Comparison of the influenza virus-specific effector and memory B-cell responses to immunization of children and adults with live attenuated or inactivated influenza virus vaccines

Cellular immune responses to influenza virus infection and influenza virus vaccination have not been rigorously characterized. We quantified the effector and memory B-cell responses in children and adults after administration of either live attenuated (LAIV) or inactivated (TIV) influenza virus vaccines and compared these to antibody responses. Peripheral blood mononuclear cells were collected at days 0, 7 to 12, and 27 to 42 after immunization of younger children (6 months to 4 years old), older children (5 to 9 years old), and adults. Influenza virus-specific effector immunoglobulin A (IgA) and IgG circulating antibody-secreting cells (ASC) and stimulated memory B cells were detected using an enzyme-linked immunospot assay. Circulating influenza virus-specific IgG and IgA ASC were detected 7 to 12 days after TIV and after LAIV immunization. Seventy-nine percent or more of adults and older children had demonstrable IgG ASC responses, while IgA ASC responses were detected in 29 to 53% of the subjects. The IgG ASC response rate to LAIV immunization in adults was significantly higher than the response rate measured by standard serum antibody assays (26.3% and 15.8% by neutralization and hemagglutination inhibition assays, respectively). IgG ASC and serum antibody responses were relatively low in the younger children compared to older children and adults. TIV, but not LAIV, significantly increased the percentage of circulating influenza virus-specific memory B cells detected at 27 to 42 days after immunization in children and adults. In conclusion, although both influenza vaccines are effective, we found significant differences in the B-cell and antibody responses elicited after LAIV or TIV immunization in adults and older children and between young children and older age groups.     

Modelling Recolonization of Second-Growth Forest Stands by the North American Red Squirrel Tamiasciurus hudsonicus

In this paper, we present a model for source–sink population dynamics where the locations of source and sink habitats change over time. We do this in the context of the population dynamics of the North American red squirrel, Tamiasciurus hudsonicus, within a forest environment subject to harvesting and regrowth. Harvested patches of forest are initially sinks, then eventually become source habitat again as the forest regrows. At the same time, each harvested patch is gradually recolonized by squirrels from other forest patches. We are interested in the interaction of forest harvesting dynamics with squirrel population dynamics. This depends on the harvesting schedule, and on the choices squirrels make when deciding whether to settle in a mature forest patch or in a recently harvested patch. We find that the time it takes for a second-growth forest patch to be recolonized at the mature forest level is longer than the time required for the habitat quality to be restored to the mature forest level. We also notice that recolonization pressure decreases squirrel populations in neighbouring patches. The connectivity between forest patches and the cutting schedule used also affect the time course of recolonization and steady-state population levels.     

Emerging threats and persistent conservation challenges for freshwater biodiversity

In the 12 years since Dudgeon et al. (2006) reviewed major pressures on freshwater ecosystems, the biodiversity crisis in the world's lakes, reservoirs, rivers, streams and wetlands has deepened. While lakes, reservoirs and rivers cover only 2.3% of the Earth's surface, these ecosystems host at least 9.5% of the Earth's described animal species. Furthermore, using the World Wide Fund for Nature's Living Planet Index, freshwater population declines (83% between 1970 and 2014) continue to outpace contemporaneous declines in marine or terrestrial systems. The Anthropocene has brought multiple new and varied threats that disproportionately impact freshwater systems. We document 12 emerging threats to freshwater biodiversity that are either entirely new since 2006 or have since intensified: (i) changing climates; (ii) e‐commerce and invasions; (iii) infectious diseases; (iv) harmful algal blooms; (v) expanding hydropower; (vi) emerging contaminants; (vii) engineered nanomaterials; (viii) microplastic pollution; (ix) light and noise; (x) freshwater salinisation; (xi) declining calcium; and (xii) cumulative stressors. Effects are evidenced for amphibians, fishes, invertebrates, microbes, plants, turtles and waterbirds, with potential for ecosystem‐level changes through bottom‐up and top‐down processes. In our highly uncertain future, the net effects of these threats raise serious concerns for freshwater ecosystems. However, we also highlight opportunities for conservation gains as a result of novel management tools (e.g. environmental flows, environmental DNA) and specific conservation‐oriented actions (e.g. dam removal, habitat protection policies, managed relocation of species) that have been met with varying levels of success. Moving forward, we advocate hybrid approaches that manage fresh waters as crucial ecosystems for human life support as well as essential hotspots of biodiversity and ecological function. Efforts to reverse global trends in freshwater degradation now depend on bridging an immense gap between the aspirations of conservation biologists and the accelerating rate of species endangerment.     

The focal complex of epithelial cells provides a signalling platform for interleukin‐8 induction in response to bacterial pathogens

Bacterial pathogens can induce an inflammatory response from epithelial tissues due to secretion of the pro‐inflammatory chemokine interleukin‐8 (IL‐8). Many bacterial pathogens manipulate components of the focal complex (FC) to induce signalling events in host cells. We examined the interaction of several bacterial pathogens with host cells, including Campylobacter jejuni, to determine if the FC is required for induction of chemokine signalling in response to bacterial pathogens. Our data indicate that secretion of IL‐8 is triggered by C. jejuni, Helicobacter pylori and Salmonella enterica serovar Typhimurium in response to engagement of β₁ integrins. Additionally, we found that the secretion of IL‐8 from C. jejuni infected epithelial cells requires FAK, Src and paxillin, which in turn are necessary for Erk 1/2 recruitment and activation. Targeting the FC component paxillin with siRNA prevented IL‐8 secretion from cells infected with several bacterial pathogens, including C. jejuni, Helicobacter pylori, Salmonella enterica serovar Typhimurium, Staphylococcus aureus, Pseudomonas aeruginosa, and Vibrio parahaemolyticus. Our findings indicate that maximal IL‐8 secretion from epithelial cells in response to bacterial infection is dependent on the FC. Based on the commonality of the host response to bacterial pathogens, we propose that the FC is a signalling platform for an epithelial cell response to pathogenic organisms.     

Inferring mechanisms of copy number change from haplotype structures at the human DEFA1A3 locus

Background

The determination of structural haplotypes at copy number variable regions can indicate the mechanisms responsible for changes in copy number, as well as explain the relationship between gene copy number and expression. However, obtaining spatial information at regions displaying extensive copy number variation, such as the DEFA1A3 locus, is complex, because of the difficulty in the phasing and assembly of these regions. The DEFA1A3 locus is intriguing in that it falls within a region of high linkage disequilibrium, despite its high variability in copy number (n = 3–16); hence, the mechanisms responsible for changes in copy number at this locus are unclear.

Results

In this study, a region flanking the DEFA1A3 locus was sequenced across 120 independent haplotypes with European ancestry, identifying five common classes of DEFA1A3 haplotype. Assigning DEFA1A3 class to haplotypes within the 1000 Genomes project highlights a significant difference in DEFA1A3 class frequencies between populations with different ancestry. The features of each DEFA1A3 class, for example, the associated DEFA1A3 copy numbers, were initially assessed in a European cohort (n = 599) and replicated in the 1000 Genomes samples, showing within-class similarity, but between-class and between-population differences in the features of the DEFA1A3 locus. Emulsion haplotype fusion-PCR was used to generate 61 structural haplotypes at the DEFA1A3 locus, showing a high within-class similarity in structure.

Conclusions

Structural haplotypes across the DEFA1A3 locus indicate that intra-allelic rearrangement is the predominant mechanism responsible for changes in DEFA1A3 copy number, explaining the conservation of linkage disequilibrium across the locus. The identification of common structural haplotypes at the DEFA1A3 locus could aid studies into how DEFA1A3 copy number influences expression, which is currently unclear.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-614) contains supplementary material, which is available to authorized users.     

Comparative genomics of enterohemorrhagic Escherichia coli O145:H28 demonstrates a common evolutionary lineage with Escherichia coli O157:H7

Background

Although serotype O157:H7 is the predominant enterohemorrhagic Escherichia coli (EHEC), outbreaks of non-O157 EHEC that cause severe foodborne illness, including hemolytic uremic syndrome have increased worldwide. In fact, non-O157 serotypes are now estimated to cause over half of all the Shiga toxin-producing Escherichia coli (STEC) cases, and outbreaks of non-O157 EHEC infections are frequently associated with serotypes O26, O45, O103, O111, O121, and O145. Currently, there are no complete genomes for O145 in public databases.

Results

We determined the complete genome sequences of two O145 strains (EcO145), one linked to a US lettuce-associated outbreak (RM13514) and one to a Belgium ice-cream-associated outbreak (RM13516). Both strains contain one chromosome and two large plasmids, with genome sizes of 5,737,294 bp for RM13514 and 5,559,008 bp for RM13516. Comparative analysis of the two EcO145 genomes revealed a large core (5,173 genes) and a considerable amount of strain-specific genes. Additionally, the two EcO145 genomes display distinct chromosomal architecture, virulence gene profile, phylogenetic origin of Stx2a prophage, and methylation profile (methylome). Comparative analysis of EcO145 genomes to other completely sequenced STEC and other E. coli and Shigella genomes revealed that, unlike any other known non-O157 EHEC strain, EcO145 ascended from a common lineage with EcO157/EcO55. This evolutionary relationship was further supported by the pangenome analysis of the 10 EHEC str ains. Of the 4,192 EHEC core genes, EcO145 shares more genes with EcO157 than with the any other non-O157 EHEC strains.

Conclusions

Our data provide evidence that EcO145 and EcO157 evolved from a common lineage, but ultimately each serotype evolves via a lineage-independent nature to EHEC by acquisition of the core set of EHEC virulence factors, including the genes encoding Shiga toxin and the large virulence plasmid. The large variation between the two EcO145 genomes suggests a distinctive evolutionary path between the two outbreak strains. The distinct methylome between the two EcO145 strains is likely due to the presence of a BsuBI/PstI methyltransferase gene cassette in the Stx2a prophage of the strain RM13514, suggesting a role of horizontal gene transfer-mediated epigenetic alteration in the evolution of individual EHEC strains.     

Sensitive and specific single-molecule sequencing of 5-hydroxymethylcytosine

We describe strand-specific, base-resolution detection of 5-hydroxymethylcytosine (5-hmC) in genomic DNA with single-molecule sensitivity, combining a bioorthogonal, selective chemical labeling method of 5-hmC with single-molecule, real-time (SMRT) DNA sequencing. The chemical labeling not only allows affinity enrichment of 5-hmC-containing DNA fragments but also enhances the kinetic signal of 5-hmC during SMRT sequencing. We applied the approach to sequence 5-hmC in a genomic DNA sample with high confidence.     

Reinforcer devaluation by extinction depends on the food restriction protocol

A common feature of reinforcer devaluation studies is that new learning induces the devaluation. The present study used extinction to induce new learning about the conditioned reinforcer in a heterogeneous chain schedule. Rats pressed a lever in a heterogeneous chain schedule to produce a conditioned reinforcer (light) associated with the opportunity to obtain an unconditioned reinforcer (food) by pulling a chain. The density of food reinforcement correlated with the conditioned reinforcer was varied in a comparison of continuous and variable-ratio reinforcement schedules of chain pulling; this had no noticeable effect on conditioned reinforcer devaluation produced by extinction of chain pulling. In contrast, how rats were deprived appeared to matter very much. Restricting meal duration to 1h daily produced more lever pressing during baseline training and a greater reductive effect of devaluation on lever pressing than restricting body weight to 80% of a control rat's weight, which eliminated the devaluation effect. Further analysis suggested that meal-duration restriction may have produced devaluation effects because it was more effective than weight restriction in reducing rats' body weights. Our results exposed an important limitation on the devaluation of conditioned reinforcers: slight differences in food restriction, using two commonly employed food-restriction procedures, can produce completely different interpretations of reinforcer devaluation while leaving reinforcer-based learning intact.     

Considerations for importing live genetically modified mice from academic laboratories

Genetically modified mice have been an invaluable tool for the study of gene function in an intact biological system. Researchers frequently obtain genetically modified mice from other academic institutions. This form of collaboration between laboratories comes with a unique set of challenges, and a clear set of guidelines for navigating the process has yet to be defined. The author provides suggestions for how to initiate an exchange of animal resources and steps for ensuring a successful collaboration. Both parties should be clear about their expectations. The importing lab should prepare in advance for potential animal health considerations and breeding and colony management strategies prior to importation. The number, gender, age and genotype of the imported animals should be confirmed as soon as possible by the importing lab. It is in the best interest of all parties to be courteous, forthright and thorough when sharing animal resources so that everyone can benefit from the resulting research.