Genomic introgression mapping of field-derived multiple-anthelmintic resistance in Teladorsagia circumcincta

Preventive chemotherapy has long been practiced against nematode parasites of livestock, leading to widespread drug resistance, and is increasingly being adopted for eradication of human parasitic nematodes even though it is similarly likely to lead to drug resistance. Given that the genetic architecture of resistance is poorly understood for any nematode, we have analyzed multidrug resistant Teladorsagia circumcincta, a major parasite of sheep, as a model for analysis of resistance selection. We introgressed a field-derived multiresistant genotype into a partially inbred susceptible genetic background (through repeated backcrossing and drug selection) and performed genome-wide scans in the backcross progeny and drug-selected F2 populations to identify the major genes responsible for the multidrug resistance. We identified variation linking candidate resistance genes to each drug class. Putative mechanisms included target site polymorphism, changes in likely regulatory regions and copy number variation in efflux transporters. This work elucidates the genetic architecture of multiple anthelmintic resistance in a parasitic nematode for the first time and establishes a framework for future studies of anthelmintic resistance in nematode parasites of humans

A Genomic Investigation of Divergence Between Tuna Species

Effective management and conservation of marine pelagic fishes is heavily dependent on a robust understanding of their population structure, their evolutionary history, and the delineation of appropriate management units. The Yellowfin tuna (Thunnus albacares) and the Blackfin tuna (Thunnus atlanticus) are two exploited epipelagic marine species with overlapping ranges in the tropical and sub-tropical Atlantic Ocean. This work analyzed genome-wide genetic variation of both species in the Atlantic basin to investigate the occurrence of population subdivision and adaptive variation. A de novo assembly of the Blackfin tuna genome was generated using Illumina paired-end sequencing data and applied as a reference for population genomic analysis of specimens from 9 localities spanning most of the Blackfin tuna range. Analysis suggested the presence of four weakly differentiated units corresponding to the northwestern Atlantic Ocean, Gulf of Mexico, Caribbean Sea, and southwestern Atlantic Ocean, respectively. Significant spatial autocorrelation of genotypes was observed for specimens collected within 800 km of each other. A high-quality genome assembly generated for the Yellowfin tuna using PacBio and Illumina sequences was scaffolded by a linkage map developed through analysis of the segregation of genome wide Single Nucleotide Polymorphisms in 164 larvae offspring from a single pair produced by controlled breeding. The genome assembly was used as a reference for population genomic analysis of juvenile specimens from the 4 main nursery areas hypothesized in the Atlantic Ocean basin. Analyses corroborated previously reported population subdivision between the east and west Atlantic Ocean, but also suggested subdivision associated with individual nursery areas within the east and west regions. Draft reference assemblies were generated for Albacore, Bigeye and Longtail tunas and used in combination with the Yellowfin and Blackfin tuna genomes obtained in this work and existing assemblies for bluefin tunas in preliminary analyses of genome wide variation between species of the Thunnus genus. Whole-genome derived SNP-based phylogenetic analysis of the Thunnus genus suggests phylogenetic relationships may be more complex than suggested in earlier work based on Restriction-site Associated DNA sequencing or muscle transcriptome sequencing and prompt for further analysis of the genus using a more comprehensive sampling of taxa in each oceanic basin

The Molecular Signature for Local Adaptation in the Seagrass <i>Posidonia oceanica</i>

In the last century, seagrass ecosystems have suffered a worldwide decline ascribed to multiple environmental stressors, among which the reduction of light available for the photosynthesis and the increase in temperature represent the strongest constraints for their growth and survival. Despite conservation, this decline is at present still continuing. In order to understand the genetic adaptive response to light and temperature in the seagrass Posidonia oceanica, two different strategies have been pursued: a genome scan approach along a latitudinal and a bathymetric gradient and a differential gene expression analysis along the bathymetric gradient, where light and temperature were the two main selective factors. For the genome scan approach 6 populations (Delimara - Malta, Lacco Ameno - Island of Ischia, Italy, Marettimo Island- Italy, Meloria - Italy, Piombino - Italy and Stareso - Corsica, France) were sampled along the bathymetric gradient at two different depths (-5m and -25m). The same populations were used for the latitudinal gradient analysis by grouping them on the basis of their geographic location (Southern group: Delimara, Lacco Ameno and Marettimo; Northern group: Meloria, Piombino and Stareso). No genes under selection were identified in the genome scan along the bathymetric gradient. Three putative genes under selection were identified in the genome scan along the latitudinal gradient and were involved in the photosynthesis and in the translation process. For assessing differential gene expression, a transcriptome sequencing of plants sampled at two different depths and different times of the day in the Stareso meadow was performed by RNAseq technology. The analysis highlighted the capability of plants living in shallow waters to cope with environmental stresses imposed by high light and high temperature. Transcriptome data generated from this study increased the resources available in P. oceanica and will be very useful for further investigations of the adaptation of in this plant

Analyses of the genomic variation to study cork oak evolution and adaptation : from past to future climatic changes

Tese de doutoramento, Biologia e Ecologia das Alterações Globais (Biologia do Genoma e Evolução), Universidade de Lisboa, Faculdade de Ciências, 2018Current scientific literature indicates that climate change will cause an average world temperature increase between 1 and 4ºC, along with changes in precipitation patterns and extreme weather events in the next 50 years. These are likely to have a negative impact for biodiversity in general, and forest ecosystems should be particularly affected, especially those in Mediterranean areas, like the cork oak (Quercus suber L.) “montados”. In order to understand how species can respond to such alterations, it is important to know their evolutionary history and genetic architecture of adaptive traits. Advances in sequencing technologies have relatively recently brought down the cost of sequencing per base pair to a point where even small research facilities can obtain genomic information of non-model organisms. These advances made SNP markers become the most abundant type of genetic variation in eukaryotic genomes, especially with the advent of Reduced Representation libraries such as RAD-Seq and GBS. Yet, despite their widespread use, SNP data analyses still bore its own set of bioinformatics challenges. While most of these are related with the practical aspects of the process, such as being able to handle very large datasets, or discriminate between neutral and non-neutral markers, some fundamental problems, like reproducibility are also important issues affecting research in this area. In this thesis, genomic and transcriptomic data from Q. suber was used to assess the evolutionary history of the species, detect the effects of natural selection across the cork oak’s distribution range and find any associations between the obtained markers and environmental variables. The main methodological contributions of this thesis are in the form of three software suites: (1) 4Pipe4, a software for automatically mining SNP markers from NGS data when no reference genome nor strain information is present, (2) NCBI Mass Sequence Downloader, a program to automate the downloading of large datasets from the NCBI databases, and (3) Structure_threader, a software to automate and parallelize analyses using several popular clustering analyses programs. All of these programs were developed with the intent to improve the automation and reproducibility value of the analysis processes they are meant to be part of. The main findings of this thesis are that (1) the evolutionary history and population structure of Q. suber is not as neatly structured as chloroplastidial markers indicate, (2) local adaptation plays and important role in the distribution of the species’ genetic variability, and (3) the cork oak may be better equipped, from a genetic point of view, to adapt to climate change than what previous studies based solely on ecological modelling indicated

Seascape genomics and phylogeography of the sailfish (Istiophorus platypterus)

Permeable phylogeographic barriers characterize the vast open ocean, boosting gene flow and counteracting population differentiation and speciation of widely distributed and migratory species. However, many widely distributed species consists of distinct populations throughout their distribution, evidencing that our understanding of how the marine environment triggers population and species divergence are insufficient. The sailfish is a circumtropical and highly migratory billfish that inhabits warm and productive areas. Despite its ecological and socioeconomic importance as a predator and fishery resource, the species is threatened by overfishing, requiring innovative approaches to improve their management and conservation status. Thus, we presented a novel high-quality reference genome for the species and applied a seascape genomics approach to understand how marine environmental features may promote local adaptation and how it affects gene flow between populations. We delimit two populations between the Atlantic and Indo-Western Pacific oceans and detect outlier loci correlated with sea surface temperature, salinity, oxygen, and chlorophyll concentrations. However, the most significant explanatory factor that explains the differences between populations was isolation by distance. Despite recent population drops, the sailfish populations are not inbred. For billfishes in general, genome-wide heterozygosity was found to be relatively low compared to other marine fishes, evidencing the need to counteract overfishing effects. In addition, in a climate change scenario, management agencies must implement state-of-the-art sequencing methods, consider our findings in their management plans, and monitor genome-wide heterozygosity over time to improve sustainable fisheries and the long-term viability of its populations.LA/P/0101/2020info:eu-repo/semantics/publishedVersio

Small data: practical modeling issues in human-model -omic data

This thesis is based on the following articles: Chapter 2: Holsbø, E., Perduca, V., Bongo, L.A., Lund, E. & Birmelé, E. (Manuscript). Stratified time-course gene preselection shows a pre-diagnostic transcriptomic signal for metastasis in blood cells: a proof of concept from the NOWAC study. Available at https://doi.org/10.1101/141325. Chapter 3: Bøvelstad, H.M., Holsbø, E., Bongo, L.A. & Lund, E. (Manuscript). A Standard Operating Procedure For Outlier Removal In Large-Sample Epidemiological Transcriptomics Datasets. Available at https://doi.org/10.1101/144519. Chapter 4: Holsbø, E. & Perduca, V. (2018). Shrinkage estimation of rate statistics. Case Studies in Business, Industry and Government Statistics 7(1), 14-25. Also available at http://hdl.handle.net/10037/14678.Human-model data are very valuable and important in biomedical research. Ethical and economical constraints limit the access to such data, and consequently these datasets rarely comprise more than a few hundred observations. As measurements are comparatively cheap, the tendency is to measure as many things as possible for the few, valuable participants in a study. With -omics technologies it is cheap and simple to make hundreds of thousands of measurements simultaneously. This few observations–many measurements setting is a high-dimensional problem in the technical language. Most gene expression experiments measure the expression levels of 10 000–15 000 genes for fewer than 100 subjects. I refer to this as the small data setting. This dissertation is an exercise in practical data analysis as it happens in a large epidemiological cohort study. It comprises three main projects: (i) predictive modeling of breast cancer metastasis from whole-blood transcriptomics measurements; (ii) standardizing a microarray data quality assessment in the Norwegian Women and Cancer (NOWAC) postgenome cohort; and (iii) shrinkage estimation of rates. These three are all small data analyses for various reasons. Predictive modeling in the small data setting is very challenging. There are several modern methods built to tackle high-dimensional data, but there is a need to evaluate these methods against one another when analyzing data in practice. Through the metastasis prediction work we learned first-hand that common practices in machine learning can be inefficient or harmful, especially for small data. I will outline some of the more important issues. In a large project such as NOWAC there is a need to centralize and disseminate knowledge and procedures. The standardization of NOWAC quality assessment was a project born of necessity. The standard operating procedure for outlier removal was developed so that preprocessing of the NOWAC microarray material should happen in the same way every time. We take this procedure from an archaic R-script that resided in peoples email inboxes to a well-documented, open-source R-package and present the NOWAC guidelines for microarray quality control. The procedure is built around the inherent high value of a singleobservation. Small data are plagued by high variance. Working with small data it is usually profitable to bias models by shrinkage or borrowing of information from elsewhere. We present a pseudo-Bayesian estimator of rates in an informal crime rate study. We exhibit the value of such procedures in a small data setting and demonstrate some novel considerations about the coverage properties of such a procedure. In short I gather some common practices in predictive modeling as applied to small data and assess their practical implications. I argue that with more focus on human-based datasets in biomedicine there is a need for particular consideration of these data in a small data paradigm to allow for reliable analysis. I will present what I believe to be sensible guidelines

Functional characterization and annotation of trait-associated genomic regions by transcriptome analysis

In this work, two novel implementations have been presented, which could assist in the design and data analysis of high-throughput genomic experiments. An efficient and flexible tiling probe selection pipeline utilizing the penalized uniqueness score has been implemented, which could be employed in the design of various types and scales of genome tiling task. A novel hidden semi-Markov model (HSMM) implementation is made available within the Bioconductor project, which provides a unified interface for segmenting genomic data in a wide range of research subjects.In dieser Arbeit werden zwei neuartige Implementierungen präsentiert, die im Design und in der Datenanalyse von genomischen Hochdurchsatz-Experiment hilfreich sein könnten. Die erste Implementierung bildet eine effiziente und flexible Auswahl-Pipeline für Tiling-Proben, basierend auf einem Eindeutigkeitsmaß mit einer Maluswertung. Als zweite Implementierung wurde ein neuartiges Hidden-Semi-Markov-Modell (HSMM) im Bioconductor Projekt verfügbar gemacht