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      Feeding Honeybee Colonies with Honeybee-Specific Lactic Acid Bacteria (Hbs-LAB) Does Not Affect Colony-Level Hbs-LAB Composition or Paenibacillus larvae Spore Levels, Although American Foulbrood Affected Colonies Harbor a More Diverse Hbs-LAB Community


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          The main current methods for controlling American Foulbrood (AFB) in honeybees, caused by the bacterial pathogen Paenibacillus larvae, are enforced incineration or prophylactic antibiotic treatment, neither of which is fully satisfactory. This has led to an increased interest in the natural relationships between the pathogenic and mutualistic microorganisms of the honeybee microbiome, in particular, the antagonistic effects of Honeybee-Specific Lactic Acid Bacteria (hbs-LAB) against P. larvae. We investigated whether supplemental administration of these bacteria affected P. larvae infection at colony level over an entire flowering season. Over the season, the supplements affected neither colony-level hbs-LAB composition nor naturally subclinical or clinical P. larvae spore levels. The composition of hbs-LAB in colonies was, however, more diverse in apiaries with a history of clinical AFB, although this was also unrelated to P. larvae spore levels. During the experiments, we also showed that qPCR could detect a wider range of hbs-LAB, with higher specificity and sensitivity than mass spectrometry. Honeybee colonies are complex super-organisms where social immune defenses, natural homeostatic mechanisms, and microbiome diversity and function play a major role in disease resistance. This means that observations made at the individual bee level cannot be simply extrapolated to infer similar effects at colony level. Although individual laboratory larval assays have clearly demonstrated the antagonistic effects of hbs-LAB on P. larvae infection, the results from the experiments presented here indicate that direct conversion of such practice to colony-level administration of live hbs-LAB is not effective.

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          The online version of this article (10.1007/s00248-019-01434-3) contains supplementary material, which is available to authorized users.

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          Immune pathways and defence mechanisms in honey bees Apis mellifera

          Social insects are able to mount both group-level and individual defences against pathogens. Here we focus on individual defences, by presenting a genome-wide analysis of immunity in a social insect, the honey bee Apis mellifera. We present honey bee models for each of four signalling pathways associated with immunity, identifying plausible orthologues for nearly all predicted pathway members. When compared to the sequenced Drosophila and Anopheles genomes, honey bees possess roughly one-third as many genes in 17 gene families implicated in insect immunity. We suggest that an implied reduction in immune flexibility in bees reflects either the strength of social barriers to disease, or a tendency for bees to be attacked by a limited set of highly coevolved pathogens.
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            Using observation-level random effects to model overdispersion in count data in ecology and evolution

            Overdispersion is common in models of count data in ecology and evolutionary biology, and can occur due to missing covariates, non-independent (aggregated) data, or an excess frequency of zeroes (zero-inflation). Accounting for overdispersion in such models is vital, as failing to do so can lead to biased parameter estimates, and false conclusions regarding hypotheses of interest. Observation-level random effects (OLRE), where each data point receives a unique level of a random effect that models the extra-Poisson variation present in the data, are commonly employed to cope with overdispersion in count data. However studies investigating the efficacy of observation-level random effects as a means to deal with overdispersion are scarce. Here I use simulations to show that in cases where overdispersion is caused by random extra-Poisson noise, or aggregation in the count data, observation-level random effects yield more accurate parameter estimates compared to when overdispersion is simply ignored. Conversely, OLRE fail to reduce bias in zero-inflated data, and in some cases increase bias at high levels of overdispersion. There was a positive relationship between the magnitude of overdispersion and the degree of bias in parameter estimates. Critically, the simulations reveal that failing to account for overdispersion in mixed models can erroneously inflate measures of explained variance (r 2), which may lead to researchers overestimating the predictive power of variables of interest. This work suggests use of observation-level random effects provides a simple and robust means to account for overdispersion in count data, but also that their ability to minimise bias is not uniform across all types of overdispersion and must be applied judiciously.
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              Determination of bacterial load by real-time PCR using a broad-range (universal) probe and primers set


                Author and article information

                Microb Ecol
                Microb. Ecol
                Microbial Ecology
                Springer US (New York )
                10 September 2019
                10 September 2019
                : 79
                : 3
                : 743-755
                [1 ]GRID grid.6341.0, ISNI 0000 0000 8578 2742, Department of Ecology, , Swedish University of Agricultural Sciences, ; Uppsala, Sweden
                [2 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Clinical Microbiology, Department of Translational Medicine, , Lund University, ; Malmö, Sweden
                [3 ]GRID grid.6341.0, ISNI 0000 0000 8578 2742, Swedish Species Information Centre, , Swedish University of Agricultural Sciences, ; Uppsala, Sweden
                [4 ]Clinical Microbiology, Labmedicine, Region Skåne, Lund, Sweden
                [5 ]GRID grid.4514.4, ISNI 0000 0001 0930 2361, Department of Laboratory Medicine Lund, , Lund University, ; Lund, Sweden
                [6 ]Arla Innovation Center, Aarhus, Denmark
                © The Author(s) 2019

                Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                : 12 April 2019
                : 26 August 2019
                Funded by: FundRef http://dx.doi.org/10.13039/501100001862, Svenska Forskningsrådet Formas;
                Award ID: 222-2013-423
                Award Recipient :
                Host Microbe Interactions
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                © Springer Science+Business Media, LLC, part of Springer Nature 2020

                Microbiology & Virology
                american foulbrood,apis mellifera,beneficial microbes,bifidobacterium,intestinal microbiota,lactobacillus


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