PARNAS: Objectively Selecting the Most Representative Taxa on a Phylogeny

The use of next-generation sequencing technology has enabled phylogenetic studies with hundreds of thousands of taxa. Such large-scale phylogenies have become a critical component in genomic epidemiology in pathogens such as SARS-CoV-2 and influenza A virus. However, detailed phenotypic characterization of pathogens or generating a computationally tractable dataset for detailed phylogenetic analyses requires objective subsampling of taxa. To address this need, we propose parnas, an objective and flexible algorithm to sample and select taxa that best represent observed diversity by solving a generalized k-medoids problem on a phylogenetic tree. parnas solves this problem efficiently and exactly by novel optimizations and adapting algorithms from operations research. For more nuanced selections, taxa can be weighted with metadata or genetic sequence parameters, and the pool of potential representatives can be user-constrained. Motivated by influenza A virus genomic surveillance and vaccine design, parnas can be applied to identify representative taxa that optimally cover the diversity in a phylogeny within a specified distance radius. We demonstrated that parnas is more efficient and flexible than existing approaches. To demonstrate its utility, we applied parnas to 1) quantify SARS-CoV-2 genetic diversity over time, 2) select representative influenza A virus in swine genes derived from over 5 years of genomic surveillance data, and 3) identify gaps in H3N2 human influenza A virus vaccine coverage. We suggest that our method, through the objective selection of representatives in a phylogeny, provides criteria for quantifying genetic diversity that has application in the the rational design of multivalent vaccines and genomic epidemiology. PARNAS is available at https://github.com/flu-crew/parnas.

Detecting Unbiased Associations in Large Data Sets

Maximal information coefficient (MIC) explores the associations between pairwise variables in complex relationships. It approaches the correlation by optimized partition on the axis. However, when the relationships meet special noise, MIC may overestimate the correlated value, which leads to the misidentification of the relationship without noiseless. In this article, a novel method of weighted information coefficient mean (WICM) is proposed to detect unbiased associations in large data sets. First, we mathematically analyze the cause of giving an abnormal correlation value to a noisy relationship. Then, the WICM is presented in two core steps. One is to detect the potential overestimation from the relationships with high value, and the other is to rectify the overestimation by calculating information coefficient mean instead of just selecting the maximum element in the characteristic matrix. Finally, experiments in functional relationships and real-world data relationships show that the overestimation can be solved by WICM with both feasibility and effectiveness.

The United States Swine Pathogen Database: integrating veterinary diagnostic laboratory sequence data to monitor emerging pathogens of swine

Veterinary diagnostic laboratories derive thousands of nucleotide sequences from clinical samples of swine pathogens such as porcine reproductive and respiratory syndrome virus (PRRSV), Senecavirus A and swine enteric coronaviruses. In addition, next generation sequencing has resulted in the rapid production of full-length genomes. Presently, sequence data are released to diagnostic clients but are not publicly available as data may be associated with sensitive information. However, these data can be used for field-relevant vaccines; determining where and when pathogens are spreading; have relevance to research in molecular and comparative virology; and are a component in pandemic preparedness efforts. We have developed a centralized sequence database that integrates private clinical data using PRRSV data as an exemplar, alongside publicly available genomic information. We implemented the Tripal toolkit, a collection of Drupal modules that are used to manage, visualize and disseminate biological data stored within the Chado database schema. New sequences sourced from diagnostic laboratories contain: genomic information; date of collection; collection location; and a unique identifier. Users can download annotated genomic sequences using a customized search interface that incorporates data mined from published literature; search for similar sequences using BLAST-based tools; and explore annotated reference genomes. Additionally, custom annotation pipelines have determined species, the location of open reading frames and nonstructural proteins and the occurrence of putative frame shifts. Eighteen swine pathogens have been curated. The database provides researchers access to sequences discovered by veterinary diagnosticians, allowing for epidemiological and comparative virology studies. The result will be a better understanding on the emergence of novel swine viruses and how these novel strains are disseminated in the USA and abroad. Database URLhttps://swinepathogendb.org.

Evidence for a recombinant origin of HIV-1 Group M from genomic variation

Reconstructing the early dynamics of the HIV-1 pandemic can provide crucial insights into the socioeconomic drivers of emerging infectious diseases in human populations, including the roles of urbanization and transportation networks. Current evidence indicates that the global pandemic comprising almost entirely of HIV-1/M originated around the 1920s in central Africa. However, these estimates are based on molecular clock estimates that are assumed to apply uniformly across the virus genome. There is growing evidence that recombination has played a significant role in the early history of the HIV-1 pandemic, such that different regions of the HIV-1 genome have different evolutionary histories. In this study, we have conducted a dated-tip analysis of all near full-length HIV-1/M genome sequences that were published in the GenBank database. We used a sliding window approach similar to the 'bootscanning' method for detecting breakpoints in inter-subtype recombinant sequences. We found evidence of substantial variation in estimated root dates among windows, with an estimated mean time to the most recent common ancestor of 1922. Estimates were significantly autocorrelated, which was more consistent with an early recombination event than with stochastic error variation in phylogenetic reconstruction and dating analyses. A piecewise regression analysis supported the existence of at least one recombination breakpoint in the HIV-1/M genome with interval-specific means around 1929 and 1913, respectively. This analysis demonstrates that a sliding window approach can accommodate early recombination events outside the established nomenclature of HIV-1/M subtypes, although it is difficult to incorporate the earliest available samples due to their limited genome coverage.

Porcine reproductive and respiratory disease virus: Evolution and recombination yields distinct ORF5 RFLP 1-7-4 viruses with individual pathogenicity

Recent cases of porcine reproductive and respiratory syndrome virus (PRRSV) infection in United States swine-herds have been associated with high mortality in piglets and severe morbidity in sows. Analysis of the ORF5 gene from such clinical cases revealed a unique restriction fragment polymorphism (RFLP) of 1-7-4. The genome diversity of seventeen of these viruses (81.4% to 99.8% identical; collected 2013-2015) and the pathogenicity of 4 representative viruses were compared to that of SDSU73, a known moderately virulent strain. Recombination analyses revealed genomic breakpoints in structural and nonstructural regions of the genomes with evidence for recombination events between lineages. Pathogenicity varied between the isolates and the patterns were not consistent. IA/2014/NADC34, IA/2013/ISU-1 and IN/2014/ISU-5 caused more severe disease, and IA/2014/ISU-2 did not cause pyrexia and had little effect on pig growth. ORF5 RFLP genotyping was ineffectual in providing insight into isolate pathogenicity and that other parameters of virulence remain to be identified.

A Phylogeny-Based Global Nomenclature System and Automated Annotation Tool for H1 Hemagglutinin Genes from Swine Influenza A Viruses

The H1 subtype of influenza A viruses (IAVs) has been circulating in swine since the 1918 human influenza pandemic. Over time, and aided by further introductions from nonswine hosts, swine H1 viruses have diversified into three genetic lineages. Due to limited global data, these H1 lineages were named based on colloquial context, leading to a proliferation of inconsistent regional naming conventions. In this study, we propose rigorous phylogenetic criteria to establish a globally consistent nomenclature of swine H1 virus hemagglutinin (HA) evolution. These criteria applied to a data set of 7,070 H1 HA sequences led to 28 distinct clades as the basis for the nomenclature. We developed and implemented a web-accessible annotation tool that can assign these biologically informative categories to new sequence data. The annotation tool assigned the combined data set of 7,070 H1 sequences to the correct clade more than 99% of the time. Our analyses indicated that 87% of the swine H1 viruses from 2010 to the present had HAs that belonged to 7 contemporary cocirculating clades. Our nomenclature and web-accessible classification tool provide an accurate method for researchers, diagnosticians, and health officials to assign clade designations to HA sequences. The tool can be updated readily to track evolving nomenclature as new clades emerge, ensuring continued relevance. A common global nomenclature facilitates comparisons of IAVs infecting humans and pigs, within and between regions, and can provide insight into the diversity of swine H1 influenza virus and its impact on vaccine strain selection, diagnostic reagents, and test performance, thereby simplifying communication of such data. IMPORTANCE A fundamental goal in the biological sciences is the definition of groups of organisms based on evolutionary history and the naming of those groups. For influenza A viruses (IAVs) in swine, understanding the hemagglutinin (HA) genetic lineage of a circulating strain aids in vaccine antigen selection and allows for inferences about vaccine efficacy. Previous reporting of H1 virus HA in swine relied on colloquial names, frequently with incriminating and stigmatizing geographic toponyms, making comparisons between studies challenging. To overcome this, we developed an adaptable nomenclature using measurable criteria for historical and contemporary evolutionary patterns of H1 global swine IAVs. We also developed a web-accessible tool that classifies viruses according to this nomenclature. This classification system will aid agricultural production and pandemic preparedness through the identification of important changes in swine IAVs and provides terminology enabling discussion of swine IAVs in a common context among animal and human health initiatives.

A New Algorithm to Optimize Maximal Information Coefficient

The maximal information coefficient (MIC) captures dependences between paired variables, including both functional and non-functional relationships. In this paper, we develop a new method, ChiMIC, to calculate the MIC values. The ChiMIC algorithm uses the chi-square test to terminate grid optimization and then removes the restriction of maximal grid size limitation of original ApproxMaxMI algorithm. Computational experiments show that ChiMIC algorithm can maintain same MIC values for noiseless functional relationships, but gives much smaller MIC values for independent variables. For noise functional relationship, the ChiMIC algorithm can reach the optimal partition much faster. Furthermore, the MCN values based on MIC calculated by ChiMIC can capture the complexity of functional relationships in a better way, and the statistical powers of MIC calculated by ChiMIC are higher than those calculated by ApproxMaxMI. Moreover, the computational costs of ChiMIC are much less than those of ApproxMaxMI. We apply the MIC values tofeature selection and obtain better classification accuracy using features selected by the MIC values from ChiMIC.

Characterization of co-circulating swine influenza A viruses in North America and the identification of a novel H1 genetic clade with antigenic significance

Multiple genetically and antigenically distinct hemagglutinin genes of the H1 and H3 influenza A virus (IAV) subtypes co-circulate in North American swine. This diversity has evolved by repeated transmission of IAVs from humans to swine and subsequent antigenic drift in swine. To understand the evolutionary dynamics of these diverse HA lineages in North American swine, we undertook a phylogenetic analysis of 1576 H1 and 607 H3 HA gene segments, as well as 834 N1 and 1293 N2 NA gene segments, and 2126 M gene segments. These data revealed yearly co-circulation of H1N1, H1N2, and H3N2 viruses, with three HA clades representing the majority of the HA sequences: of the H1 viruses, 42% were classified as H1δ1 and 40.6% were classified as H1γ; and of the H3 viruses 53% were classified as cluster IV-A H3N2. We detected a genetically distinct minor clade consisting of 37 H1 viruses isolated between 2003 and 2013, which we classified as H1γ-2. We estimated that this clade circulated in swine since approximately 1995, but it was not detected in swine until 2003. Though this clade only represents 1.07% of swine H1 sequences reported over the past 10 years, hemagglutination inhibition (HI) assays demonstrated that representatives of this clade of viruses are antigenically distinct, and, when measured using antigenic cartography, were as many as 7 antigenic units from other H1γ viruses. Therefore vaccines against the contemporary H1γ viruses are not likely to cross-protect against γ-2 viruses. The long-term circulation of these γ-2 viruses suggests that minor populations of viruses may be underreported in the national dataset given the long branch lengths and gaps in detections. The identification of these γ-2 viruses demonstrates the need for robust surveillance to capture the full diversity IAVs in swine in the USA and the importance of antigenic drift in the diversification and emergence of new antigenic variants in swine, which complicates vaccine design.

Vaccine design: emerging concepts and renewed optimism

Arguably, vaccination represents the single most effective medical intervention ever developed. Yet, vaccines have failed to provide any or adequate protection against some of the most significant global diseases. The pathogens responsible for these vaccine-recalcitrant diseases have properties that allow them to evade immune surveillance and misdirect or eliminate the immune response. However, genomic and systems biology tools, novel adjuvants and delivery systems, and refined molecular insight into protective immunity have started to redefine the landscape, and results from recent efficacy trials of HIV and malaria vaccines have instilled hope that another golden age of vaccines may be on the horizon.

Porcine reproductive and respiratory syndrome virus vaccines: current status and strategies to a universal vaccine

Porcine reproductive and respiratory syndrome virus (PRRSV) is the causative agent of PRRS, the most significant infectious disease currently affecting swine industry worldwide. In the United States alone, the economic losses caused by PRRS amount to more than 560 million US dollars every year. Due to immune evasion strategies and the antigenic heterogeneity of the virus, current commercial PRRSV vaccines (killed-virus and modified-live vaccines) are of unsatisfactory efficacy, especially against heterologous infection. Continuous efforts have been devoted to develop better PRRSV vaccines. Experimental PRRSV vaccines, including live attenuated vaccines, recombinant vectors expressing PRRSV viral proteins, DNA vaccines and plant-made subunit vaccines, have been developed. However, the genetic and antigenic heterogeneity of the virus limits the value of almost all of the PRRSV vaccines tested. Developing a universal vaccine that can provide broad protection against circulating PRRSV strains has become a major challenge for current vaccine development. This paper reviews current status of PRRSV vaccine development and discusses strategies to develop a universal PRRSV vaccine.

Minerva and minepy: a C engine for the MINE suite and its R, Python and MATLAB wrappers

We introduce a novel implementation in ANSI C of the MINE family of algorithms for computing maximal information-based measures of dependence between two variables in large datasets, with the aim of a low memory footprint and ease of integration within bioinformatics pipelines. We provide the libraries minerva (with the R interface) and minepy for Python, MATLAB, Octave and C++. The C solution reduces the large memory requirement of the original Java implementation, has good upscaling properties and offers a native parallelization for the R interface. Low memory requirements are demonstrated on the MINE benchmarks as well as on large ( = 1340) microarray and Illumina GAII RNA-seq transcriptomics datasets.

AVAILABILITY AND IMPLEMENTATION

Source code and binaries are freely available for download under GPL3 licence at http://minepy.sourceforge.net for minepy and through the CRAN repository http://cran.r-project.org for the R package minerva. All software is multiplatform (MS Windows, Linux and OSX).