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Plasmids are the primary vector for horizontal transfer of antimicrobial resistance (AMR) within bacterial populations. We applied the MOB-suite, a toolset for reconstructing and typing plasmids, to 150 767 publicly available Salmonella whole-genome sequencing samples covering 1204 distinct serovars to produce a large-scale population survey of plasmids based on the MOB-suite plasmid nomenclature. Reconstruction yielded 183 017 plasmids representing 1044 primary MOB-clusters and 830 potentially novel MOB-clusters. Replicon and relaxase typing were able to type 83.4 and 58 % of plasmids, respectively, compared to 99.9 % for MOB-clusters. Within this work, we developed an approach to characterize the horizonal transfer of MOB-clusters and AMR genes across different serotypes, as well as the diversity of MOB-cluster associations with AMR genes. Aggregating conjugative mobility predictions provided by the MOB-suite and their corresponding serovar entropy demonstrated that non-mobilizable plasmids were associated with fewer serotypes compared to mobilizable or conjugative MOB-clusters. The host-range predictions for MOB-clusters also showed differences between the mobility classes, with mobilizable MOB-clusters accounting for 88.3 % of the multi-phyla (broad-host-range) predictions compared to 3 and 8.6 % for conjugative and non-mobilizable, respectively. A total of 296 (22 %) of identified MOB-clusters were associated with at least one resistance gene, indicating that the majority of Salmonella plasmids are not involved in AMR dissemination. Shannon entropy analysis of horizontal transfer of AMR genes across serovars and MOB-clusters demonstrated that horizonal transfer of genes is higher between serovars compared to transfer between different MOB-clusters. In addition to the population structure characterization based on primary MOB-clusters, we characterized a multi-plasmid outbreak responsible for the global dissemination of bla CMY-2 across different serotypes using higher resolution MOB-suite secondary cluster codes. The plasmid characterization approach developed here can be applied to different organisms to identify plasmids and genes which pose high risks for horizontal transfer.

Enterococcus faecium is a ubiquitous opportunistic pathogen that is exhibiting increasing levels of antimicrobial resistance (AMR). Many of the genes that confer resistance and pathogenic functions are localized on mobile genetic elements (MGEs), which facilitate their transfer between lineages. Here, features including resistance determinants, virulence factors and MGEs were profiled in a set of 1273 E. faecium genomes from two disparate geographic locations (in the UK and Canada) from a range of agricultural, clinical and associated habitats. Neither lineages of E. faecium , type A and B, nor MGEs are constrained by geographic proximity, but our results show evidence of a strong association of many profiled genes and MGEs with habitat. Many features were associated with a group of clinical and municipal wastewater genomes that are likely forming a new human-associated ecotype within type A. The evolutionary dynamics of E. faecium make it a highly versatile emerging pathogen, and its ability to acquire, transmit and lose features presents a high risk for the emergence of new pathogenic variants and novel resistance combinations. This study provides a workflow for MGE-centric surveillance of AMR in Enterococcus that can be adapted to other pathogens.

Ingestion of food- or waterborne antibiotic-resistant bacteria may lead to dissemination of antibiotic resistance genes (ARGs) in the gut microbiota. The gut microbiota often suffers from various disturbances. It is not clear whether and how disturbed microbiota may affect ARG mobility under antibiotic treatments. For proof of concept, in the presence or absence of streptomycin pre-treatment, mice were inoculated orally with a β-lactam-susceptible Salmonella enterica serovar Heidelberg clinical isolate (recipient) and a β-lactam resistant Escherichia coli O80:H26 isolate (donor) carrying a blaCMY-2 gene on an IncI2 plasmid. Immediately following inoculation, mice were treated with or without ampicillin in drinking water for 7 days. Faeces were sampled, donor, recipient and transconjugant were enumerated, blaCMY-2 abundance was determined by quantitative PCR, faecal microbial community composition was determined by 16S rRNA amplicon sequencing and cecal samples were observed histologically for evidence of inflammation. In faeces of mice that received streptomycin pre-treatment, the donor abundance remained high, and the abundance of S. Heidelberg transconjugant and the relative abundance of Enterobacteriaceae increased significantly during the ampicillin treatment. Co-blooming of the donor, transconjugant and commensal Enterobacteriaceae in the inflamed intestine promoted significantly (PblaCMY-2 gene in the gut microbiota of mice that received the combination of streptomycin pre-treatment and ampicillin treatment (Str–Amp) compared to the other mice. Following cessation of the ampicillin treatment, faecal shedding of S. Heidelberg transconjugant persisted much longer from mice in the Str–Amp group compared to the other mice. In addition, only mice in the Str–Amp group shed a commensal E. coli O2:H6 transconjugant, which carries three copies of the blaCMY-2 gene, one on the IncI2 plasmid and two on the chromosome. The findings highlight the significance of pre-existing gut microbiota for ARG dissemination and persistence during and following antibiotic treatments of infectious diseases.

Escherichia coli is a priority foodborne pathogen of public health concern and phenotypic serotyping provides critical information for surveillance and outbreak detection activities. Public health and food safety laboratories are increasingly adopting whole-genome sequencing (WGS) for characterizing pathogens, but it is imperative to maintain serotype designations in order to minimize disruptions to existing public health workflows. Multiple in silico tools have been developed for predicting serotypes from WGS data, including SRST2, SerotypeFinder and EToKi EBEis, but these tools were not designed with the specific requirements of diagnostic laboratories, which include: speciation, input data flexibility (fasta/fastq), quality control information and easily interpretable results. To address these specific requirements, we developed ECTyper (https://github.com/phac-nml/ecoli_serotyping) for performing both speciation within Escherichia and Shigella , and in silico serotype prediction. We compared the serotype prediction performance of each tool on a newly sequenced panel of 185 isolates with confirmed phenotypic serotype information. We found that all tools were highly concordant, with 92–97 % for O-antigens and 98–100 % for H-antigens, and ECTyper having the highest rate of concordance. We extended the benchmarking to a large panel of 6954 publicly available E. coli genomes to assess the performance of the tools on a more diverse dataset. On the public data, there was a considerable drop in concordance, with 75–91 % for O-antigens and 62–90 % for H-antigens, and ECTyper and SerotypeFinder being the most concordant. This study highlights that in silico predictions show high concordance with phenotypic serotyping results, but there are notable differences in tool performance. ECTyper provides highly accurate and sensitive in silico serotype predictions, in addition to speciation, and is designed to be easily incorporated into bioinformatic workflows.

The reliable taxonomic identification of organisms through DNA sequence data requires a well parameterized library of curated reference sequences. However, it is estimated that just 15% of described animal species are represented in public sequence repositories. To begin to address this deficiency, we provide DNA barcodes for 1,500,003 animal specimens collected from 23 terrestrial and aquatic ecozones at sites across Canada, a nation that comprises 7% of the planet’s land surface. In total, 14 phyla, 43 classes, 163 orders, 1123 families, 6186 genera, and 64,264 Barcode Index Numbers (BINs; a proxy for species) are represented. Species-level taxonomy was available for 38% of the specimens, but higher proportions were assigned to a genus (69.5%) and a family (99.9%). Voucher specimens and DNA extracts are archived at the Centre for Biodiversity Genomics where they are available for further research. The corresponding sequence and taxonomic data can be accessed through the Barcode of Life Data System, GenBank, the Global Biodiversity Information Facility, and the Global Genome Biodiversity Network Data Portal., Measurement(s)DNA • digital imagingTechnology Type(s)taxonomic diversity assessment by targeted gene survey • digital curationFactor Type(s)species • geographic location • habitatSample Characteristic - OrganismMetazoaSample Characteristic - Environmentterrestrial biome • freshwater biome • marine biomeSample Characteristic - LocationCanada Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.10298354

Bacterial plasmids play a large role in allowing bacteria to adapt to changing environments and can pose a significant risk to human health if they confer virulence and antimicrobial resistance (AMR). Plasmids differ significantly in the taxonomic breadth of host bacteria in which they can successfully replicate, this is commonly referred to as ‘host range’ and is usually described in qualitative terms of ‘narrow’ or ‘broad’. Understanding the host range potential of plasmids is of great interest due to their ability to disseminate traits such as AMR through bacterial populations and into human pathogens. We developed the MOB-suite to facilitate characterization of plasmids and introduced a whole-sequence-based classification system based on clustering complete plasmid sequences using Mash distances (https://github.com/phac-nml/mob-suite). We updated the MOB-suite database from 12 091 to 23 671 complete sequences, representing 17 779 unique plasmids. With advances in new algorithms for rapidly calculating average nucleotide identity (ANI), we compared clustering characteristics using two different distance measures – Mash and ANI – and three clustering algorithms on the unique set of plasmids. The plasmid nomenclature is designed to group highly similar plasmids together that are unlikely to have multiple representatives within a single cell. Based on our results, we determined that clusters generated using Mash and complete-linkage clustering at a Mash distance of 0.06 resulted in highly homogeneous clusters while maintaining cluster size. The taxonomic distribution of plasmid biomarker sequences for replication and relaxase typing, in combination with MOB-suite whole-sequence-based clusters have been examined in detail for all high-quality publicly available plasmid sequences. We have incorporated prediction of plasmid replication host range into the MOB-suite based on observed distributions of these sequence features in combination with known plasmid hosts from the literature. Host range is reported as the highest taxonomic rank that covers all of the plasmids which share replicon or relaxase biomarkers or belong to the same MOB-suite cluster code. Reporting host range based on these criteria allows for comparisons of host range between studies and provides information for plasmid surveillance.

Ingestion of food- or waterborne antibiotic-resistant bacteria may lead to the dissemination of antibiotic-resistance genes in the gut microbiota and the development of antibiotic-resistant bacterial infection, a significant threat to animal and public health. Food or water may be contaminated with multiple resistant bacteria, but animal models on gene transfer were mainly based on single-strain infections. In this study, we investigated the mobility of β-lactam resistance following infection with single- versus multi-strain of resistant bacteria under ampicillin treatment. We characterized three bacterial strains isolated from food-animal production systems, Escherichia coli O80:H26 and Salmonella enterica serovars Bredeney and Heidelberg. Each strain carries at least one conjugative plasmid that encodes a β-lactamase. We orally infected mice with each or all three bacterial strain(s) in the presence or absence of ampicillin treatment. We assessed plasmid transfer from the three donor bacteria to an introduced E. coli CV601gfp recipient in the mouse gut, and evaluated the impacts of the bacterial infection on gut microbiota and gut health. In the absence of ampicillin treatment, none of the donor or recipient bacteria established in the normal gut microbiota and plasmid transfer was not detected. In contrast, the ampicillin treatment disrupted the gut microbiota and enabled S. Bredeney and Heidelberg to colonize and transfer their plasmids to the E. coli CV601gfp recipient. E. coli O80:H26 on its own failed to colonize the mouse gut. However, during co-infection with the two Salmonella strains, E. coli O80:H26 colonized and transferred its plasmid to the E. coli CV601gfp recipient and a residential E. coli O2:H6 strain. The co-infection significantly increased plasmid transfer frequency, enhanced Proteobacteria expansion and resulted in inflammation in the mouse gut. Our findings suggest that single-strain infection models for evaluating in vivo gene transfer may underrepresent the consequences of multi-strain infections following the consumption of heavily contaminated food or water.

The molecular details of the association between the human Fyn-SH3 domain, and the fragment of 18.5-kDa myelin basic protein (MBP) spanning residues S38-S107 (denoted as xα2-peptide, murine sequence numbering), were studied in silico via docking and molecular dynamics over 50-ns trajectories. The results show that interaction between the two proteins is energetically favorable and heavily dependent on the MBP proline-rich region (P93-P98) in both aqueous and membrane environments. In aqueous conditions, the xα2-peptide/Fyn-SH3 complex adopts a "sandwich""-like structure. In the membrane context, the xα2-peptide interacts with the Fyn-SH3 domain via the proline-rich region and the β-sheets of Fyn-SH3, with the latter wrapping around the proline-rich region in a form of a clip. Moreover, the simulations corroborate prior experimental evidence of the importance of upstream segments beyond the canonical SH3-ligand. This study thus provides a more-detailed glimpse into the context-dependent interaction dynamics and importance of the β-sheets in Fyn-SH3 and proline-rich region of MBP. Proteins 2017; 85:1336-1350. © 2017 Wiley Periodicals, Inc.

BioHansel performs high-resolution genotyping of bacterial isolates by identifying phylogenetically informative single nucleotide polymorphisms (SNPs), also known as canonical SNPs, in whole genome sequencing (WGS) data. The application uses a fast k-mer matching algorithm to map pathogen WGS data to canonical SNPs contained in hierarchically structured schemas and assigns genotypes based on the detected SNP profile. Using modest computing resources, BioHansel efficiently types isolates from raw sequence reads or assembled contigs in a matter of seconds, making it attractive for use by public health, food safety, environmental, and agricultural authorities that wish to apply WGS methodologies for their surveillance, diagnostics, and research programs. BioHansel currently provides canonical SNP genotyping schemas for four prevalent Salmonella serovars—Typhi, Typhimurium, Enteritidis and Heidelberg—as well as a schema for Mycobacterium tuberculosis. Users can also supply their own schemas for genotyping other organisms. BioHansel’s quality assurance system assesses the validity of the genotyping results and can identify low quality data, contaminated datasets, and misidentified organisms. BioHansel is targeted to support surveillance, source attribution, risk assessment, diagnostics, and rapid screening for public health purposes, such as product recalls. BioHansel is an open source application with packages available for PyPI, Conda, and the Galaxy workflow manager. In summary, BioHansel performs efficient, rapid, accurate, and high-resolution classification of bacterial genomes from sequence reads or assembled contigs on standard computing hardware. BioHansel is suitable for use as a general research tool as well as in fully operationalized WGS workflows at the front lines of infectious disease surveillance, diagnostics, and outbreak investigation and response.Impact statementPublic health, food safety, environmental, and agricultural authorities are currently engaged in a global effort to incorporate whole genome sequencing technologies into their infectious disease research, surveillance, and outbreak investigation programs. Its widespread adoption, however, has been impeded by two major obstacles: the need for high performance computing to generate results and the expert knowledge required to interpret and communicate those results. BioHansel addresses these limitations by rapidly genotyping pathogens from whole genome sequence data in an accurate, simple, familiar, and easily sharable manner using standard computing resources. BioHansel provides a compact and readily interpretable genotype based on canonical SNP genotyping schemas. BioHansel’s genotyping nomenclature encodes the pathogen’s position in its population structure, which simplifies and facilitates its comparison with actively circulating strains and historical strains. The genotyping information provided by BioHansel can identify points of intervention to prevent the spread of pathogenic bacteria, screen for the presence of priority pathogens, and perform source attribution and risk assessment. Thus, BioHansel serves as a readily accessible and powerful WGS method, implementable on a laptop, for genotyping pathogens to detect, monitor, and control the emergence and spread of infectious disease through surveillance, screening, diagnostics, and outbreak investigation and response activities.Data summaryBioHansel is a Python 3 application available as PyPI, Conda Galaxy Tool Shed packages. It is an open source application distributed under the Apache License, Version 2.0. Source code is available at https://github.com/phac-nml/biohansel. The BioHansel user guide is available at https://bio-hansel.readthedocs.io/en/readthedocs/. Supplementary Materials are available at https://github.com/phac-nml/biohansel-manuscript-supplementary-data.The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

Harmful algal blooms have important implications for the health, functioning and services of aquatic ecosystems. Our ability to detect and monitor these events is often challenged by the lack of rapid and cost-effective methods to identify bloom-forming organisms and their potential for toxin production, Here, we developed and applied a combination of DNA barcoding and Next Generation Sequencing (NGS) for the rapid assessment of phytoplankton community composition with focus on two important indicators of ecosystem health: toxigenic bloom-forming cyanobacteria and impaired planktonic biodiversity. To develop this molecular toolset for identification of cyanobacterial and algal species present in HABs (Harmful Algal Blooms), hereafter called HAB-ID, we optimized NGS protocols, applied a newly developed bioinformatics pipeline and constructed a BOLD (Barcode of Life Data System) 16S reference database from cultures of 203 cyanobacterial and algal strains representing 101 species with particular focus on bloom and toxin producing taxa. Using the new reference database of 16S rDNA sequences and constructed mock communities of mixed strains for protocol validation we developed new NGS primer set which can recover 16S from both cyanobacteria and eukaryotic algal chloroplasts. We also developed DNA extraction protocols for cultured algal strains and environmental samples, which match commercial kit performance and offer a cost-efficient solution for large scale ecological assessments of harmful blooms while giving benefits of reproducibility and increased accessibility. Our bioinformatics pipeline was designed to handle low taxonomic resolution for problematic genera of cyanobacteria such as theAnabaena-Aphanizomenon-Dolichospermumspecies complex, two clusters ofAnabaena(I and II),PlanktothrixandMicrocystis. This newly developed HAB-ID toolset was further validated by applying it to assess cyanobacterial and algal composition in field samples from waterbodies with recurrent HABs events.

The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach, Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified, respectively, 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Although the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response, and several cancer types. Importantly, we observed significant overrepresentation of cancer-related pathways including glioma, especially in the XORnet network, suggesting a nonignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors.

Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIFcond) may lead to improved performance compared to Breiman's implementation of Random Forests (RF). Among all newly introduced CIF-based methods and five network scenarios obtained from the DREAM4 challenge, CIFcond performed best. Networks derived from well-tuned CIFs, obtained by simply averaging P-values over tree ensembles (CIFmean) are particularly attractive, because they combine adequate performance with computational efficiency. Moreover, thresholds for variable selection are based on significance levels for P-values and, hence, do not need to be tuned. From a practical point of view, our extensive simulations show the potential advantages of CIFmean-based methods. Although more work is needed to improve on speed, especially when fully exploiting the advantages of CITs in the context of heterogeneous and correlated data, we have shown that CIF methodology can be flexibly inserted in a framework to infer biological interactions. Notably, we confirmed biologically relevant interaction between IL2RA and FOXP1, linked to the IL-2 signaling pathway and to type 1 diabetes.

We report here 32 completed closed genome sequences of strains representing 30 serotypes of Salmonella. These genome sequences will provide useful references for understanding the genetic variation within Salmonella enterica serotypes, particularly as references to aid in comparative genomics studies, as well as providing information for improving in silico serotyping accuracy.

Environmental DNA (eDNA) is an effective approach for detecting vertebrates and plants, especially in aquatic ecosystems, but prior studies have largely examined eDNA in cool temperate settings. By contrast, this study employs eDNA to survey the fish fauna in tropical Lake Bacalar (Mexico) with the additional goal of assessing the possible presence of invasive fishes, such as Amazon sailfin catfish. Sediment and water samples were collected from eight stations in Lake Bacalar on three occasions over a 4-month interval. Each sample was stored in the presence or absence of lysis buffer to compare eDNA recovery. Short fragments (184-187 bp) of the cytochrome c oxidase I (COI) gene were amplified using fusion primers and then sequenced on Ion Torrent PGM and S5 before their source species were determined using a custom reference sequence database constructed on BOLD. In total, eDNA sequences were recovered from 75 species of vertebrates including 47 fishes, 15 birds, 7 mammals, 5 reptiles, and 1 amphibian. Although all species are known from this region, 6 fish species represent new records for the study area, while 2 require verification. Sequences for five species (2 birds, 2 mammals, 1 reptile) were only detected from sediments, while sequences from 52 species were only recovered from water. Because DNA from the Amazon sailfin catfish was not detected, we used a mock eDNA experiment to confirm our methods were appropriate for its detection. We developed protocols that enabled the recovery of eDNA from tropical oligotrophic aquatic ecosystems, and confirmed their effectiveness in detecting diverse species of vertebrates including an invasive species of Amazon catfish.

Inflammatory bowel disease (IBD) is recognized as an independent risk factor for thrombosis. First, we investigate whether the concentration of fibrinolysis inhibitors is increased in patients with IBD. Second, we investigate the effect of infliximab induction therapy on the hemostatic profile.This prospective study included 103 patients with IBD starting infliximab therapy and 113 healthy controls. Plasma was collected before the first infliximab infusion (wk 0) and after induction therapy (wk 14). Patients not showing a clinical response on induction were considered as primary nonresponders. Fibrinolysis inhibitors were measured by enzyme-linked immunosorbent assay. Using a clot lysis assay, the area under the curve (global marker for coagulation/fibrinolysis), 50% clot lysis time (marker for fibrinolytic capacity), and amplitude (indicator for clot formation) were determined.Patients with IBD selected for infliximab treatment have higher area under the curve (median 29 [interquartile range, 20-38]) and amplitude (0.4 [0.3-0.5]) compared with healthy controls (18 [13-24] and 0.3 [0.2-0.3], respectively, P0.001). Primary nonresponders showed a decrease neither in inflammatory markers nor in hemostatic parameters, whereas in primary responders, a decrease in inflammatory markers was associated with a decrease in both area under the curve (29 [20-38] (wk 0) to 20 [14-28] (wk 14), P0.001) and amplitude (0.4 [0.3-0.5] (wk 0) to 0.3 [0.3-0.4] (wk 14), P0.001).This is the first prospective study demonstrating that the clot lysis profile differs between patients with IBD and healthy individuals. On infliximab induction treatment, this clot lysis profile normalizes in responders suggesting that infliximab treatment is advisable for patients with IBD with an activated hemostatic profile.

Genome-wide association studies have revealed a vast amount of common loci associated to human complex diseases. Still, a large proportion of heritability remains unexplained. The extent to which rare genetic variants (RVs) are able to explain a relevant portion of the genetic heritability for complex traits leaves room for several debates and paves the way to the collection of RV databases and the development of novel analytic tools to analyze these. To date, several statistical methods have been proposed to uncover the association of RVs with complex diseases, but none of them is the clear winner in all possible scenarios of study design and assumed underlying disease model. The latter may involve differences in the distributions of effect sizes, proportions of causal variants, and ratios of protective to deleterious variants at distinct regions throughout the genome. Therefore, there is a need for robust scalable methods with acceptable overall performance in terms of power and type I error under various realistic scenarios. In this paper, we propose a novel RV association analysis strategy, which satisfies several of the desired properties that a RV analysis tool should exhibit.

Objectives: Different types of ‘-omics' data are becoming available in the post-genome era; still a single -omics assessment provides limited insights to understand the biological mechanism of complex diseases. Genomics, epigenomics and transcriptomics data provide insight into the molecular dysregulation of neoplastic diseases, among them urothelial bladder cancer (UBC). Here, we propose a detailed analytical framework necessary to achieve an adequate integration of the three sets of -omics data to ultimately identify previously hidden genetic mechanisms in UBC. Methods: We built a multi-staged framework to study possible pair-wise combinations and integrated the data in three-way relationships. SNP genotypes, CpG methylation levels and gene expression levels were determined for a total of 70 individuals with UBC and with fresh tumour tissue available. Results: We suggest two main hypothesis-based scenarios for gene regulation based on the -omics integration analysis, where DNA methylation affects gene expression and genetic variants co-regulate gene expression and DNA methylation. We identified several three-way trans-association ‘hotspots' that are found at the molecular level and that deserve further studies. Conclusions: The proposed integrative framework allowed us to identify relationships at the whole-genome level providing some new biological insights and highlighting the importance of integrating -omics data.

The molecular details of the association between the human Fyn-SH3 domain, and the fragment of 18.5-kDa myelin basic protein (MBP) spanning residues S38-S107 (denoted as xα2-peptide, murine sequence numbering), were studied in silico via docking and molecular dynamics over 50-ns trajectories. The results show that interaction between the two proteins is energetically favorable and heavily dependent on the MBP proline-rich region (P93-P98) in both aqueous and membrane environments. In aqueous conditions, the xα2-peptide/Fyn-SH3 complex adopts a "sandwich""-like structure. In the membrane context, the xα2-peptide interacts with the Fyn-SH3 domain via the proline-rich region and the β-sheets of Fyn-SH3, with the latter wrapping around the proline-rich region in a form of a clip. Moreover, the simulations corroborate prior experimental evidence of the importance of upstream segments beyond the canonical SH3-ligand. This study thus provides a more-detailed glimpse into the context-dependent interaction dynamics and importance of the β-sheets in Fyn-SH3 and proline-rich region of MBP. Proteins 2017; 85:1336-1350. © 2017 Wiley Periodicals, Inc.

The 18.5 kDa myelin basic protein (MBP), the most abundant splice isoform in adult mammalian myelin, is a multifunctional, intrinsically disordered protein involved in the development and compaction of the myelin sheath in the central nervous system. A highly conserved central segment comprises a membrane-anchoring amphipathic α-helix followed by a proline-rich segment that represents a ligand for SH3 domain-containing proteins. Here, we have determined using solution nuclear magnetic resonance spectroscopy the structure of a 36-residue peptide fragment of MBP (murine 18.5 kDa residues S72-S107, denoted the α2-peptide) comprising these two structural motifs, in association with dodecylphosphocholine (DPC) micelles. The structure was calculated using CS-ROSETTA (version 1.01) because the nuclear Overhauser effect restraints were insufficient for this protein. The experimental studies were complemented by molecular dynamics simulations of a corresponding 24-residue peptide fragment (murine 18.5 kDa residues E80-G103, denoted the MD-peptide), also in association with a DPC micelle in silico. The experimental and theoretical results agreed well with one another, despite the independence of the starting structures and analyses, both showing membrane association via the amphipathic α-helix, and a sharp bend in the vicinity of the Pro93 residue (murine 18.5 kDa sequence numbering). Overall, the conformations elucidated here show how the SH3 ligand is presented to the cytoplasm for interaction with SH3 domain-containing proteins such as Fyn and contribute to our understanding of myelin architecture at the molecular level.

In this paper we investigate a method for classifying microarray data using association rules. Associative classifiers, classification systems based on association rules, show good performance level while being easy to read and understand. This feature is especially attractive for biological data where experts can read and validate the association rules. Relevant features are selected using Support Vector Machines with Recursive Feature Elimination. These features are discretized according to their relative expression levels (upregulated, downregulated or no change) and then they are used to build an associative classifier. The proposed combination proves highly accurate for the studied microarray data collection. In addition, the classification rules discovered and employed in the classification process prove to be biologically relevant.

Simulation of proteins and membranes composed of synthetic lipids on computer clusters provides molecular information that complements experimental data. This paper describes molecular dynamics (MD) approaches to study the properties of biological membranes and proteins using the freely available GROMACS package on the C-terminal α-helical peptide of myelin basic protein (MBP). We simulated a mixed membrane – consisting of 2-dimyristoyl-sn-glycero-3-phosphocholine/1,2-dimyristoyl-sn-glycero-3-phosphoethanolamine (DMPC/DMPE), and a pure DMPC membrane, composed of 188 and 248 lipids, respectively – for 200 ns at 309K. The DMPC membrane was approximately three times more fluid compared to the DMPC/DMPE system, with the diffusion coefficients (D) being 0.0207x10-5 cm2/s and 0.0068x10-5 cm2/s, respectively. In addition, we simulated the 14-residue peptide representing the C-terminal α-helical region of murine MBP, with sequence NH2-A141YDAQGTLSKIFKL154-COOH, in both membrane systems for 200 ns. The negatively-charged N-terminal end of the peptide penetrated further into the DMPC bilayer than into the mixed DMPC/DMPE bilayer. Reduced peptide accessibility to a formal positive charge of the DMPC amine ‘N’ atom surrounded by methyl and methylene groups may be the cause [1]. The peptide lost its α-helical structure in DMPC/DMPE but not in the DMPC bilayer. These findings show that membrane composition affects MBP’s interaction with it, a phenomenon that provides insights into myelin structure – and that may eventually be relevant to understanding the pathogenesis of multiple sclerosis (MS).

Gene regulatory network (GRN) inference is an active area of research that facilitates understanding the complex interplays between biological molecules. We propose a novel framework to create such GRNs, based on Conditional Inference Forests (CIFs) as proposed by Strobl et al. Our framework consists of using ensembles of Conditional Inference Trees (CITs) and selecting an appropriate aggregation scheme for variant selection prior to network construction. We show on synthetic microarray data that taking the original implementation of CIFs with conditional permutation scheme (CIF

The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work, we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylation data in two steps. First, penalized regressions are used to build Expression-Expression (EEnet) and Expression-Genomic or Expression-Methylation (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach, Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified, respectively, 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Although the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response, and several cancer types. Importantly, we observed significant overrepresentation of cancer-related pathways including glioma, especially in the XORnet network, suggesting a nonignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors.

Genome-wide association interaction (GWAI) studies have increased in popularity. Yet to date, no standard protocol exists. In practice, any GWAI workflow involves making choices about quality control strategy, SNP filtering, linkage disequilibrium (LD) pruning, analytic tool to model or to test for genetic interactions. Each of these can have an impact on the final epistasis findings and may affect their reproducibility in follow-up analyses. Choosing an analytic tool is not straightforward, as different tools exist and current understanding about their performance is based on often very particular simulation settings. In the present study, we wish to create awareness for the impact of (minor) changes in a GWAI analysis protocol can have on final epistasis findings. In particular, we investigate the influence of marker selection and marker prioritization strategies, LD pruning and the choice of epistasis detection analytics on study results, giving rise to 8 GWAI protocols. Discussions are made in the context of the ankylosing spondylitis (AS) data obtained via the Wellcome Trust Case Control Consortium (WTCCC2). As expected, the largest impact on AS epistasis findings is caused by the choice of marker selection criterion, followed by marker coding and LD pruning. In MB-MDR, co-dominant coding of main effects is more robust to the effects of LD pruning than additive coding. We were able to reproduce previously reported epistasis involvement of HLA-B and ERAP1 in AS pathology. In addition, our results suggest involvement of MAGI3 and PARK2, responsible for cell adhesion and cellular trafficking. Gene ontology biological function enrichment analysis across the 8 considered GWAI protocols also suggested that AS could be associated to the central nervous system malfunctions, specifically, in nerve impulse propagation and in neurotransmitters metabolic processes.

We propose a minimal protocol for exhaustive genome-wide association interaction analysis that involves screening for epistasis over large-scale genomic datacombining strengths of different methods and statistical tools. The different steps of this protocol are illustrated on a real-life data application for Alzheimer's disease (AD) (2259 patients and 6017 controls from France). Particularly, in the exhaustive genome-wide epistasis screening we identified AD-associated interacting SNPs-pair from chromosome 6q11.1 (rs6455128, the KHDRBS2 gene) and 13q12.11 (rs7989332, the CRYL1 gene) (p = 0.006, corrected for multiple testing). A replication analysis in the independent AD cohort from Germany (555 patients and 824 controls) confirmed the discovered epistasis signal (p - 0.036). This signal was also supported by a meta-analysis approach in 5 independent AD cohorts that was applied in the context of epistasis for the first time. Transcriptome analysis revealed negative correlation between expression levels of KHDRBS2 and CRYL1 in both the temporal cortex (beta = -0.19, p = 0.0006) and cerebellum (beta = -0.23, p < 0.0001) brain regions. This is the first time a replicable epistasis associated with AD was identified using a hypothesis free screening approach. (C) 2014 The Authors. Published by Elsevier Inc. All rights reserved.

A mixed phospholipid-cholestrol bilayer, with cholera toxin B (CTB) units attached to the monosialotetrahexosylganglioside (GM1) binding sites in the distal leaflet, was deposited on a Au(111) electrode surface. Polarization modulation infrared reflection absorption spectroscopy (PM-IRRAS) measurements were used to characterize structural and orientational changes in this model biological membrane upon binding CTB and the application of the electrode potential. The data presented in this article show that binding cholera toxin to the membrane leads to an overall increase in the tilt angle of the fatty acid chains; however, the conformation of the bilayer remains relatively constant as indicated by the small decrease in the total number of gauche conformers of acyl tails. In addition, the bound toxin caused a significant decrease in the hydration of the ester group contained within the lipid bilayer. Furthermore, changes in the applied potential had a minimal effect on the overall structure of the membrane. In contrast, our results showed significant voltage-dependent changes in the average orientation of the protein α-helices that may correspond to the voltage-gated opening and closing of the central pore that resides within the B subunit of cholera toxin.

We have evaluated the effects of the proline homologue Aze (1) (azetidine-2-carboxylic acid) on growth of Escherichia coli strains used to over-express recombinant forms of murine myelin basic protein (rmMBP), and on the degree of misincorporation. Addition of Aze to minimal media resulted in severe diminution of growth rate, but rmMBP could still be produced and purified. Mass spectrometry indicated that a detectable proportion of the rmMBP produced had incorporated Aze instead of proline (Pro), to a maximum of three of eleven possible sites. Molecular modelling of a proline-rich region of rmMBP illustrated that the misincorporation of Aze at any site would cause a severe bend in the polypeptide chain, and that multiple Pro → Aze substitutions would completely disrupt a poly-proline type II structure that has been conjectured to be functionally significant.