Proteogenomics of Non-smoking Lung Cancer in East Asia Delineates Molecular Signatures of Pathogenesis and Progression.

Lung cancer in East Asia is characterized by a high percentage of never-smokers, early onset and predominant EGFR mutations. To illuminate the molecular phenotype of this demographically distinct disease, we performed a deep comprehensive proteogenomic study on a prospectively collected cohort in Taiwan, representing early stage, predominantly female, non-smoking lung adenocarcinoma. Integrated genomic, proteomic, and phosphoproteomic analysis delineated the demographically distinct molecular attributes and hallmarks of tumor progression. Mutational signature analysis revealed age- and gender-related mutagenesis mechanisms, characterized by high prevalence of APOBEC mutational signature in younger females and over-representation of environmental carcinogen-like mutational signatures in older females. A proteomics-informed classification distinguished the clinical characteristics of early stage patients with EGFR mutations. Furthermore, integrated protein network analysis revealed the cellular remodeling underpinning clinical trajectories and nominated candidate biomarkers for patient stratification and therapeutic intervention. This multi-omic molecular architecture may help develop strategies for management of early stage never-smoker lung adenocarcinoma.

SeeCiTe: a method to assess CNV calls from SNP arrays using trio data.

MOTIVATION: Single nucleotide polymorphism (SNP) genotyping arrays remain an attractive platform for assaying copy number variants (CNVs) in large population-wide cohorts. However, current tools for calling CNVs are still prone to extensive false positive calls when applied to biobank scale arrays. Moreover, there is a lack of methods exploiting cohorts with trios available (e.g. nuclear family) to assist in quality control and downstream analyses following the calling. RESULTS: We developed SeeCiTe (Seeing CNVs in Trios), a novel CNV-quality control tool that postprocesses output from current CNV-calling tools exploiting child-parent trio data to classify calls in quality categories and provide a set of visualizations for each putative CNV call in the offspring. We apply it to the Norwegian Mother, Father and Child Cohort Study (MoBa) and show that SeeCiTe improves the specificity and sensitivity compared to the common empiric filtering strategies. To our knowledge, it is the first tool that utilizes probe-level CNV data in trios (and singletons) to systematically highlight potential artifacts and visualize signal intensities in a streamlined fashion suitable for biobank scale studies. AVAILABILITY AND IMPLEMENTATION: The software is implemented in R with the source code freely available at https://github.com/aksenia/SeeCiTe. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Atlantic salmon genome provides insights into rediploidization.

The whole-genome duplication 80 million years ago of the common ancestor of salmonids (salmonid-specific fourth vertebrate whole-genome duplication, Ss4R) provides unique opportunities to learn about the evolutionary fate of a duplicated vertebrate genome in 70 extant lineages. Here we present a high-quality genome assembly for Atlantic salmon (Salmo salar), and show that large genomic reorganizations, coinciding with bursts of transposon-mediated repeat expansions, were crucial for the post-Ss4R rediploidization process. Comparisons of duplicate gene expression patterns across a wide range of tissues with orthologous genes from a pre-Ss4R outgroup unexpectedly demonstrate far more instances of neofunctionalization than subfunctionalization. Surprisingly, we find that genes that were retained as duplicates after the teleost-specific whole-genome duplication 320 million years ago were not more likely to be retained after the Ss4R, and that the duplicate retention was not influenced to a great extent by the nature of the predicted protein interactions of the gene products. Finally, we demonstrate that the Atlantic salmon assembly can serve as a reference sequence for the study of other salmonids for a range of purposes.

The salmon louse genome: Copepod features and parasitic adaptations.

Copepods encompass numerous ecological roles including parasites, detrivores and phytoplankton grazers. Nonetheless, copepod genome assemblies remain scarce. Lepeophtheirus salmonis is an economically and ecologically important ectoparasitic copepod found on salmonid fish. We present the 695.4 Mbp L. salmonis genome assembly containing ≈60% repetitive regions and 13,081 annotated protein-coding genes. The genome comprises 14 autosomes and a ZZ-ZW sex chromosome system. Assembly assessment identified 92.4% of the expected arthropod genes. Transcriptomics supported annotation and indicated a marked shift in gene expression after host attachment, including apparent downregulation of genes related to circadian rhythm coinciding with abandoning diurnal migration. The genome shows evolutionary signatures including loss of genes needed for peroxisome biogenesis, presence of numerous FNII domains, and an incomplete heme homeostasis pathway suggesting heme proteins to be obtained from the host. Despite repeated development of resistance against chemical treatments L. salmonis exhibits low numbers of many genes involved in detoxification.

Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data.

BACKGROUND: SNP arrays, short- and long-read genome sequencing are genome-wide high-throughput technologies that may be used to assay copy number variants (CNVs) in a personal genome. Each of these technologies comes with its own limitations and biases, many of which are well-known, but not all of them are thoroughly quantified. RESULTS: We assembled an ensemble of public datasets of published CNV calls and raw data for the well-studied Genome in a Bottle individual NA12878. This assembly represents a variety of methods and pipelines used for CNV calling from array, short- and long-read technologies. We then performed cross-technology comparisons regarding their ability to call CNVs. Different from other studies, we refrained from using the golden standard. Instead, we attempted to validate the CNV calls by the raw data of each technology. CONCLUSIONS: Our study confirms that long-read platforms enable recalling CNVs in genomic regions inaccessible to arrays or short reads. We also found that the reproducibility of a CNV by different pipelines within each technology is strongly linked to other CNV evidence measures. Importantly, the three technologies show distinct public database frequency profiles, which differ depending on what technology the database was built on.

A novel approach to co-expression network analysis identifies modules and genes relevant for moulting and development in the Atlantic salmon louse (Lepeophtheirus salmonis).

BACKGROUND: The salmon louse (Lepeophtheirus salmonis) is an obligate ectoparasitic copepod living on Atlantic salmon and other salmonids in the marine environment. Salmon lice cause a number of environmental problems and lead to large economical losses in aquaculture every year. In order to develop novel parasite control strategies, a better understanding of the mechanisms of moulting and development of the salmon louse at the transcriptional level is required. METHODS: Three weighted gene co-expression networks were constructed based on the pairwise correlations of salmon louse gene expression profiles at different life stages. Network-based approaches and gene annotation information were applied to identify genes that might be important for the moulting and development of the salmon louse. RNA interference was performed for validation. Regulatory impact factors were calculated for all the transcription factor genes by examining the changes in co-expression patterns between transcription factor genes and deferentially expressed genes in middle stages and moulting stages. RESULTS: Eight gene modules were predicted as important, and 10 genes from six of the eight modules have been found to show observable phenotypes in RNA interference experiments. We knocked down five hub genes from three modules and observed phenotypic consequences in all experiments. In the infection trial, no copepodids with a RAB1A-like gene knocked down were found on fish, while control samples developed to chalimus-1 larvae. Also, a FOXO-like transcription factor obtained highest scores in the regulatory impact factor calculation. CONCLUSIONS: We propose a gene co-expression network-based approach to identify genes playing an important role in the moulting and development of salmon louse. The RNA interference experiments confirm the effectiveness of our approach and demonstrated the indispensable role of a RAB1A-like gene in the development of the salmon louse. We propose that our approach could be generalized to identify important genes associated with a phenotype of interest in other organisms.

Reconstructing ribosomal genes from large scale total RNA meta-transcriptomic data.

MOTIVATION: Technological advances in meta-transcriptomics have enabled a deeper understanding of the structure and function of microbial communities. 'Total RNA' meta-transcriptomics, sequencing of total reverse transcribed RNA, provides a unique opportunity to investigate both the structure and function of active microbial communities from all three domains of life simultaneously. A major step of this approach is the reconstruction of full-length taxonomic marker genes such as the small subunit ribosomal RNA. However, current tools for this purpose are mainly targeted towards analysis of amplicon and metagenomic data and thus lack the ability to handle the massive and complex datasets typically resulting from total RNA experiments. RESULTS: In this work, we introduce MetaRib, a new tool for reconstructing ribosomal gene sequences from total RNA meta-transcriptomic data. MetaRib is based on the popular rRNA assembly program EMIRGE, together with several improvements. We address the challenge posed by large complex datasets by integrating sub-assembly, dereplication and mapping in an iterative approach, with additional post-processing steps. We applied the method to both simulated and real-world datasets. Our results show that MetaRib can deal with larger datasets and recover more rRNA genes, which achieve around 60 times speedup and higher F1 score compared to EMIRGE in simulated datasets. In the real-world dataset, it shows similar trends but recovers more contigs compared with a previous analysis based on random sub-sampling, while enabling the comparison of individual contig abundances across samples for the first time. AVAILABILITY AND IMPLEMENTATION: The source code of MetaRib is freely available at https://github.com/yxxue/MetaRib. CONTACT: yaxin.xue@uib.no or Inge.Jonassen@uib.no. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Repeated bronchoscopy in health and obstructive lung disease: is the airway microbiome stable?

OBJECTIVE: Little is known concerning the stability of the lower airway microbiome. We have compared the microbiota identified by repeated bronchoscopy in healthy subjects and patients with ostructive lung diseaseases (OLD). METHODS: 21 healthy controls and 41 patients with OLD completed two bronchoscopies. In addition to negative controls (NCS) and oral wash (OW) samples, we gathered protected bronchoalveolar lavage in two fractions (PBAL1 and PBAL2) and protected specimen brushes (PSB). After DNA extraction, we amplified the V3V4 region of the 16S rRNA gene, and performed paired-end sequencing (Illumina MiSeq). Initial bioinformatic processing was carried out in the QIIME-2 pipeline, identifying amplicon sequence variants (ASVs) with the DADA2 algorithm. Potentially contaminating ASVs were identified and removed using the decontam package in R and the sequenced NCS. RESULTS: A final table of 551 ASVs consisted of 19 × 106 sequences. Alpha diversity was lower in the second exam for OW samples, and borderline lower for PBAL1, with larger differences in subjects not having received intercurrent antibiotics. Permutational tests of beta diversity indicated that within-individual changes were significantly lower than between-individual changes. A non-parametric trend test showed that differences in composition between the two exams (beta diversity) were largest in the PSBs, and that these differences followed a pattern of PSB > PBAL2 > PBAL1 > OW. Time between procedures was not associated with increased diversity. CONCLUSION: The airways microbiota varied between examinations. However, there is compositional microbiota stability within a person, beyond that of chance, supporting the notion of a transient airways microbiota with a possibly more stable individual core microbiome.

Using Deep Learning to Extrapolate Protein Expression Measurements.

Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R2=0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.

RASflow: an RNA-Seq analysis workflow with Snakemake.

BACKGROUND: With the cost of DNA sequencing decreasing, increasing amounts of RNA-Seq data are being generated giving novel insight into gene expression and regulation. Prior to analysis of gene expression, the RNA-Seq data has to be processed through a number of steps resulting in a quantification of expression of each gene/transcript in each of the analyzed samples. A number of workflows are available to help researchers perform these steps on their own data, or on public data to take advantage of novel software or reference data in data re-analysis. However, many of the existing workflows are limited to specific types of studies. We therefore aimed to develop a maximally general workflow, applicable to a wide range of data and analysis approaches and at the same time support research on both model and non-model organisms. Furthermore, we aimed to make the workflow usable also for users with limited programming skills. RESULTS: Utilizing the workflow management system Snakemake and the package management system Conda, we have developed a modular, flexible and user-friendly RNA-Seq analysis workflow: RNA-Seq Analysis Snakemake Workflow (RASflow). Utilizing Snakemake and Conda alleviates challenges with library dependencies and version conflicts and also supports reproducibility. To be applicable for a wide variety of applications, RASflow supports the mapping of reads to both genomic and transcriptomic assemblies. RASflow has a broad range of potential users: it can be applied by researchers interested in any organism and since it requires no programming skills, it can be used by researchers with different backgrounds. The source code of RASflow is available on GitHub: https://github.com/zhxiaokang/RASflow. CONCLUSIONS: RASflow is a simple and reliable RNA-Seq analysis workflow covering many use cases.

Quantitative transcriptomics, and lipidomics in evaluating ovarian developmental effects in Atlantic cod (Gadus morhua) caged at a capped marine waste disposal site.

In the present study, a previously capped waste disposal site at Kollevåg (Norway) was selected to study the effects of contaminant leakage on biomarkers associated with Atlantic cod (Gadus morhua) reproductive endocrinology and development. Immature cod were caged for 6 weeks at 3 locations, selected to achieve a spatial gradient of contamination, and compared to a reference station. Quantitative transcriptomic, and lipidomic analysis was used to evaluate the effects of the potential complex contaminant mixture on ovarian developmental and endocrine physiology. The number of expressed transcripts, with 0.75 log2-fold differential expression or more, varied among stations and paralleled the severity of contamination. Particularly, significant bioaccumulation of ∑PCB-7, ∑DDTs and ∑PBDEs were observed at station 1, compared to the other station, including the reference station. Respectively 1416, 698 and 719 differentially expressed genes (DEGs), were observed at stations 1, 2 and 3, compared to the reference station, with transcripts belonging to steroid hormone synthesis pathway being significantly upregulation. Transcription factors such as esr2 and ahr2 were increased at all three stations, with highest fold-change at Station 1. MetaCore pathway maps identified affected pathways that are involved in ovarian physiology, where some unique pathways were significantly affected at each station. For the lipidomics, sphingolipid metabolism was particularly affected at station 1, and these effects paralleled the high contaminant burden at this station. Overall, our findings showed a novel and direct association between contaminant burden and ovarian toxicological and endocrine physiological responses in cod caged at the capped Kollevåg waste disposal site.

Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data from heterogeneous samples.

BACKGROUND: Towards discovering robust cancer biomarkers, it is imperative to unravel the cellular heterogeneity of patient samples and comprehend the interactions between cancer cells and the various cell types in the tumor microenvironment. The first generation of 'partial' computational deconvolution methods required prior information either on the cell/tissue type proportions or the cell/tissue type-specific expression signatures and the number of involved cell/tissue types. The second generation of 'complete' approaches allowed estimating both of the cell/tissue type proportions and cell/tissue type-specific expression profiles directly from the mixed gene expression data, based on known (or automatically identified) cell/tissue type-specific marker genes. RESULTS: We present Deblender, a flexible complete deconvolution tool operating in semi-/unsupervised mode based on the user's access to known marker gene lists and information about cell/tissue composition. In case of no prior knowledge, global gene expression variability is used in clustering the mixed data to substitute marker sets with cluster sets. In addition, we integrate a model selection criterion to predict the number of constituent cell/tissue types. Moreover, we provide a tailored algorithmic scheme to estimate mixture proportions for realistic experimental cases where the number of involved cell/tissue types exceeds the number of mixed samples. We assess the performance of Deblender and a set of state-of-the-art existing tools on a comprehensive set of benchmark and patient cancer mixture expression datasets (including TCGA). CONCLUSION: Our results corroborate that Deblender can be a valuable tool to improve understanding of gene expression datasets with implications for prediction and clinical utilization. Deblender is implemented in MATLAB and is available from ( https://github.com/kondim1983/Deblender/ ).

Mathematical Modelling of Nitric Oxide/Cyclic GMP/Cyclic AMP Signalling in Platelets.

Platelet activation contributes to normal haemostasis but also to pathologic conditions like stroke and cardiac infarction. Signalling by cGMP and cAMP inhibit platelet activation and are therefore attractive targets for thrombosis prevention. However, extensive cross-talk between the cGMP and cAMP signalling pathways in multiple tissues complicates the selective targeting of their activities. We have used mathematical modelling based on experimental data from the literature to quantify the steady state behaviour of nitric oxide (NO)/cGMP/cAMP signalling in platelets. The analysis provides an assessment of NO-induced cGMP synthesis and PKG activation as well as cGMP-mediated cAMP and PKA activation though modulation of phosphodiesterase (PDE2 and 3) activities. Both one- and two-compartment models of platelet cyclic nucleotide signalling are presented. The models provide new insight for understanding how NO signalling to cGMP and indirectly cAMP, can inhibit platelet shape-change, the initial step of platelet activation. Only the two-compartment models could account for the experimental observation that NO-mediated PKA activation can occur when the bulk platelet cAMP level is unchanged. The models revealed also a potential for hierarchical interplay between the different platelet phosphodiesterases. Specifically, the models predict, unexpectedly, a strong effect of pharmacological inhibitors of cGMP-specific PDE5 on the cGMP/cAMP cross-talk. This may explain the successful use of weak PDE5-inhibitors, such as dipyridamole, in anti-platelet therapy. In conclusion, increased NO signalling or PDE5 inhibition are attractive ways of increasing cGMP-cAMP cross-talk selectively in platelets.

Whole genome resequencing reveals diagnostic markers for investigating global migration and hybridization between minke whale species.

BACKGROUND: In the marine environment, where there are few absolute physical barriers, contemporary contact between previously isolated species can occur across great distances, and in some cases, may be inter-oceanic. An example of this can be seen in the minke whale species complex. Antarctic minke whales are genetically and morphologically distinct from the common minke found in the north Atlantic and Pacific oceans, and the two species are estimated to have been isolated from each other for 5 million years or more. Recent atypical migrations from the southern to the northern hemisphere have been documented and fertile hybrids and back-crossed individuals between both species have also been identified. However, it is not known whether this represents a contemporary event, potentially driven by ecosystem changes in the Antarctic, or a sporadic occurrence happening over an evolutionary time-scale. We successfully used whole genome resequencing to identify a panel of diagnostic SNPs which now enable us address this evolutionary question. RESULTS: A large number of SNPs displaying fixed or nearly fixed allele frequency differences among the minke whale species were identified from the sequence data. Five panels of putatively diagnostic markers were established on a genotyping platform for validation of allele frequencies; two panels (26 and 24 SNPs) separating the two species of minke whale, and three panels (22, 23, and 24 SNPs) differentiating the three subspecies of common minke whale. The panels were validated against a set of reference samples, demonstrating the ability to accurately identify back-crossed whales up to three generations. CONCLUSIONS: This work has resulted in the development of a panel of novel diagnostic genetic markers to address inter-oceanic and global contact among the genetically isolated minke whale species and sub-species. These markers, including a globally relevant genetic reference data set for this species complex, are now openly available for researchers interested in identifying other potential whale hybrids in the world's oceans. The approach used here, combining whole genome resequencing and high-throughput genotyping, represents a universal approach to develop similar tools for other species and population complexes.

RareVariantVis: new tool for visualization of causative variants in rare monogenic disorders using whole genome sequencing data.

MOTIVATION: The search for causative genetic variants in rare diseases of presumed monogenic inheritance has been boosted by the implementation of whole exome (WES) and whole genome (WGS) sequencing. In many cases, WGS seems to be superior to WES, but the analysis and visualization of the vast amounts of data is demanding. RESULTS: To aid this challenge, we have developed a new tool-RareVariantVis-for analysis of genome sequence data (including non-coding regions) for both germ line and somatic variants. It visualizes variants along their respective chromosomes, providing information about exact chromosomal position, zygosity and frequency, with point-and-click information regarding dbSNP IDs, gene association and variant inheritance. Rare variants as well as de novo variants can be flagged in different colors. We show the performance of the RareVariantVis tool in the Genome in a Bottle WGS data set. AVAILABILITY AND IMPLEMENTATION: https://www.bioconductor.org/packages/3.3/bioc/html/RareVariantVis.html CONTACT: tomasz.stokowy@k2.uib.no SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Disentangling genetic and epigenetic determinants of ultrafast adaptation.

A major rationale for the advocacy of epigenetically mediated adaptive responses is that they facilitate faster adaptation to environmental challenges. This motivated us to develop a theoretical-experimental framework for disclosing the presence of such adaptation-speeding mechanisms in an experimental evolution setting circumventing the need for pursuing costly mutation-accumulation experiments. To this end, we exposed clonal populations of budding yeast to a whole range of stressors. By growth phenotyping, we found that almost complete adaptation to arsenic emerged after a few mitotic cell divisions without involving any phenotypic plasticity. Causative mutations were identified by deep sequencing of the arsenic-adapted populations and reconstructed for validation. Mutation effects on growth phenotypes, and the associated mutational target sizes were quantified and embedded in data-driven individual-based evolutionary population models. We found that the experimentally observed homogeneity of adaptation speed and heterogeneity of molecular solutions could only be accounted for if the mutation rate had been near estimates of the basal mutation rate. The ultrafast adaptation could be fully explained by extensive positive pleiotropy such that all beneficial mutations dramatically enhanced multiple fitness components in concert. As our approach can be exploited across a range of model organisms exposed to a variety of environmental challenges, it may be used for determining the importance of epigenetic adaptation-speeding mechanisms in general.

Tumour-associated glial host cells display a stem-like phenotype with a distinct gene expression profile and promote growth of GBM xenografts.

BACKGROUND: Little is known about the role of glial host cells in brain tumours. However, supporting stromal cells have been shown to foster tumour growth in other cancers. METHODS: We isolated stromal cells from patient-derived glioblastoma (GBM) xenografts established in GFP-NOD/scid mice. With simultaneous removal of CD11b+ immune and CD31+ endothelial cells by fluorescence activated cell sorting (FACS), we obtained a population of tumour-associated glial cells, TAGs, expressing markers of terminally differentiaed glial cell types or glial progenitors. This cell population was subsequently characterised using gene expression analyses and immunocytochemistry. Furthermore, sphere formation was assessed in vitro and their glioma growth-promoting ability was examined in vivo. Finally, the expression of TAG related markers was validated in human GBMs. RESULTS: TAGs were highly enriched for the expression of glial cell proteins including GFAP and myelin basic protein (MBP), and immature markers such as Nestin and O4. A fraction of TAGs displayed sphere formation in stem cell medium. Moreover, TAGs promoted brain tumour growth in vivo when co-implanted with glioma cells, compared to implanting only glioma cells, or glioma cells and unconditioned glial cells from mice without tumours. Genome-wide microarray analysis of TAGs showed an expression profile distinct from glial cells from healthy mice brains. Notably, TAGs upregulated genes associated with immature cell types and self-renewal, including Pou3f2 and Sox2. In addition, TAGs from highly angiogenic tumours showed upregulation of angiogenic factors, including Vegf and Angiopoietin 2. Immunohistochemistry of three GBMs, two patient biopsies and one GBM xenograft, confirmed that the expression of these genes was mainly confined to TAGs in the tumour bed. Furthermore, their expression profiles displayed a significant overlap with gene clusters defining prognostic subclasses of human GBMs. CONCLUSIONS: Our data demonstrate that glial host cells in brain tumours are functionally distinct from glial cells of healthy mice brains. Furthermore, TAGs display a gene expression profile with enrichment for genes related to stem cells, immature cell types and developmental processes. Future studies are needed to delineate the biological mechanisms regulating the brain tumour-host interplay.

The genome sequence of Atlantic cod reveals a unique immune system.

Atlantic cod (Gadus morhua) is a large, cold-adapted teleost that sustains long-standing commercial fisheries and incipient aquaculture. Here we present the genome sequence of Atlantic cod, showing evidence for complex thermal adaptations in its haemoglobin gene cluster and an unusual immune architecture compared to other sequenced vertebrates. The genome assembly was obtained exclusively by 454 sequencing of shotgun and paired-end libraries, and automated annotation identified 22,154 genes. The major histocompatibility complex (MHC) II is a conserved feature of the adaptive immune system of jawed vertebrates, but we show that Atlantic cod has lost the genes for MHC II, CD4 and invariant chain (Ii) that are essential for the function of this pathway. Nevertheless, Atlantic cod is not exceptionally susceptible to disease under natural conditions. We find a highly expanded number of MHC I genes and a unique composition of its Toll-like receptor (TLR) families. This indicates how the Atlantic cod immune system has evolved compensatory mechanisms in both adaptive and innate immunity in the absence of MHC II. These observations affect fundamental assumptions about the evolution of the adaptive immune system and its components in vertebrates.

High-throughput metabarcoding of eukaryotic diversity for environmental monitoring of offshore oil-drilling activities.

As global exploitation of available resources increases, operations extend towards sensitive and previously protected ecosystems. It is important to monitor such areas in order to detect, understand and remediate environmental responses to stressors. The natural heterogeneity and complexity of communities means that accurate monitoring requires high resolution, both temporally and spatially, as well as more complete assessments of taxa. Increased resolution and taxonomic coverage is economically challenging using current microscopy-based monitoring practices. Alternatively, DNA sequencing-based methods have been suggested for cost-efficient monitoring, offering additional insights into ecosystem function and disturbance. Here, we applied DNA metabarcoding of eukaryotic communities in marine sediments, in areas of offshore drilling on the Norwegian continental shelf. Forty-five samples, collected from seven drilling sites in the Troll/Oseberg region, were assessed, using the small subunit ribosomal RNA gene as a taxonomic marker. In agreement with results based on classical morphology-based monitoring, we were able to identify changes in sediment communities surrounding oil platforms. In addition to overall changes in community structure, we identified several potential indicator taxa, responding to pollutants associated with drilling fluids. These included the metazoan orders Macrodasyida, Macrostomida and Ceriantharia, as well as several ciliates and other protist taxa, typically not targeted by environmental monitoring programmes. Analysis of a co-occurrence network to study the distribution of taxa across samples provided a framework for better understanding the impact of anthropogenic activities on the benthic food web, generating novel, testable hypotheses of trophic interactions structuring benthic communities.

Linkage-disequilibrium-based binning affects the interpretation of GWASs.

Genome-wide association studies (GWASs) are critically dependent on detailed knowledge of the pattern of linkage disequilibrium (LD) in the human genome. GWASs generate lists of variants, usually SNPs, ranked according to the significance of their association to a trait. Downstream analyses generally focus on the gene or genes that are physically closest to these SNPs and ignore their LD profile with other SNPs. We have developed a flexible R package (LDsnpR) that efficiently assigns SNPs to genes on the basis of both their physical position and their pairwise LD with other SNPs. We used the positional-binning and LD-based-binning approaches to investigate whether including these "LD-based" SNPs would affect the interpretation of three published GWASs on bipolar affective disorder (BP) and of the imputed versions of two of these GWASs. We show how including LD can be important for interpreting and comparing GWASs. In the published, unimputed GWASs, LD-based binning effectively "recovered" 6.1%-8.3% of Ensembl-defined genes. It altered the ranks of the genes and resulted in nonnegligible differences between the lists of the top 2,000 genes emerging from the two binning approaches. It also improved the overall gene-based concordance between independent BP studies. In the imputed datasets, although the increases in coverage (>0.4%) and rank changes were more modest, even greater concordance between the studies was observed, attesting to the potential of LD-based binning on imputed data as well. Thus, ignoring LD can result in the misinterpretation of the GWAS findings and have an impact on subsequent genetic and functional studies.