Genomic similarity and antibody-dependent enhancement of immune serum potentially affect the protective efficacy of commercial MLV vaccines against NADC30-like PRRSV.

Porcine reproductive and respiratory syndrome (PRRS) is one of the most significant diseases affecting the pig industry worldwide. The PRRSV mutation rate is the highest among the RNA viruses. To date, NADC30-like PRRSV and highly pathogenic PRRSV (HP-PRRSV) are the dominant epidemic strains in China; however, commercial vaccines do not always provide sufficient cross-protection, and the reasons for insufficient protection are unclear. This study isolated a wild-type NADC30-like PRRSV, SX-YL1806, from Shaanxi Province. Vaccination challenge experiments in piglets showed that commercial modified live virus (MLV) vaccines provided good protection against HP-PRRSV. However, it could not provide sufficient protection against the novel strain SX-YL1806. To explore the reasons for this phenomenon, we compared the genomic homology between the MLV strain and HP-PRRSV or NADC30-like PRRSV and found that the MLV strain had a lower genome similarity with NADC30-like PRRSV. Serum neutralization assay showed that MLV-immune serum slightly promoted the homologous HP-PRRSV replication and significantly promoted the heterologous NADC30-like PRRSV strain replication in vitro, suggesting that antibody-dependent enhancement (ADE) might also play a role in decreasing MLV protective efficacy. These findings expand our understanding of the potential factors affecting the protective effect of PRRSV MLV vaccines against the NADC30-like strains.

Genetic Improvement in Plant Architecture, Maturity Duration and Agronomic Traits of Three Traditional Rice Landraces through Gamma Ray-Based Induced Mutagenesis.

Mutation breeding offers a simple, fast and efficient way to rectify major defects without altering their original identity. The present study deployed radiation (gamma rays @ 300Gy)-induced mutation breeding for the improvement and revival of three traditional rice landraces, viz., Samundchini, Vishnubhog and Jhilli. Among the various putative mutants identified in the M2 generation, only three, ten and five rice mutants of Samundchini, Vishnubhog and Jhilli, respectively, were advanced to the M4, M5 and M6 generations, along with their parents and three checks for evaluations based on 13 agro-morphological and 16 grain quality traits. Interestingly, all the mutants of the three landraces showed a reduction in days to 50% flowering and plant height as compared to their parents in all the three generations. The reduction in days to 50% flowering ranges from 4.94% (Vishnubhog Mutant V-67) to 21.40% (Jhilli Mutant J-2-13), whereas the reduction in plant height varies from 11.28% (Vishnubhog Mutant V-45-2, Vishnubhog Mutant V-67) to 37.65% (Jhilli Mutant J-15-1). Furthermore, two, six and three mutants of Samundchini, Vishnubhog and Jhilli have increased their yield potential over their corresponding parents, respectively. Interestingly, Samundchini Mutant S-18-1 (22.45%), Vishnubhog Mutant V-74-6 (36.87%) and Jhilli Mutant J-13-5 (25.96%) showed the highest yield advantages over their parents. Further, a pooled analysis of variance based on a randomized complete block design revealed ample variations among the genotypes for the studied traits. In addition, all the traits consistently showed high to moderate PCV and GCV and a slight difference between them in all three generations indicated the negligible effect of the environment. Moreover, in the association analysis, the traits, viz., fertile spikelets/panicle, panicle length, total tillers/plant, spikelet fertility percent and 100-seed weight showed the usual grain yield/plant, whereas the traits hulling (%) and milling (%) with HRR (%) consistently showed high direct effects and significant positive correlations. The SSR marker-based genome similarity in rice mutants and corresponding parents ranged from 95.60% to 71.70% (Vishnubhog); 95.62% to 89.10% (Samundchini) and 95.62% to 80.40% (Jhilli), indicating the trueness of the mutants. Moreover, the UPGMA algorithm and Gower distance-based dendrogram, neighbour joining tree and PCA scatter diagram assured that mutants were grouped with their respective parents and fell into separate clusters showing high similarity between mutants and parents and dissimilarity among the 24 genotypes. Overall, the information and materials generated from the current study will be very useful and informative for students, researchers and plant breeders. Additionally, our results also showed that irradiation could generate a considerable amount of genetic variability and provide new avenues for crop improvement and diversification.

LINflow: a computational pipeline that combines an alignment-free with an alignment-based method to accelerate generation of similarity matrices for prokaryotic genomes.

BACKGROUND: Computing genomic similarity between strains is a prerequisite for genome-based prokaryotic classification and identification. Genomic similarity was first computed as Average Nucleotide Identity (ANI) values based on the alignment of genomic fragments. Since this is computationally expensive, faster and computationally cheaper alignment-free methods have been developed to estimate ANI. However, these methods do not reach the level of accuracy of alignment-based methods. METHODS: Here we introduce LINflow, a computational pipeline that infers pairwise genomic similarity in a set of genomes. LINflow takes advantage of the speed of the alignment-free sourmash tool to identify the genome in a dataset that is most similar to a query genome and the precision of the alignment-based pyani software to precisely compute ANI between the query genome and the most similar genome identified by sourmash. This is repeated for each new genome that is added to a dataset. The sequentially computed ANI values are stored as Life Identification Numbers (LINs), which are then used to infer all other pairwise ANI values in the set. We tested LINflow on four sets, 484 genomes in total, and compared the needed time and the generated similarity matrices with other tools. RESULTS: LINflow is up to 150 times faster than pyani and pairwise ANI values generated by LINflow are highly correlated with those computed by pyani. However, because LINflow infers most pairwise ANI values instead of computing them directly, ANI values occasionally depart from the ANI values computed by pyani. In conclusion, LINflow is a fast and memory-efficient pipeline to infer similarity among a large set of prokaryotic genomes. Its ability to quickly add new genome sequences to an already computed similarity matrix makes LINflow particularly useful for projects when new genome sequences need to be regularly added to an existing dataset.

No Evidence for Social Genetic Effects or Genetic Similarity Among Friends Beyond that Due to Population Stratification: A Reappraisal of Domingue et al (2018).

Using data from 5500 adolescents from the National Longitudinal Study of Adolescent to Adult Health, Domingue et al. (Proc Natl Acad Sci 25:256., 2018) claimed to show that friends are genetically more similar to one another than randomly selected peers, beyond the confounding effects of population stratification by ancestry. The authors also claimed to show 'social-genetic' effects, whereby individuals' educational attainment (EA) is influenced by their friends' genes. We argue that neither claim is justified by the data. Mathematically we show that (1) the genetic similarity reported between friends is far larger than theoretically possible if it was caused by phenotypic assortment as the authors claim; uncontrolled population stratification is a likely reason for the genetic similarity they observed, and (2) significant association between individuals' EA and their friends' polygenic scores for EA is a necessary consequence of EA similarity among friends, and does not provide evidence for social-genetic effects. Going forward, we urge caution in the analysis and interpretation of data at the intersection of human genetics and the social sciences.

Global genomic similarity and core genome sequence diversity of the Streptococcus genus as a toolkit to identify closely related bacterial species in complex environments.

BACKGROUND: The Streptococcus genus is relevant to both public health and food safety because of its ability to cause pathogenic infections. It is well-represented (>100 genomes) in publicly available databases. Streptococci are ubiquitous, with multiple sources of isolation, from human pathogens to dairy products. The Streptococcus genus has traditionally been classified by morphology, serum types, the 16S ribosomal RNA (rRNA) gene, and multi-locus sequence types subject to in-depth comparative genomic analysis. METHODS: Core and pan-genomes described the genomic diversity of 108 strains belonging to 16 Streptococcus species. The core genome nucleotide diversity was calculated and compared to phylogenomic distances within the genus Streptococcus. The core genome was also used as a resource to recruit metagenomic fragment reads from streptococci dominated environments. A conventional 16S rRNA gene phylogeny reconstruction was used as a reference to compare the resulting dendrograms of average nucleotide identity (ANI) and genome similarity score (GSS) dendrograms. RESULTS: The core genome, in this work, consists of 404 proteins that are shared by all 108 Streptococcus. The average identity of the pairwise compared core proteins decreases proportionally to GSS lower scores, across species. The GSS dendrogram recovers most of the clades in the 16S rRNA gene phylogeny while distinguishing between 16S polytomies (unresolved nodes). The GSS is a distance metric that can reflect evolutionary history comparing orthologous proteins. Additionally, GSS resulted in the most useful metric for genus and species comparisons, where ANI metrics failed due to false positives when comparing different species. DISCUSSION: Understanding of genomic variability and species relatedness is the goal of tools like GSS, which makes use of the maximum pairwise shared orthologous sequences for its calculation. It allows for long evolutionary distances (above species) to be included because of the use of amino acid alignment scores, rather than nucleotides, and normalizing by positive matches. Newly sequenced species and strains could be easily placed into GSS dendrograms to infer overall genomic relatedness. The GSS is not restricted to ubiquitous conservancy of gene features; thus, it reflects the mosaic-structure and dynamism of gene acquisition and loss in bacterial genomes.

An unsupervised machine learning method for discovering patient clusters based on genetic signatures.

INTRODUCTION: Many chronic disorders have genomic etiology, disease progression, clinical presentation, and response to treatment that vary on a patient-to-patient basis. Such variability creates a need to identify characteristics within patient populations that have clinically relevant predictive value in order to advance personalized medicine. Unsupervised machine learning methods are suitable to address this type of problem, in which no a priori class label information is available to guide this search. However, it is challenging for existing methods to identify cluster memberships that are not just a result of natural sampling variation. Moreover, most of the current methods require researchers to provide specific input parameters a priori. METHOD: This work presents an unsupervised machine learning method to cluster patients based on their genomic makeup without providing input parameters a priori. The method implements internal validity metrics to algorithmically identify the number of clusters, as well as statistical analyses to test for the significance of the results. Furthermore, the method takes advantage of the high degree of linkage disequilibrium between single nucleotide polymorphisms. Finally, a gene pathway analysis is performed to identify potential relationships between the clusters in the context of known biological knowledge. DATASETS AND RESULTS: The method is tested with a cluster validation and a genomic dataset previously used in the literature. Benchmark results indicate that the proposed method provides the greatest performance out of the methods tested. Furthermore, the method is implemented on a sample genome-wide study dataset of 191 multiple sclerosis patients. The results indicate that the method was able to identify genetically distinct patient clusters without the need to select parameters a priori. Additionally, variants identified as significantly different between clusters are shown to be enriched for protein-protein interactions, especially in immune processes and cell adhesion pathways, via Gene Ontology term analysis. CONCLUSION: Once links are drawn between clusters and clinically relevant outcomes, Immunochip data can be used to classify high-risk and newly diagnosed chronic disease patients into known clusters for predictive value. Further investigation can extend beyond pathway analysis to evaluate these clusters for clinical significance of genetically related characteristics such as age of onset, disease course, heritability, and response to treatment.

Computing the family-free DCJ similarity.

BACKGROUND: The genomic similarity is a large-scale measure for comparing two given genomes. In this work we study the (NP-hard) problem of computing the genomic similarity under the DCJ model in a setting that does not assume that the genes of the compared genomes are grouped into gene families. This problem is called family-free DCJ similarity. RESULTS: We propose an exact ILP algorithm to solve the family-free DCJ similarity problem, then we show its APX-hardness and present four combinatorial heuristics with computational experiments comparing their results to the ILP. CONCLUSIONS: We show that the family-free DCJ similarity can be computed in reasonable time, although for larger genomes it is necessary to resort to heuristics. This provides a basis for further studies on the applicability and model refinement of family-free whole genome similarity measures.

Comparative genomic analysis of novel bacteriophages infecting Vibrio parahaemolyticus isolated from western and southern coastal areas of Korea.

Vibrio parahaemolyticus, a foodborne pathogen, has become resistant to antibiotics. Therefore, alternative bio-control agents such bacteriophage are urgently needed for its control. Six novel bacteriophages specific to V. parahaemolyticus (vB_VpaP_KF1~2, vB_VpaS_KF3~6) were characterized at the molecular level in this study. Genomic similarity analysis revealed that these six bacteriophages could be divided into two groups with different genomic features, phylogenetic grouping, and morphologies. Two groups of bacteriophages had their own genes with different mechanisms for infection, assembly, and metabolism. Our results could be used as a future reference to study phage genomics or apply phages in future bio-control studies.

Utilization of information from gene networks towards a better understanding of functional similarities between complex traits: a dairy cattle model.

Our study focused on quantifying functional similarities between complex traits recorded in dairy cattle: milk yield, fat yield, protein yield, somatic cell score and stature. Similarities were calculated based on gene sets forming gene networks and on gene ontology term sets underlying genes estimated as significant for the analysed traits. Gene networks were obtained by the Bisogenet and Gene Set Linkage Analysis (GSLA) software. The highest similarity was observed between milk yield and fat yield. A very low degree of similarity was attributed to protein yield and stature when using gene sets as a similarity criterion, as well as to protein yield and fat yield when using sets of gene ontology terms. Pearson correlation coefficients between gene effect estimates, representing additive polygenic similarities, were highest for protein yield and milk yield, and the lowest in case of protein yield and somatic cell score. Using the 50 K Illumina SNP chip from the national genomic selection data set only the most significant gene-trait associations can be retrieved, while enhancing it by the functional information contained in interaction data stored in public data bases and by metabolic pathways information facilitates a better characterization of the functional background of the traits and furthermore - trait comparison. The most interesting result of our study was that the functional similarity observed between protein yield and milk-/fat yields contradicted moderate genetic correlations estimated earlier for the same population based on a multivariate mixed model. The discrepancy indicates that an infinitesimal model assumed in that study reflects an averaged correlation due to polygenes, but fails to reveal the functional background underlying the traits, which is due to the cumulative composition of many genes involved in metabolic pathways, which appears to differ between protein-fat yield and protein-milk yield pairs.

Transcriptome profiling reveals mosaic genomic origins of modern cultivated barley.

The domestication of cultivated barley has been used as a model system for studying the origins and early spread of agrarian culture. Our previous results indicated that the Tibetan Plateau and its vicinity is one of the centers of domestication of cultivated barley. Here we reveal multiple origins of domesticated barley using transcriptome profiling of cultivated and wild-barley genotypes. Approximately 48-Gb of clean transcript sequences in 12 Hordeum spontaneum and 9 Hordeum vulgare accessions were generated. We reported 12,530 de novo assembled transcripts in all of the 21 samples. Population structure analysis showed that Tibetan hulless barley (qingke) might have existed in the early stage of domestication. Based on the large number of unique genomic regions showing the similarity between cultivated and wild-barley groups, we propose that the genomic origin of modern cultivated barley is derived from wild-barley genotypes in the Fertile Crescent (mainly in chromosomes 1H, 2H, and 3H) and Tibet (mainly in chromosomes 4H, 5H, 6H, and 7H). This study indicates that the domestication of barley may have occurred over time in geographically distinct regions.