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.

The accurate in silico simulation of metagenomic datasets is of great importance for benchmarking bioinformatics tools as well as for experimental design. Users are dependant on large-scale simulation to not only design experiments and new projects but also for accurate estimation of computational needs within a project. Unfortunately, most current read simulators are either not suited for metagenomics, out of date or relatively poorly documented. In this article, we describe InSilicoSeq, a software package to simulate metagenomic Illumina sequencing data. InsilicoSeq has a simple command-line interface and extensive documentation. InSilicoSeq is implemented in Python and capable of simulating realistic Illumina (meta) genomic data in a parallel fashion with sensible default parameters. Source code and documentation are available under the MIT license at https://github.com/HadrienG/InSilicoSeq and https://insilicoseq.readthedocs.io/. Supplementary data are available at Bioinformatics online.

We have developed a specialized database, HBVdb (http://hbvdb.ibcp.fr), allowing the researchers to investigate the genetic variability of Hepatitis B Virus (HBV) and viral resistance to treatment. HBV is a major health problem worldwide with more than 350 million individuals being chronically infected. HBV is an enveloped DNA virus that replicates by reverse transcription of an RNA intermediate. HBV genome is optimized, being circular and encoding four overlapping reading frames. Indeed, each nucleotide of the genome takes part in the coding of at least one protein. However, HBV shows some genome variability leading to at least eight different genotypes and recombinant forms. The main drugs used to treat infected patients are nucleos(t)ides analogs (reverse transcriptase inhibitors). Unfortunately, HBV mutants resistant to these drugs may be selected and be responsible for treatment failure. HBVdb contains a collection of computer-annotated sequences based on manually annotated reference genomes. The database can be accessed through a web interface that allows static and dynamic queries and offers integrated generic sequence analysis tools and specialized analysis tools (e.g. annotation, genotyping, drug resistance profiling).

Background & Aims The development of vaccines and other strategies to prevent hepatitis C virus (HCV) infection is limited by rapid viral evasion. HCV entry is the first step of infection; this process involves several viral and host factors and is targeted by host-neutralizing responses. Although the roles of host factors in HCV entry have been well characterized, their involvement in evasion of immune responses is poorly understood. We used acute infection of liver graft as a model to investigate the molecular mechanisms of viral evasion. Methods We studied factors that contribute to evasion of host immune responses using patient-derived antibodies, HCV pseudoparticles, and cell culture–derived HCV that express viral envelopes from patients who have undergone liver transplantation. These viruses were used to infect hepatoma cell lines that express different levels of HCV entry factors. Results By using reverse genetic analyses, we identified altered use of host-cell entry factors as a mechanism by which HCV evades host immune responses. Mutations that alter use of the CD81 receptor also allowed the virus to escape neutralizing antibodies. Kinetic studies showed that these mutations affect virus–antibody interactions during postbinding steps of the HCV entry process. Functional studies with a large panel of patient-derived antibodies showed that this mechanism mediates viral escape, leading to persistent infection in general. Conclusions We identified a mechanism by which HCV evades host immune responses, in which use of cell entry factors evolves with escape from neutralizing antibodies. These findings advance our understanding of the pathogenesis of HCV infection and might be used to develop antiviral strategies and vaccines.

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.