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Additional file 1.

The pairwise amino acid identity matrix of CuPV-1 with other iflaviruses. (PDF 60 kb)

Conserved domains corresponding to 1C(VP3) and 1D(VP1) in CuPV-1. (PDF 489 kb)

Mosquitoes carry a wide variety of viruses that can cause vector-borne infectious diseases and affect both human and veterinary public health. Although Mozambique can be considered a hot spot for emerging infectious diseases due to factors such as a rich vector population and a close vector/human/wildlife interface, the viral flora in mosquitoes have not previously been investigated. In this study, viral metagenomics was employed to analyze the viral communities in Culex and Mansonia mosquitoes in the Zambezia province of Mozambique. Among the 1.7 and 2.6 million sequences produced from the Culex and Mansonia samples, respectively, 3269 and 983 reads were classified as viral sequences. Viruses belonging to the Flaviviridae, Rhabdoviridae and Iflaviridae families were detected, and different unclassified single- and double-stranded RNA viruses were also identified. A near complete genome of a flavivirus, tentatively named Cuacua virus, was obtained from the Mansonia mosquitoes. Phylogenetic analysis of this flavivirus, using the NS5 amino acid sequence, showed that it grouped with 'insect-specific' viruses and was most closely related to Nakiwogo virus previously identified in Uganda. Both mosquito genera had viral sequences related to Rhabdoviruses, and these were most closely related to Culex tritaeniorhynchus rhabdovirus (CTRV). The results from this study suggest that several viruses specific for insects belonging to, for example, the Flaviviridae and Rhabdoviridae families, as well as a number of unclassified RNA viruses, are present in mosquitoes in Mozambique.

Neonatal porcine diarrhoea of uncertain aetiology has been reported from a number of European countries. The aim of the present study was to use viral metagenomics to examine a potential viral involvement in this diarrhoea and to describe the intestinal virome with focus on eukaryotic viruses. Samples from the distal jejunum of 50 diarrhoeic and 19 healthy piglets from 10 affected herds were analysed. The viral fraction of the samples was isolated and nucleic acids (RNA and DNA fractions) were subjected to sequence independent amplification. Samples from diarrhoeic piglets from the same herds were pooled whereas samples from healthy piglets were analysed individually. In total, 29 clinical samples, plus two negative controls and one positive control consisting of a mock metagenome were sequenced using the Ion Torrent platform. The resulting sequence data was subjected to taxonomic classification using Kraken, Diamond and HMMER. In the healthy specimens, eight different mammalian virus families were detected (Adenoviridae, Anelloviridae, Astroviridae, Caliciviridae, Circoviridae, Parvoviridae, Picornaviridae, and Reoviridae) compared to four in the pooled diarrhoeic samples (Anelloviridae, Circoviridae, Picornaviridae, and Reoviridae). It was not possible to associate a particular virus family with the investigated diarrhoea. In conclusion, this study does not support the hypothesis that the investigated diarrhoea was caused by known mammalian viruses. The results do, however, indicate that known mammalian viruses were present in the intestine as early as 24-48 hours after birth, indicating immediate infection post-partum or possibly transplacental infection.

We developed HBVdb (http://hbvdb.ibcp.fr) to allow researchers to investigate the geneticcharacteristics and variability of the HBV sequences and viral resistance to treatment. HBVdb contains a collection of computer-annotated sequences based on manually annotated reference genomes. The automatic annotation procedure ensures standardized nomenclature for all HBV entries across the database. HBVdb is accessible through a dedicated website integrating generic and specialized analysis tools (annotation, genotyping, resistance profile detection), and pre- computed datasets. The HBV polymerase is the main target of anti-HBV drugs, nucleos(t)ides analogues (NA), which inhibit the activity of reverse transcriptase (RT), but NA resistance mutations appeared. Nevertheless, another enzymatic domain could be a potential drug target: RNase H domain, linked to RT, and involved in degradation of the RNA during the reverse transcription. To overcome the lack of experimental solved structure, thanks to sequences analysis from HBVdb, we built an homology model of RNase H, which helped to define the features of this type 1 RNase H. Finally, to confirm assumptions from this model and to put it in a more global context, we built an extensive HBV polymerase model, which includes the RT and RNase H domains, and helps to answer the question about the existence of connection domain linking them. We performed analyses on this model, regarding the interactions between the RT catalytic site and the Tenofovir, mapping known resistance mutations and the most variables positions of the HBV polymerase, Nous avons développé la base HBVdb (http://hbvdb.ibcp.fr) pour permettre aux chercheurs d'étudier les caractéristiques génétiques et la variabilité des séquences du virus de l'hépatite B (VHB), ainsi que la résistance virale aux traitements. HBVdb contient une collection de séquences annotées automatiquement sur la base de génomes de référence annotés manuellement, ce qui assure une nomenclature normalisée pour toutes les entrées de la base. HBVdb est accessible via un site Web dédié avec des outils d'analyses génériques et spécialisés (annotation, génotypage, détection de profils de résistance), et des jeux de données pré-calculés. La polymérase du VHB est la principale cible des traitements anti-VHB. Les analogues de nucléos(t)ides (NA) inhibent l'activité de la transcriptase inverse (RT), mais il existe des mutations de résistance aux NA. Cependant, un autre domaine enzymatique pourrait être une cible potentielle : la RNase H, liée au domaine RT, permettant la dégradation de l'ARN durant la transcription inverse. Pour pallier l'absence d'une structure expérimentale résolue, et grâce à l'analyse de séquences à partir de HBVdb, nous avons construit le modèle par homologie de la RNase H, qui a permis de définir les caractéristiques de cette RNase H de type 1. Enfin pour vérifier des hypothèses émises à partir de ce modèle, et pour le placer dans son contexte, nous avons construit un modèle plus étendu de la polymérase du VHB, qui comprend la les domaines RT et RNase H, et contribue à répondre à la question sur l'existence d'un domaine de connexion les reliant. Nous avons utilisé notre modèle pour analyser les interactions entre le site catalytique de la RT et le ténofovir

As pollinators, bees are cornerstones for terrestrial ecosystem stability and key components in agricultural productivity. All animals, including bees, are associated with a diverse community of microbes, commonly referred to as the microbiome. The bee microbiome is likely to be a crucial factor affecting host health. However, with the exception of a few pathogens, the impacts of most members of the bee microbiome on host health are poorly understood. Further, the evolutionary and ecological forces that shape and change the microbiome are unclear. Here, we discuss recent progress in our understanding of the bee microbiome, and we present challenges associated with its investigation. We conclude that global coordination of research efforts is needed to fully understand the complex and highly dynamic nature of the interplay between the bee microbiome, its host, and the environment. High-throughput sequencing technologies are ideal for exploring complex biological systems, including host-microbe interactions. To maximize their value and to improve assessment of the factors affecting bee health, sequence data should be archived, curated, and analyzed in ways that promote the synthesis of different studies. To this end, the BeeBiome consortium aims to develop an online database which would provide reference sequences, archive metadata, and host analytical resources. The goal would be to support applied and fundamental research on bees and their associated microbes and to provide a collaborative framework for sharing primary data from different research programs, thus furthering our understanding of the bee microbiome and its impact on pollinator health., mBio, 7 (2), ISSN:2150-7511, ISSN:2161-2129