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bioRxiv2016

Discovery of Cancer Driver Long Noncoding RNAs across 1112 Tumour Genomes: New Candidates and Distinguishing Features.

Long noncoding RNAs (lncRNAs) represent a vast unexplored genetic space that may hold missing drivers of tumourigenesis, but few such \"driver lncRNAs\" are known. Until now, they have been discovered through changes in expression, leading to problems in distinguishing between causative roles and passenger effects. We here present a different approach for driver lncRNA discovery using mutational patterns in tumour DNA. Our pipeline, ExInAtor, identifies genes with excess load of somatic single nucleotide variants (SNVs) across panels of tumour genomes. Heterogeneity in mutational signatures between cancer types and individuals is accounted for using a simple local trinucleotide background model, which yields high precision and low computational demands. We use ExInAtor to predict drivers from the GENCODE annotation across 1112 entire genomes from 23 cancer types. Using a stratified approach, we identify 15 high-confidence candidates: 9 novel and 6 known cancer-related genes, including MALAT1, NEAT1 and SAMMSON. Both known and novel driver lncRNAs are distinguished by elevated gene length, evolutionary conservation and expression. We have presented a first catalogue of mutated lncRNA genes driving cancer, which will grow and improve with the application of ExInAtor to future tumour genome projects.

Cancer Biology↗

bioRxiv2016

The druggable genome and support for target identification and validation in drug development

Target identification (identifying the correct drug targets for each disease) and target validation (demonstrating the effect of target perturbation on disease biomarkers and disease end-points) are essential steps in drug development. We showed previously that biomarker and disease endpoint associations of single nucleotide polymorphisms (SNPs) in a gene encoding a drug target accurately depict the effect of modifying the same target with a pharmacological agent; others have shown that genomic support for a target is associated with a higher rate of drug development success. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome wide association studies (GWAS) to an updated set of genes encoding druggable human proteins, to compounds with bioactivity against these targets and, where these were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, to enable druggable genome-wide association studies for drug target selection and validation in human disease.

Genetics↗

bioRxiv2016

Genome-wide methylation data mirror ancestry information

Genetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data. We demonstrate, using three large cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data, and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data. EPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io.

Genetics↗

bioRxiv2016

Recombination-driven genome evolution and stability of bacterial species

While bacteria divide clonally, horizontal gene transfer followed by homologous recombination is now recognized as an important and sometimes even dominant contributor to their evolution. However, the details of how the competition between clonal inheritance and recombination shapes genome diversity, population structure, and species stability remains poorly understood. Using a computational model, we find two principal regimes in bacterial evolution and identify two composite parameters that dictate the evolutionary fate of bacterial species. In the divergent regime, characterized by either a low recombination frequency or strict barriers to recombination, cohesion due to recombination is not sufficient to overcome the mutational drift. As a consequence, the divergence between any pair of genomes in the population steadily increases in the course of their evolution. The species as a whole lacks genetic coherence with sexually isolated clonal sub-populations continuously formed and dissolved. In contrast, in the metastable regime, characterized by a high recombination frequency combined with low barriers to recombination, genomes continuously recombine with the rest of the population. The population remains genetically cohesive and stable over time. The transition between these two regimes can be affected by relatively small changes in evolutionary parameters. Using the Multi Locus Sequence Typing (MLST) data we classify a number of well-studied bacterial species to be either the divergent or the metastable type. Generalizations of our framework to include fitness and selection, ecologically structured populations, and horizontal gene transfer of non-homologous regions are discussed.

Evolutionary Biology↗

bioRxiv2016

Exploring evolutionary relationships across the genome using topology weighting

We introduce the concept of topology weighting, a method for quantifying relationships between taxa that are not necessarily monophyletic, and visualising how these relationships change across the genome. A given set of taxa can be related in a limited number of ways, but if each taxon is represented by multiple sequences, the number of possible topologies becomes very large. Topology weighting reduces this complexity by quantifying the contribution of each 'taxon topology' to the full tree. We describe our method for topology weighting by iterative sampling of sub-trees (Twisst), and test it on both simulated and real genomic data. Overall, we show that this is an informative and versatile approach, suitable for exploring relationships in almost any genomic dataset.\n\nScripts to implement the method described are available at github.com/simonhmartin/twisst.

Evolutionary Biology↗

bioRxiv2016

The evolution of the natural killer complex; a comparison between mammals using new high-quality genome assemblies and targeted annotation

Natural killer (NK) cells are a diverse population of lymphocytes with a range of biological roles including essential immune functions. NK cell diversity is created by the differential expression of cell surface receptors which modulate activation and function, including multiple subfamilies of C-type lectin receptors encoded within the NK gene complex (NKC). Little is known about the gene content of the NKC beyond rodent and primate lineages, other than it appears to be extremely variable between mammalian groups. We compared the NKC structure between mammalian species using new high quality draft genome assemblies for cattle and goat, re-annotated sheep, pig and horse genome assemblies and the published human, rat and mouse lemur NKC. The major NKC genes are largely in syntenic positions in all eight species, with significant independent expansions and deletions between species, allowing us to present a model for NKC evolution during mammalian radiation. The ruminant species, cattle and goats, have independently evolved a second KLRC locus flanked by KLRA and KLRJ and a novel KLRH-like gene has acquired an activating tail. This novel gene has duplicated several times within cattle, while other activating receptor genes have been selectively disrupted. Targeted genome enrichment in cattle identified varying levels of allelic polymorphism between these NKC genes concentrated in the predicted extracellular ligand binding domains. This novel recombination and allelic polymorphism is consistent with NKC evolution under balancing selection, suggesting this diversity influences individual immune responses and may impact on differential outcomes of pathogen infection and vaccination.

Genetics↗

bioRxiv2016

The impact of chromatin dynamics on Cas9-mediated genome editing in human cells

In order to efficiently edit eukaryotic genomes, it is critical to test the impact of chromatin dynamics on CRISPR/Cas9 function and develop strategies to adapt the system to eukaryotic contexts. So far, research has extensively characterized the relationship between the CRISPR endonuclease Cas9 and the composition of the RNADNA duplex that mediates the systems precision. Evidence suggests that chromatin modifications and DNA packaging can block eukaryotic genome editing by custom-built DNA endonucleases like Cas9; however, the underlying mechanism of Cas9 inhibition is unclear. Here, we demonstrate that closed, gene-silencing-associated chromatin is a mechanism for the interference of Cas9-mediated DNA editing. Our assays use a transgenic cell line with a drug-inducible switch to control chromatin states (open and closed) at a single genomic locus. We show that closed chromatin inhibits editing at specific target sites, and that artificial reversal of the silenced state restores editing efficiency. These results provide new insights to improve Cas9-mediated editing in human and other mammalian cells.\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=95 SRC=\"FIGDIR/small/071464_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (26K):\norg.highwire.dtl.DTLVardef@1f2c52org.highwire.dtl.DTLVardef@96df75org.highwire.dtl.DTLVardef@1288f24org.highwire.dtl.DTLVardef@1cdaa02_HPS_FORMAT_FIGEXP M_FIG C_FIG

Synthetic Biology↗

bioRxiv2016

Genomic signature of kin selection in an ant with obligately sterile workers

Kin selection is thought to drive the evolution of cooperation and conflict, but the specific genes and genome-wide patterns shaped by kin selection are unknown. We identified thousands of genes associated with the sterile ant worker caste, the archetype of an altruistic phenotype shaped by kin selection, and then used population and comparative genomic approaches to study patterns of molecular evolution at these genes. Consistent with population genetic theoretical predictions, worker-upregulated genes showed relaxed adaptive evolution compared to genes upregulated in reproductive castes. Worker-upregulated genes included more taxonomically-restricted genes, indicating that the worker caste has recruited more novel genes, yet these genes also showed relaxed selection. Our study identifies a putative genomic signature of kin selection and helps to integrate emerging sociogenomic data with longstanding social evolution theory.

Evolutionary Biology↗

bioRxiv2016

Resurrected protein interaction networks reveal the strong rewiring that leads to network organisation after whole genome duplication

The evolution of plants is characterized by several rounds of ancient whole genome duplication, sometimes closely associated with the origin of large groups of species. A good example is the {gamma} triplication at the origin of core eudicots. Core eudicots comprise about 75% of flowering plants and are characterized by the canalization of reproductive development. To better understand the impact of this genomic event, we studied the protein interaction network of MADS-domain transcription factors, which are key regulators of reproductive development. We accurately inferred, resurrected and tested the interactions of ancestral proteins before and after the triplication and directly compared these ancestral networks to the networks of Arabidopsis and tomato. We find that the {gamma} triplication generated a dramatically innovated network that strongly rewired through the addition of many new interactions. Many of these interactions were established between paralogous proteins and a new interaction partner, establishing new redundancy. Simulations show that both node and edge addition through the triplication were important to maintain modularity in the network. In addition to generating insights into the impact of whole genome duplication and elementary processes involved in network evolution, our data provide a resource for comparative developmental biology in flowering plants.

Evolutionary Biology↗

bioRxiv2016

PCR artifact in testing for homologous recombination in genomic editing in zebrafish

We report a PCR-induced artifact in testing for homologous recombination in zebrafish. We attempted to replace the lnx2a gene with a donor cassette, mediated by a TALEN induced double stranded cut. The donor construct was flanked with homology arms of about 1 kb at the 5 and 3 ends. Injected embryos (G0) were raised and outcrossed to wild type fish. A fraction of the progeny appeared to have undergone the desired homologous recombination, as tested by PCR using primer pairs extending from genomic DNA outside the homology region to a site within the donor cassette. However, Southern blots revealed that no recombination had taken place. We conclude that recombination happened during PCR in vitro between the donor integrated elsewhere in the genome and the lnx2a locus, as suggested by earlier work [1]. We conclude that PCR alone may be insufficient to verify homologous recombination in genome editing experiments in zebrafish.

Developmental Biology↗

bioRxiv2016

Variant Set Enrichment: An R package to Identify Dis-ease-Associated Functional Genomic Regions

SummaryGenetic predispositions to diseases populate the noncoding regions of the human genome. Delineating their functional basis can inform on the mechanisms contributing to disease development. However, this remains a challenge due to the poor characterization of the noncoding genome. Variant Set Enrichment (VSE) is a fast method to calculate the enrichment of a set of disease-associated variants across functionally annotated genomic regions, consequently highlighting the mechanisms important in the etiology of the disease studied.\n\nAvailability and ImplementationVSE is implemented as an R package and can easily be implemented in any system with R. See supplementary information for details.\n\nContacthansenhe@uhnresearch.ca; mlupien@uhnresearch.ca

Bioinformatics↗

bioRxiv2016

Quality control analysis of the 1000 Genomes Project Omni2.5 genotypes

CitationFor any use of the 1000 Genomes Project data, please use the citation as noted here: http://www.1000genomes.org/faq/how-do-i-cite-1000-genomes-project. To cite this report or the lists described here, please use the following:\n\nRoslin NM, Li W, Paterson AD, Strug LJ. Quality control analysis of the 1000 Genomes Project Omni2.5 genotypes (Abstract/Program #576/F). Presented at the 66th Annual Meeting of The American Society of Human Genetics, October 18-22, 2016, Vancouver, Canada.\n\nData SummaryChips: IlluminaHumanOmni2.5-4v1_B and Illumina HumanOmni25M-8v1-1_B\n\nInitial number of SNPs: 2 458 861\n\nInitial number of samples: 2318\n\nNumber of SNPs passing QC: 1 989 184 (80.9%)\n\nNumber of samples passing QC: 2318 (100%)\n\nNumber of quasi-unrelated samples with consistent ethnicity and well inferred sex: 1736\n\nAbstractThe 1000 Genomes Project genotype 2318 individuals (48.1% male) from 19 populations in 5 continental groups on the Illumina Omni2.5 platform. The data are publicly available, and will prove a valuable resource to obtain ethnic-specific allele frequencies, as well as exploring population histories through principal components analysis (PCA), estimation of inbreeding coefficients, and admixture analysis. As in any study, the data should be cleaned prior to analysis, to remove individuals or markers of questionable quality. Furthermore, a thorough understanding of the relationships between individuals must be established. Here we report our findings after comprehensive examination of the data for quality control.\n\nThe basic quality of the genotypes was assessed using standard procedures. KING version 1.4 was used to confirm the relationships in the provided pedigrees, and also to detect undeclared relationships. PCA was used to examine the similarities and differences between individuals among and between population groups.\n\nIn general, the data was found to be of high quality. No samples were removed due to low call rate (<97%) or excess heterozygosity. Sex chromosome genotypes showed two individuals with discrepancies between reported and inferred sex, and were unable to determine sex in an additional 20 individuals; the sex for these was changed to unknown. Relationship checking found discrepancies between first-degree relationships in the provided pedigrees and the genotypes in 9 families, including one instance where a reported parent/child pair was unrelated, two instances where full sibs were unrelated, and one set of three individuals who formed a newly defined trio. A set of 1756 individuals who were inferred to be more distant than 3rd degree relatives was extracted and used in PCA. These individuals clustered in a pattern that is consistent with other published reports of global populations. We identified 4 individuals whose genotypes clustered more closely with a different geographic region than the one in the provided data.\n\nAlthough the genotype data is of high quality, errors exist in the publicly available dataset that require attention prior to using the genotypes. PLINK-format files including SNPs with good quality metrics and revised pedigree structures is available at http://tcag.ca. Files with distantly related or unrelated individuals, with sex inference consistent with provided gender, and with PCA consistent with continental group are also available.

Genetics↗

bioRxiv2016

Assessing Pathogens for Natural versus Laboratory Origins Using Genomic Data and Machine Learning

Pathogen genomic data is increasingly important in investigations of infectious disease outbreaks. The objective of this study is to develop methods for using large-scale genomic data to determine the type of the environment an outbreak pathogen came from. Specifically, this study focuses on assessing whether an outbreak strain came from a natural environment or experienced substantial laboratory culturing. The approach uses phylogenetic analyses and machine learning to identify DNA changes that are characteristic of laboratory culturing. The analysis methods include parallelized sequence read alignment, variant identification, phylogenetic tree construction, ancestral state reconstruction, semi-supervised classification, and random forests. These methods were applied to 902 Salmonella enterica serovar Typhimurium genomes from the NCBI Sequence Read Archive database. The analyses identified candidate signatures of laboratory culturing that are highly consistent with genes identified in published laboratory passage studies. In particular, the analysis identified mutations in rpoS, hfq, rfb genes, acrB, and rbsR as strong signatures of laboratory culturing. In leave-one-out cross-validation, the classifier had an area under the receiver operating characteristic (ROC) curve of 0.89 for strains from two laboratory reference sets collected in the 1940s and 1980s. The classifier was also used to assess laboratory culturing in foodborne and laboratory acquired outbreak strains closely related to laboratory reference strain serovar Typhimurium 14028. The classifier detected some evidence of laboratory culturing on the phylogeny branch leading to this clade, suggesting all of these strains may have a common ancestor that experienced laboratory culturing. Together, these results suggest that phylogenetic analysis and machine learning could be used to assess whether pathogens collected from patients are naturally occurring or have been extensively cultured in laboratories. The data analysis methods can be applied to any bacterial pathogen species, and could be adapted to assess viral pathogens and other types of source environments.

Bioinformatics↗

bioRxiv2016

Genome-level parameters describe the pan-nuclear fractal nature of eukaryotic interphase chromosomal arrangement

Long-range inter-chromosomal interactions in the interphase nucleus subsume critical genome-level regulatory functions such as transcription and gene expression. To decipher the physical basis of diverse pan-nuclear patterns of chromosomal arrangement that facilitates these processes, we investigate the scaling effects within disparate genomes and compared their total number of genes with chromosome size. First, we derived the pan-nuclear average fractal dimension of inter-chromosomal arrangement in interphase nuclei of different species and corroborated our predictions with independently reported results. Then, we described the different patterns across disparate unicellular and multicellular eukaryotes. We report that, unicellular lower eukaryotes have inter-chromosomal fractal dimension = 1 at the pan-nuclear scales, which is analogous to the multi-polymer crumpled globule model. Multi-fractal dimensions, corresponding to different inter-chromosomal arrangements emerged from multicellular eukaryotes, such that closely related species have relatively similar patterns. Using this theoretical approach, we could distinguish fractal patterns from human acrocentric versus metacentric chromosomes, implying that the multi-fractal nature of inter-chromosomal geometry facilitates viable large-scale chromosomal aberrations, such as Robertsonian translocations. We report that the nature of such an average multi-fractal dimension for nocturnal mammals is very different in diurnal mammals, which suggests a greatly enhanced plasticity in arrangement across different cell types, for example retinal versus dermal fibroblasts. Altogether, our results substantiate that genome-level constraints have also co-evolved with the average pan-nuclear fractal dimension of inter-chromosomal folding during eukaryotic evolution.

evolutionary biology↗

bioRxiv2016

An Optimized Approach for Annotation of Large Eukaryotic Genomic Sequences using Genetic Algorithm

Detection of important functional and/or structural elements and identifying their positions in a large eukaryotic genome is an active research area. Gene is an important functional and structural unit of DNA. The computation of gene prediction is essential for detailed genome annotation. In this paper, we propose a new gene prediction technique based on Genetic Algorithm (GA) for determining the optimal positions of exons of a gene in a chromosome or genome. The correct identification of the coding and non-coding regions are difficult and computationally demanding. The proposed genetic-based method, named Gene Prediction with Genetic Algorithm (GPGA), reduces this problem by searching only one exon at a time instead of all exons along with its introns. The advantage of this representation is that it can break the entire gene-finding problem into a number of smaller subspaces and thereby reducing the computational complexity. We tested the performance of the GPGA with some benchmark datasets and compared the results with the well-known and relevant techniques. The comparison shows the better or comparable performance of the proposed method (GPGA). We also used GPGA for annotating the human chromosome 21 (HS21) using cross species comparison with the mouse orthologs.

bioinformatics↗

bioRxiv2016

Metafounders are Fst fixation indices and reduce bias in single step genomic evaluations

BACKGROUNDMetafounders are pseudo-individuals that condense the genetic heterozygosity and relationships within and across base pedigree populations, i.e. ancestral populations. This work addresses estimation and usefulness of metafounder relationships in Single Step GBLUP.\n\nRESULTSWe show that the ancestral relationship parameters are proportional to standardized covariances of base allelic frequencies across populations, like Fst fixation indexes. These covariances of base allelic frequencies can be estimated from marker genotypes of related recent individuals, and pedigree. Simple methods for estimation include naive computation of allele frequencies from marker genotypes or a method of moments equating average pedigree-based and marker-based relationships. Complex methods include generalized least squares or maximum likelihood based on pedigree relationships. To our knowledge, methods to infer Fst coefficients and Fst differentiation have not been developed for related populations.\n\nA compatible genomic relationship matrix constructed as a crossproduct of {-1,0,1} codes, and equivalent (up to scale factors) to an identity by state relationship matrix at the markers, is derived. Using a simulation with a single population under selection, in which only males and youngest animals were genotyped, we observed that generalized least squares or maximum likelihood gave accurate and unbiased estimates of the ancestral relationship parameter (true value: 0.40) whereas the other two (naive and method of moments) were biased (estimates of 0.43 and 0.35). We also observed that genomic evaluation by Single Step GBLUP using metafounders was less biased in terms of accurate genetic trend (0.01 instead of 0.12 bias), slightly overdispersed (0.94 instead of 0.99) and as accurate (0.74) than the regular Single Step GBLUP. Single Step GBLUP using metafounders also provided consistent estimates of heritability.\n\nCONCLUSIONSEstimation of metafounder relationship can be achieved using BLUP-like methods with pedigree and markers. Inclusion of metafounder relationships improves bias of genomic predictions with no loss in accuracy.

genetics↗

bioRxiv2016

Phylogenetic relationships and genome size evolution within the genus Amaranthus indicate the ancestors of an ancient crop

The genus Amaranthus consists of 50 to 70 species and harbors several cultivated and weedy species of great economic importance. A small number of suitable traits, phenotypic plasticity, gene flow and hybridization made it difficult to establish the taxonomy and phylogeny of the whole genus despite various studies using molecular markers. We inferred the phylogeny of the Amaranthus genus using genotyping by sequencing (GBS) of 94 genebank accessions representing 35 Amaranthus species and measured their genome sizes. SNPs were called by de novo and reference-based methods, for which we used the distant sugarbeet Beta vulgaris and the closely related Amaranthus hypochondriacus as references. SNP counts and proportions of missing data differed between methods, but the resulting phylogenetic trees were highly similar. A distance-based neighbor joing tree of individual accessions and a species tree calculated with the multispecies coalescent supported a previous taxonomic classification into three subgenera although the subgenus A. Acnida consists of two highly differentiated clades. The analysis of the Hybridus complex within the A. Amaranthus subgenus revealed insights on the history of cultivated grain amaranths. The complex includes the three cultivated grain amaranths and their wild relatives and was well separated from other species in the subgenus. Wild and cultivated amaranth accessions did not differentiate according to the species assignment but clustered by their geographic origin from South and Central America. Different geographically separated populations of Amaranthus hybridus appear to be the common ancestors of the three cultivated grain species and A. quitensis might be additionally be involved in the evolution of South American grain amaranth (A. caudatus). We also measured genome sizes of the species and observed little variation with the exception of two lineages that showed evidence for a recent polyploidization. With the exception of two lineages, genome sizes are quite similar and indicate that polyploidization did not play a major role in the history of the genus.

evolutionary biology↗

bioRxiv2016

Genome-Wide Fitness Analyses of the Foodborne Pathogen Campylobacter jejuni in In Vitro and In Vivo Models

Infection by Campylobacter is recognised as the most common cause of foodborne bacterial illness worldwide. Faecal contamination of meat, especially chicken, during processing represents a key route of transmission to humans. There is currently no licenced vaccine and no Campylobacter-resistant chickens. In addition, preventative measures aimed at reducing environmental contamination and exposure of chickens to Campylobacter jejuni (biosecurity) have been ineffective. There is much interest in the factors/mechanisms that drive C. jejuni colonisation and infection of animals, and survival in the environment. It is anticipated that understanding these mechanisms will guide the development of effective intervention strategies to reduce the burden of C. jejuni infection. Here we present a comprehensive analysis of C. jejuni fitness during growth and survival within and outside hosts. A comparative analysis of transposon (Tn) gene inactivation libraries in three C. jejuni strains by Tn-seq demonstrated that a large proportion, 331 genes, of the C. jejuni genome is dedicated to (in vitro) growth. An extensive Tn library in C. jejuni M1cam (~10,000 mutants) was screened for the colonisation of commercial broiler chickens, survival in houseflies and under nutrient-rich and-poor conditions at low temperature, and infection of human gut epithelial cells. We report C. jejuni factors essential throughout its life cycle and we have identified genes that fulfil important roles across multiple conditions, including maf3, fliW, fliD, pflB and capM, as well as novel genes uniquely implicated in survival outside hosts. Taking a comprehensive screening approach has confirmed previous studies, that the flagella are central to the ability of C. jejuni to interact with its hosts. Future efforts should focus on how to exploit this knowledge to effectively control infections caused by C. jejuni.\n\nAuthor SummaryCampylobacter jejuni is the leading bacterial cause of human diarrhoeal disease. C. jejuni encounters and has to overcome a wide range of \"stress\" conditions whilst passing through the gastrointestinal tract of humans and other animals, during processing of food products, on/in food and in the environment. We have taken a comprehensive approach to understand the basis of C. jejuni growth and within/outside host survival, with the aim to inform future development of intervention strategies. Using a genome-wide transposon gene inactivation approach we identified genes core to the growth of C. jejuni. We also determined genes that were required during the colonisation of chickens, survival in the housefly and under nutrient-rich and -poor conditions at low temperature, and during interaction with human gut epithelial tissue culture cells. This study provides a comprehensive dataset linking C. jejuni genes to growth and survival in models relevant to its life cycle. Genes important across multiple models were identified as well as genes only required under specific conditions. We identified that a large proportion of the C. jejuni genome is dedicated to growth and that the flagella fulfil a prominent role in the interaction with hosts. Our data will aid development of effective control strategies.

microbiology↗

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