Guido Cordoni

The Acute bee paralysis virus–Kashmir bee virus–Israeli acute paralysis virus complex

Acute bee paralysis virus (ABPV), Kashmir bee virus (KBV) and Israeli acute paralysis virus (IAPV) are part of a complex of closely related viruses from the Family Dicistroviridae. These viruses have a widespread prevalence in honey bee (Apis mellifera) colonies and a predominantly sub-clinical etiology that contrasts sharply with the extremely virulent pathology encountered at elevated titres, either artificially induced or encountered naturally. These viruses are frequently implicated in honey bee colony losses, especially when the colonies are infested with the parasitic mite Varroa destructor. Here we review the historical and recent literature of this virus complex, covering history and origins; the geographic, host and tissue distribution; pathology and transmission; genetics and variation; diagnostics, and discuss these within the context of the molecular and biological similarities and differences between the viruses. We also briefly discuss three recent developments relating specifically to IAPV, concerning its association with Colony Collapse Disorder, treatment of IAPV infection with siRNA and possible honey bee resistance to IAPV.

First molecular detection of a viral pathogen in Ugandan honey bees

Ugandan honey bees (Apis mellifera L.) produce honey, and are key pollinators within commercial crops and natural ecosystems. Real-time RT-PCR was used to screen immature and adult bees collected from 63 beekeeping sites across Uganda for seven viral pathogens. No samples tested positive for Chronic bee paralysis virus, Sacbrood virus, Deformed wing virus, Acute bee paralysis virus, Apis iridescent virus or Israeli acute paralysis virus. However, Black queen cell virus (BQCV) was found in 35.6% of samples. It occurred in adults and larvae, and was most prevalent in the Western highlands, accounting for over 40% of positive results nationally.

Comparative genomics of European avian pathogenic E. Coli (APEC)

BackgroundAvian pathogenic Escherichia coli (APEC) causes colibacillosis, which results in significant economic losses to the poultry industry worldwide. However, the diversity between isolates remains poorly understood. Here, a total of 272 APEC isolates collected from the United Kingdom (UK), Italy and Germany were characterised using multiplex polymerase chain reactions (PCRs) targeting 22 equally weighted factors covering virulence genes, R-type and phylogroup. Following these analysis, 95 of the selected strains were further analysed using Whole Genome Sequencing (WGS).ResultsThe most prevalent phylogroups were B2 (47%) and A1 (22%), although there were national differences with Germany presenting group B2 (35.3%), Italy presenting group A1 (53.3%) and UK presenting group B2 (56.1%) as the most prevalent. R-type R1 was the most frequent type (55%) among APEC, but multiple R-types were also frequent (26.8%). Following compilation of all the PCR data which covered a total of 15 virulence genes, it was possible to build a similarity tree using each PCR result unweighted to produce 9 distinct groups. The average number of virulence genes was 6–8 per isolate, but no positive association was found between phylogroup and number or type of virulence genes. A total of 95 isolates representing each of these 9 groupings were genome sequenced and analysed for in silico serotype, Multilocus Sequence Typing (MLST), and antimicrobial resistance (AMR). The UK isolates showed the greatest variability in terms of serotype and MLST compared with German and Italian isolates, whereas the lowest prevalence of AMR was found for German isolates. Similarity trees were compiled using sequencing data and notably single nucleotide polymorphism data generated ten distinct geno-groups. The frequency of geno-groups across Europe comprised 26.3% belonging to Group 8 representing serogroups O2, O4, O18 and MLST types ST95, ST140, ST141, ST428, ST1618 and others, 18.9% belonging to Group 1 (serogroups O78 and MLST types ST23, ST2230), 15.8% belonging to Group 10 (serogroups O8, O45, O91, O125ab and variable MLST types), 14.7% belonging to Group 7 (serogroups O4, O24, O35, O53, O161 and MLST type ST117) and 13.7% belonging to Group 9 (serogroups O1, O16, O181 and others and MLST types ST10, ST48 and others). The other groups (2, 3, 4, 5 and 6) each contained relatively few strains.However, for some of the genogroups (e.g. groups 6 and 7) partial overlap with SNPs grouping and PCR grouping (matching PCR groups 8 (13 isolates on 22) and 1 (14 isolates on 16) were observable). However, it was not possible to obtain a clear correlation between genogroups and unweighted PCR groupings. This may be due to the genome plasticity of E. coli that enables strains to carry the same virulence factors even if the overall genotype is substantially different.ConclusionsThe conclusion to be drawn from the lack of correlations is that firstly, APEC are very diverse and secondly, it is not possible to rely on any one or more basic molecular or phenotypic tests to define APEC with clarity, reaffirming the need for whole genome analysis approaches which we describe here.This study highlights the presence of previously unreported serotypes and MLSTs for APEC in Europe. Moreover, it is a first step on a cautious reconsideration of the merits of classical identification criteria such as R typing, phylogrouping and serotyping.

Phylogenomic approaches to determine the zoonotic potential of Shiga toxin-producing Escherichia coli (STEC) isolated from Zambian dairy cattle

This study assessed the prevalence and zoonotic potential of Shiga toxin-producing Escherichia coli (STEC) sampled from 104 dairy units in the central region of Zambia and compared these with isolates from patients presenting with diarrhoea in the same region. A subset of 297 E. coli strains were sequenced allowing in silico analyses of phylo- and sero-groups. The majority of the bovine strains clustered in the B1 ‘commensal’ phylogroup (67%) and included a diverse array of serogroups. 11% (41/371) of the isolates from Zambian dairy cattle contained Shiga toxin genes (stx) while none (0/73) of the human isolates were positive. While the toxicity of a subset of these isolates was demonstrated, none of the randomly selected STEC belonged to key serogroups associated with human disease and none encoded a type 3 secretion system synonymous with typical enterohaemorrhagic strains. Positive selection for E. coli O157:H7 across the farms identified only one positive isolate again indicating this serotype is rare in these animals. In summary, while Stx-encoding E. coli strains are common in this dairy population, the majority of these strains are unlikely to cause disease in humans. However, the threat remains of the emergence of strains virulent to humans from this reservoir.

Rapid diagnosis of strangles (Streptococcus equi subspecies equi) using PCR.

Strangles is one of the most common equine infectious diseases with serious health, welfare and socio-economic impact. However, the detection of Streptococcus equi subspecies equi can be challenging and persistently infected carriers are common. Furthermore, the use of classical microbiology can result in an underestimation of the prevalence of the disease. The difficulties associated with the slow diagnosis of Strangles can result in rapid spread of the disease. Therefore, rapid and economical diagnostic tests are urgently required. Here, two multiplex assays, were developed and validated for the detection of S. equi and S. equi subspecies zooepidemicus, the most common differential diagnosis. Using 59 S. equi and 59 S. zooepidemicus strains collected from various geographical areas, the PCR tests demonstrated a sensitivity of 95% and a specificity of 98%. Furthermore, the assay can be performed directly from clinical swabs. Thus, the assays designed here provide a rapid, reliable and economical solution for the diagnosis of Strangles.

Learning implicational models of universal grammar parameters

The use of parameters in the description of natural language syntax has to balance between the need to discriminate among (sometimes subtly different) languages, which can be seen as a cross-linguistic version of Chomskys descriptive adequacy (Chomsky, 1964), and the complexity of the acquisition task that a large number of parameters would imply, which is a problem for explanatory adequacy. Here we first present a novel approach in which machine learning is used to detect hidden dependencies in a table of parameters. The result is a dependency graph in which some of the parameters can be fully predicted from others. These findings can be then subjected to linguistic analysis, which may either refute them by providing typological counter-examples of languages not included in the original dataset, dismiss them on theoretical grounds, or uphold them as tentative empirical laws worth of further study. Machine learning is also used to explore the full sets of parameters that are sufficient to distinguish one historically established language family from others. These results provide a new type of empirical evidence about the historical adequacy of parameter theories.

Machine Learning Models of Universal Grammar Parameter Dependencies

The use of parameters in the description of natural language syntax has to balance between the need to discriminate among (sometimes subtly different) languages, which can be seen as a cross-linguistic version of Chomsky’s (1964) descriptive adequacy, and the complexity of the acquisition task that a large number of parameters would imply, which is a problem for explanatory adequacy. Here we present a novel approach in which a machine learning algorithm is used to find dependencies in a table of parameters. The result is a dependency graph in which some of the parameters can be fully predicted from others. These empirical findings can be then subjected to linguistic analysis, which may either refute them by providing typological counter-examples of languages not included in the original dataset, dismiss them on theoretical grounds, or uphold them as tentative empirical laws worth of further study.