1N1), A(H3N2), and A(H1N2). Analysis of the hemagglutinin (HA) sequences of the A(H1N2) viruses identified >30 amino acid differences between the HA1 of these viruses and the most closely related CVV. As an exercise in pandemic preparedness, all sequences were emailed to CDC collaborators who initiated the development of a synthetically derived CVV.IMPORTANCE Swine are influenza virus reservoirs that have caused outbreaks and pandemics. Genomic characterization of these viruses enables pandemic risk assessment and vaccine comparisons, though this typically occurs after a novel swine virus jumps into humans. The greatest risk occurs where large groups of swine and humans comingle. At a large swine exhibition, we used Nanopore sequencing and on-site analytics to interpret 13 swine influenza virus genomes and identified an influenza virus cluster that was genetically highly varied to currently available vaccines. As part of the National Strategy for Pandemic Preparedness exercises, the sequences were emailed to colleagues at the CDC who initiated the development of a synthetically derived vaccine designed to match the viruses at the exhibition. Subsequently, this virus caused 14 infections in humans and was the dominant U.S. variant virus in 2018.

Author Correction: Direct RNA Sequencing of the Coding Complete Influenza A Virus Genome.

A correction to this article has been published and is linked from the HTML and PDF versions of this paper. The error has been fixed in the paper.

Direct RNA Sequencing of the Coding Complete Influenza A Virus Genome.

For the first time, a coding complete genome of an RNA virus has been sequenced in its original form. Previously, RNA was sequenced by the chemical degradation of radiolabeled RNA, a difficult method that produced only short sequences. Instead, RNA has usually been sequenced indirectly by copying it into cDNA, which is often amplified to dsDNA by PCR and subsequently analyzed using a variety of DNA sequencing methods. We designed an adapter to short highly conserved termini of the influenza A virus genome to target the (-) sense RNA into a protein nanopore on the Oxford Nanopore MinION sequencing platform. Utilizing this method with total RNA extracted from the allantoic fluid of influenza rA/Puerto Rico/8/1934 (H1N1) virus infected chicken eggs (EID50 6.8 × 109), we demonstrate successful sequencing of the coding complete influenza A virus genome with 100% nucleotide coverage, 99% consensus identity, and 99% of reads mapped to influenza A virus. By utilizing the same methodology one can redesign the adapter in order to expand the targets to include viral mRNA and (+) sense cRNA, which are essential to the viral life cycle, or other pathogens. This approach also has the potential to identify and quantify splice variants and base modifications, which are not practically measurable with current methods.

Improving pandemic influenza risk assessment.

Assessing the pandemic risk posed by specific non-human influenza A viruses is an important goal in public health research. As influenza virus genome sequencing becomes cheaper, faster, and more readily available, the ability to predict pandemic potential from sequence data could transform pandemic influenza risk assessment capabilities. However, the complexities of the relationships between virus genotype and phenotype make such predictions extremely difficult. The integration of experimental work, computational tool development, and analysis of evolutionary pathways, together with refinements to influenza surveillance, has the potential to transform our ability to assess the risks posed to humans by non-human influenza viruses and lead to improved pandemic preparedness and response.

Analytical challenges for emerging public health surveillance.

The root of effective disease control and prevention is an informed understanding of the epidemiology of a particular disease based on sound scientific interpretation of evidence. Such evidence must frequently be transformed from raw data into consumable information before it can be used for making decisions, determining policy, and conducting programs. However, the work of building such evidence in public health practice--doing the right thing at the right time--is essentially hidden from view. Surveillance involves acquiring, analyzing, and interpreting data and information from several sources across various systems. Achieving the goals and objectives of surveillance investments requires attention to analytic requirements of such systems. The process requires computer programming, statistical reasoning, subject matter expertise, often modeling, and effective communication skills.

Evaluation of affymetrix severe acute respiratory syndrome resequencing GeneChips in characterization of the genomes of two strains of coronavirus infecting humans.

Severe acute respiratory syndrome (SARS) was discovered during a recent global outbreak of atypical pneumonia. A number of immunologic and molecular studies of the clinical samples led to the conclusion that a novel coronavirus (SARS-CoV) was associated with the outbreak. Later, a SARS resequencing GeneChip was developed by Affymetrix to characterize the complete genome of SARS-CoV on a single GeneChip. The present study was carried out to evaluate the performance of SARS resequencing GeneChips. Two human SARS-CoV strains (CDC#200301157 and Urbani) were resequenced by the SARS GeneChips. Five overlapping PCR amplicons were generated for each strain and hybridized with these GeneChips. The successfully hybridized GeneChips generated nucleotide sequences of nearly complete genomes for the two SARS-CoV strains with an average call rate of 94.6%. Multiple alignments of nucleotide sequences obtained from SARS GeneChips and conventional sequencing revealed full concordance. Furthermore, the GeneChip-based analysis revealed no additional polymorphic sites. The results of this study suggest that GeneChip-based genome characterization is fast and reproducible. Thus, SARS resequencing GeneChips may be employed as an alternate tool to obtain genome sequences of SARS-CoV strains pathogenic for humans in order to further understand the transmission dynamics of these viruses.

Solution NMR studies of the A beta(1-40) and A beta(1-42) peptides establish that the Met35 oxidation state affects the mechanism of amyloid formation.

The pathogenesis of Alzheimer's disease is characterized by the aggregation and fibrillation of the 40-residue A beta(1-40) and 42-residue A beta(1-42) peptides into amyloid plaques. The structural changes associated with the conversion of monomeric A beta peptide building blocks into multimeric fibrillar beta-strand aggregates remain unknown. Recently, we established that oxidation of the methionine-35 side chain to the sulfoxide (Met35(red) --> Met35(ox)) significantly impedes the rate of aggregation and fibrillation of the A beta peptide. To explore this effect at greater resolution, we carefully compared the (1)H, (15)N, and (13)C NMR chemical shifts of four A beta peptides that had the Met35 reduced or oxidized (A beta(1-40)Met35(red), A beta(1-40)Met35(ox), A beta(1-42)Met35(red), and A beta(1-42)Met35(ox)). With the use of a special disaggregation protocol, the highly aggregation prone A beta peptides could be studied at higher, millimolar concentrations (as required by NMR) in aqueous solution at neutral pH, remaining largely monomeric at 5 degrees C as determined by sedimentation equilibrium studies. The NOE, amide-NH temperature coefficients, and chemical shift indices of the (1)H alpha, (13)C alpha, and (13)C beta established that the four peptides are largely random, extended chain structures, with the Met35(ox) reducing the propensity for beta-strand structure at two hydrophobic regions (Leu17-Ala21 and Ile31-Val36), and turn- or bendlike structures at Asp7-Glu11 and Phe20-Ser26. Additional NMR studies monitoring changes that occur during aging at 37 degrees C established that, along with a gradual loss of signal/noise, the Met35(ox) significantly hindered upfield chemical shift movements of the 2H NMR signals for the His6, His13, and His14 side chains. Taken together, the present NMR studies demonstrate that the Met35(red) --> Met35(ox) conversion prevents aggregation by reducing both hydrophobic and electrostatic association and that the A beta(1-40)Met35(red), A beta(1-40)Met35(ox), A beta(1-42)Met35(red), and A beta(1-42)Met35(ox) peptides may associate differently, through specific, sharp changes in structure during the initial stages of aggregation.