Investigating the genetic components of tuber bruising in a breeding population of tetraploid potatoes.

BACKGROUND: Tuber bruising in tetraploid potatoes (Solanum tuberosum) is a trait of economic importance, as it affects tubers' fitness for sale. Understanding the genetic components affecting tuber bruising is a key step in developing potato lines with increased resistance to bruising. As the tetraploid setting renders genetic analyses more complex, there is still much to learn about this complex phenotype. Here, we used capture sequencing data on a panel of half-sibling populations from a breeding programme to perform a genome-wide association analysis (GWAS) for tuber bruising. In addition, we collected transcriptomic data to enrich the GWAS results. However, there is currently no satisfactory method to represent both GWAS and transcriptomics analysis results in a single visualisation and to compare them with existing knowledge about the biological system under study. RESULTS: When investigating population structure, we found that the STRUCTURE algorithm yielded greater insights than discriminant analysis of principal components (DAPC). Importantly, we found that markers with the highest (though non-significant) association scores were consistent with previous findings on tuber bruising. In addition, new genomic regions were found to be associated with tuber bruising. The GWAS results were backed by the transcriptomics differential expression analysis. The differential expression notably highlighted for the first time the role of two genes involved in cellular strength and mechanical force sensing in tuber resistance to bruising. We proposed a new visualisation, the HIDECAN plot, to integrate the results from the genomics and transcriptomics analyses, along with previous knowledge about genomic regions and candidate genes associated with the trait. CONCLUSION: This study offers a unique genome-wide exploration of the genetic components of tuber bruising. The role of genetic components affecting cellular strength and resistance to physical force, as well as mechanosensing mechanisms, was highlighted for the first time in the context of tuber bruising. We showcase the usefulness of genomic data from breeding programmes in identifying genomic regions whose association with the trait of interest merit further investigation. We demonstrate how confidence in these discoveries and their biological relevance can be increased by integrating results from transcriptomics analyses. The newly proposed visualisation provides a clear framework to summarise of both genomics and transcriptomics analyses, and places them in the context of previous knowledge on the trait of interest.

Yersinia enterocolitica in wild and peridomestic rodents within Great Britain, a prevalence study.

Yersinia enterocolitica is a human pathogen transmitted via the faecal-oral route among animals and humans and is a major foodborne public health hazard. This study explores the role of Y. enterocolitica transmission at the livestock-wildlife interface and investigates the potential role wild and peridomestic rodents play as a source of this zoonotic pathogen. The total of 342 faecal samples collected from the seven rodent species and one insectivore was examined using an optimized protocol to culture and identify Y. enterocolitica. Positive samples were also bioserotyped for grouping and determination of sample pathogenicity. Wildlife species sampled in this study were separated into two sample groups: randomly sampled (brown rats, house mice, wood mice, bank voles, field voles and the common shrew), as well as targeted sampling (red and grey squirrels). The overall prevalence of Y. enterocolitica in the randomly sampled population was 3.73%. Brown rats were chosen as sentinel species and tested to determine if location (pig farm vs non-pig farm) was a significant factor affecting Y. enterocolitica prevalence. In this study, location was not significant. All positive samples were found to be of biotype 1A, deemed non-pathogenic. Three of the samples were serotype 09, six were serotype 27 and five had an unidentifiable serotype. This study represents the first time Y. enterocolitica has been identified in these species of wildlife within mainland Britain. In addition, this study's findings are entirely novel and overall with regard to field voles and common shrews. However, the role of wild and peridomestic rodents in the transmission of pathogenic Y. enterocolitica remains unknown, as this study was unable to detect the presence of pathogenic Y. enterocolitica strains in these species.

Diverse Epidemiology of Leptospira serovars Notified in New Zealand, 1999-2017.

Leptospirosis in New Zealand has been strongly associated with animal-contact occupations and with serovars Hardjo and Pomona. However, recent data suggest changes in these patterns, hence, serovar-specific epidemiology of leptospirosis from 1999 to 2017 was investigated. The 19-year average annual incidence is 2.01/100,000. Early (1999-2007) and late (2008-2017) study period comparisons showed a significant increase in notifications with serovar Ballum (IRR: 1.59, 95% CI: 1.22-2.09) in all cases and serovar Tarassovi (IRR: 1.75, 95% CI: 1.13-2.78) in Europeans and a decrease in notifications with serovars Hardjo and Pomona in all cases. Incidences of Ballum peaked in winter, Hardjo peaked in spring and Tarassovi peaked in summer. Incidence was highest in Maori (2.24/100,000) with dominant serovars being Hardjo and Pomona. Stratification by occupation showed meat workers had the highest incidence of Hardjo (57.29/100,000) and Pomona (45.32/100,000), farmers had the highest incidence of Ballum (11.09/100,000) and dairy farmers had the highest incidence of Tarassovi (12.59/100,000). Spatial analysis showed predominance of Hardjo and Pomona in Hawke's Bay, Ballum in West Coast and Northland and Tarassovi in Waikato, Taranaki and Northland. This study highlights the serovar-specific heterogeneity of human leptospirosis in New Zealand that should be considered when developing control and prevention strategies.

sismonr: simulation of in silico multi-omic networks with adjustable ploidy and post-transcriptional regulation in R.

SUMMARY: We present sismonr, an R package for an integral generation and simulation of in silico biological systems. The package generates gene regulatory networks, which include protein-coding and non-coding genes along with different transcriptional and post-transcriptional regulations. The effect of genetic mutations on the system behaviour is accounted for via the simulation of genetically different in silico individuals. The ploidy of the system is not restricted to the usual haploid or diploid situations but can be defined by the user to higher ploidies. A choice of stochastic simulation algorithms allows us to simulate the expression profiles of the genes in the in silico system. We illustrate the use of sismonr by simulating the anthocyanin biosynthesis regulation pathway for three genetically distinct in silico plants. AVAILABILITY AND IMPLEMENTATION: The sismonr package is implemented in R and Julia and is publicly available on the CRAN repository (https://CRAN.R-project.org/package=sismonr). A detailed tutorial is available from GitHub at https://oliviaab.github.io/sismonr/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene Regulatory Networks: A Primer in Biological Processes and Statistical Modelling.

Modelling gene regulatory networks requires not only a thorough understanding of the biological system depicted, but also the ability to accurately represent this system from a mathematical perspective. Throughout this chapter, we aim to familiarize the reader with the biological processes and molecular factors at play in the process of gene expression regulation. We first describe the different interactions controlling each step of the expression process, from transcription to mRNA and protein decay. In the second section, we provide statistical tools to accurately represent this biological complexity in the form of mathematical models. Among other considerations, we discuss the topological properties of biological networks, the application of deterministic and stochastic frameworks, and the quantitative modelling of regulation. We particularly focus on the use of such models for the simulation of expression data that can serve as a benchmark for the testing of network inference algorithms.