Background: Profiling the microbiome of low-biomass samples is challenging for metagenomics since these samples are prone to contain DNA from other sources (e.g. host or environment). The usual approach is sequencing short regions of the 16S rRNA gene, which fails to assign taxonomy to genus and species level. To achieve an increased taxonomic resolution, we aim to develop long-amplicon PCR-based approaches using Nanopore sequencing. We assessed two different genetic markers: the full-length 16S rRNA (~1,500 bp) and the 16S-ITS-23S region from the rrn operon (4,300 bp). Methods: We sequenced a clinical isolate of Staphylococcus pseudintermedius, two mock communities and two pools of low-biomass samples (dog skin). Nanopore sequencing was performed on MinION™ using the 1D PCR barcoding kit. Sequences were pre-processed, and data were analyzed using EPI2ME or Minimap2 with rrn database. Consensus sequences of the 16S-ITS-23S genetic marker were obtained using canu. Results: The full-length 16S rRNA and the 16S-ITS-23S region of the rrn operon were used to retrieve the microbiota composition of the samples at the genus and species level. For the Staphylococcus pseudintermedius isolate, the amplicons were assigned to the correct bacterial species in ~98% of the cases with the16S-ITS-23S genetic marker, and in ~68%, with the 16S rRNA gene when using EPI2ME. Using mock communities, we found that the full-length 16S rRNA gene represented better the abundances of a microbial community; whereas, 16S-ITS-23S obtained better resolution at the species level. Finally, we characterized low-biomass skin microbiota samples and detected species with an environmental origin. Conclusions: Both full-length 16S rRNA and the 16S-ITS-23S of the rrn operon retrieved the microbiota composition of simple and complex microbial communities, even from the low-biomass samples such as dog skin. For an increased resolution at the species level, targeting the 16S-ITS-23S of the rrn operon would be the best choice.

BackgroundThe individual, together with its environment, has been reported as the main force driving composition and structure of skin microbiota in healthy dogs. Therefore, one of the major concerns when analyzing canine skin microbiota is the likely influence of the environment. Despite the dense fur covering, certain skin diseases exhibit differential prevalence among skin sites, dog breeds, and individuals.ResultsWe have characterized the normal variability of dog skin microbiota in a well-controlled cohort of a large number of Golden-Labrador Retriever crossed dogs (N = 35) with similar ages, related genetic background, and a shared environment. We found that the individual drives the skin microbiota composition and structure followed by the skin site. The main bacterial classes inhabiting dog skin in this cohort are Gammaproteobacteria and Bacilli. We also detected bacteria associated to the environment on different dog skin sites that could be reflecting the different degrees of exposure of each skin site and each dog. Network analyses elucidated bacterial interactions within and between skin sites, especially in the chin, abdomen, axilla, and perianal region, with the highly shared interactions probably representing an anatomical, behavioral, or environmental component. When analyzing each skin site independently to assess host-specific factors, we found that temporality (season of birth and time spent in the kennel) affected all the skin sites and specially the inner pinna. The most abundant taxon driving this difference was Sphingomonas. We also found taxonomic differences among male and female dogs on the abdomen, axilla, and back.ConclusionsWe observed a large inter-individual variability and differences among skin sites. Host-specific variables, such as temporality or sex, were also shaping skin microbiota of healthy dogs, even in an environmental homogenous cohort.Electronic supplementary materialThe online version of this article (10.1186/s40168-017-0355-6) contains supplementary material, which is available to authorized users.

The aim of this study was to define the microbiota of water buffalo milk during sub-clinical and clinical mastitis, as compared to healthy status, by using high-throughput sequencing of the 16S rRNA gene. A total of 137 quarter samples were included in the experimental design: 27 samples derived from healthy, culture negative quarters, with a Somatic Cell Count (SCC) of less than 200,000 cells/ml; 27 samples from quarters with clinical mastitis; 83 samples were collected from quarters with subclinical mastitis, with a SCC number greater of 200,000 cells/ml and/or culture positive for udder pathogens, without clinical signs of mastitis. Bacterial DNA was purified and the 16S rRNA genes were individually amplified and sequenced. Significant differences were found in milk samples from healthy quarters and those with sub-clinical and clinical mastitis. The microbiota diversity of milk from healthy quarters was richer as compared to samples with sub-clinical mastitis, whose microbiota diversity was in turn richer as compared to those from clinical mastitis. The core microbiota of water buffalo milk, defined as the asset of microorganisms shared by all healthy milk samples, includes 15 genera, namely Micrococcus, Propionibacterium, 5-7N15, Solibacillus, Staphylococcus, Aerococcus, Facklamia, Trichococcus, Turicibacter, 02d06, SMB53, Clostridium, Acinetobacter, Psychrobacter and Pseudomonas. Only two genera (Acinetobacter and Pseudomonas) were present in all the samples from sub-clinical mastitis, and no genus was shared across all in clinical mastitis milk samples. The presence of mastitis was found to be related to the change in the relative abundance of genera, such as Psychrobacter, whose relative abundance decreased from 16.26% in the milk samples from healthy quarters to 3.2% in clinical mastitis. Other genera, such as SMB53 and Solibacillus, were decreased as well. Discriminant analysis presents the evidence that the microbial community of healthy and clinical mastitis could be discriminated on the background of their microbiota profiles.

Dogs present almost all their skin sites covered by hair, but canine skin disorders are more common in certain skin sites and breeds. The goal of our study is to characterize the composition and variability of the skin microbiota in healthy dogs and to evaluate the effect of the breed, the skin site, and the individual. We have analyzed eight skin sites of nine healthy dogs from three different breeds by massive sequencing of 16S rRNA gene V1–V2 hypervariable regions. The main phyla inhabiting the skin microbiota in healthy dogs are Proteobacteria, Firmicutes, Fusobacteria, Actinobacteria, and Bacteroidetes. Our results suggest that skin microbiota composition pattern is individual specific, with some dogs presenting an even representation of the main phyla and other dogs with only a major phylum. The individual is the main force driving skin microbiota composition and diversity rather than the skin site or the breed. The individual is explaining 45% of the distances among samples, whereas skin site explains 19% and breed 9%. Moreover, analysis of similarities suggests a strong dissimilarity among individuals (R = 0.79, P = 0.001) that is mainly explained by low-abundant species in each dog. Skin site also plays a role: inner pinna presents the highest diversity value, whereas perianal region presents the lowest one and the most differentiated microbiota composition.

Background: Profiling the microbiome of low-biomass samples is challenging for metagenomics since these samples often contain DNA from other sources, such as the host or the environment. The usual approach is sequencing specific hypervariable regions of the 16S rRNA gene, which fails to assign taxonomy to genus and species level. Here, we aim to assess long-amplicon PCR-based approaches for assigning taxonomy at the genus and species level. We use Nanopore sequencing with two different markers: full-length 16S rRNA (~1,500 bp) and the whole rrn operon (16S rRNA–ITS–23S rRNA; 4,500 bp). Methods: We sequenced a clinical isolate of Staphylococcus pseudintermedius, two mock communities (HM-783D, Bei Resources; D6306, ZymoBIOMICS™) and two pools of low-biomass samples (dog skin from either the chin or dorsal back), using the MinION™ sequencer 1D PCR barcoding kit. Sequences were pre-processed, and data were analyzed using the WIMP workflow on EPI2ME or Minimap2 software with rrn database. Results: The full-length 16S rRNA and the rrn operon were used to retrieve the microbiota composition at the genus and species level from the bacterial isolate, mock communities and complex skin samples. For the Staphylococcus pseudintermedius isolate, when using EPI2ME, the amplicons were assigned to the correct bacterial species in ~98% of the cases with the rrn operon marker, and in ~68% of the cases with the 16S rRNA gene. In both skin microbiota samples, we detected many species with an environmental origin. In chin, we found different Pseudomonas species in high abundance, whereas in dorsal skin there were more taxa with lower abundances. Conclusions: Both full-length 16S rRNA and the rrn operon retrieved the microbiota composition of simple and complex microbial communities, even from the low-biomass samples such as dog skin. For an increased resolution at the species level, using the rrn operon would be the best choice.

Background Long-read sequencing in metagenomics facilitates the assembly of complete genomes out of complex microbial communities. These genomes include essential biologic information such as the ribosomal genes or the mobile genetic elements, which are usually missed with short-reads. We applied long-read metagenomics with Nanopore sequencing to retrieve high-quality metagenome-assembled genomes (HQ MAGs) from a dog fecal sample. Results We used nanopore long-read metagenomics and frameshift aware correction on a canine fecal sample and retrieved eight single-contig HQ MAGs, which were > 90% complete with < 5% contamination, and contained most ribosomal genes and tRNAs. At the technical level, we demonstrated that a high-molecular-weight DNA extraction improved the metagenomics assembly contiguity, the recovery of the rRNA operons, and the retrieval of longer and circular contigs that are potential HQ MAGs. These HQ MAGs corresponded to Succinivibrio, Sutterella, Prevotellamassilia, Phascolarctobacterium, Catenibacterium, Blautia, and Enterococcus genera. Linking our results to previous gastrointestinal microbiome reports (metagenome or 16S rRNA-based), we found that some bacterial species on the gastrointestinal tract seem to be more canid-specific –Succinivibrio, Prevotellamassilia, Phascolarctobacterium, Blautia_A sp900541345–, whereas others are more broadly distributed among animal and human microbiomes –Sutterella, Catenibacterium, Enterococcus, and Blautia sp003287895. Sutterella HQ MAG is potentially the first reported genome assembly for Sutterella stercoricanis, as assigned by 16S rRNA gene similarity. Moreover, we show that long reads are essential to detect mobilome functions, usually missed in short-read MAGs. Conclusions We recovered eight single-contig HQ MAGs from canine feces of a healthy dog with nanopore long-reads. We also retrieved relevant biological insights from these specific bacterial species previously missed in public databases, such as complete ribosomal operons and mobilome functions. The high-molecular-weight DNA extraction improved the assembly’s contiguity, whereas the high-accuracy basecalling, the raw read error correction, the assembly polishing, and the frameshift correction reduced the insertion and deletion errors. Both experimental and analytical steps ensured the retrieval of complete bacterial genomes.

Antimicrobials have been used in a prophylactic way to decrease the incidence of digestive disorders during the piglet post-weaning period. Nowadays, it is urgent to reduce their consumption in livestock to address the problem of antimicrobial resistance. In this study, the effect of a product on piglet microbiota has been investigated as an alternative to antimicrobials. Three groups of ten post-weaning pigs were sampled at 0, 15 and 30 days one week post-weaning; the control, antibiotic and feed additive group received a standard post-weaning diet without antibiotics or additives, the same diet as the control group but with amoxicillin and colistin sulphate and the same diet as the control group but with a feed additive (Sanacore-EN, Nutriad International N.V.), respectively. The total DNA extracted from faeces was used to amplify the 16S RNA gene for massive sequencing under manufacturer’s conditions. Sequencing data was quality filtered and analyzed using QIIME software and suitable statistical methods. In general terms, age modifies significantly the microbiota of the piglets. Thus, the oldest the animal, the highest bacterial diversity observed for the control and the feed additive groups. However, this diversity was very similar in the antibiotic group throughout the trial. Interestingly, a clear increase in abundance of Bacillus and Lactobacillus spp was detected within the feed additive group versus the antibiotic and control groups. In conclusion, the feed additive group had a positive effect in the endogenous microbiota of post-weaning pigs increasing both, the diversity of bacterial families and the abundance of lactic acid bacteria during the post-weaning period.

The availability of long-read technologies, like Oxford Nanopore Technologies, provides the opportunity to sequence longer fragments of the fungal ribosomal operon, up to 6 Kb (18S-ITS1-5.8S-ITS2-28S) and to improve the taxonomy assignment of the communities up to species level and in real-time. We assess the applicability for taxonomic assignment of amplicons targeting a 3.5 Kb region (V3 18S-ITS1-5.8S-ITS2-28S D2) and a 6 Kb region (V1 18S-ITS1-5.8S-ITS2-28S D12) with the What’s in my pot (WIMP) classifier. We used the ZymoBIOMICSTM mock community and different microbiological fungal cultures as positive controls. Long amplicon sequencing correctly identified Saccharomyces cerevisiae and Cryptococcus neoformans from the mock community and Malassezia pachydermatis, Microsporum canis and Aspergillus fumigatus from the microbiological cultures. Besides, we identified Rhodotorula graminis in a culture mislabelled as Candida spp. We applied the same approach to external otitis in dogs. Malassezia was the dominant fungal genus in dogs’ ear skin, whereas Ma. pachydermatis was the main species in the healthy sample. Conversely, we identified a higher representation of Ma. globosa and Ma. sympodialis in otitis affected samples. We demonstrate the suitability of long ribosomal amplicons to characterize the fungal community of complex samples, either healthy or with clinical signs of infection.