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Over a billion people are infected withToxocara canisorT. cati, the roundworms of dogs and cats. Historically,T. canishas been considered the main responsible of human toxocarosis but as serodiagnosis cannot discriminate the two species, this remains unresolved. We assessed the migratory pattern ofT. catiandT. canisin a pig model and found them to be equally infective. Overall, they had a similar migration pattern reaching multiple organs and tissues, including mesenteric lymph nodes, liver, lungs and diaphragm. We recovered larvae of both species in the brain, suggesting thatT. catialso can cause neurological toxocarosis in humans. Both species induced systemic eosinophilia and histopathological changes in lungs, livers and mesenteric lymph nodes. This study emphasizes the importance ofT. catias a zoonotic agent and the need to develop diagnostic methods that can differentiate between sources of infection in humans.

Metagenomics is increasingly used to describe microbial communities in biological specimens. Ideally, the steps involved in the processing of the biological specimens should not change the microbiome composition in a way that it could lead to false interpretations of inferred microbial community composition. Common steps in sample preparation include sample collection, storage, DNA isolation, library preparation, and DNA sequencing. Here we assess the effect of three library preparation kits and two DNA sequencing platforms. Of the library preparation kits, one involved a polymerase chain reaction (PCR) step (Nextera), and two were PCR-free (NEXTflex and KAPA). We sequenced the libraries on Illumina HiSeq and NextSeq platforms. As example microbiomes, we assessed two pig fecal samples and two sewage samples of which aliquots were stored at different storage conditions (immediate processing and storage at −80°C). All DNA isolations were performed in duplicate, totaling 80 samples excluding controls. We found that both library preparation and sequencing platform had systematic effects on the inferred microbial community composition. The different sequencing platforms introduced more variation than library preparation and freezing the samples. The results highlight that all sample processing steps need to be considered when comparing studies. Standardization of sample processing is key to generate comparable data within a study, and comparisons of differently generated data, such as in a meta-analysis, should be performed cautiously.ImportancePrevious research has reported effects of sample storage conditions and DNA isolation procedures on metagenomics-based microbiome composition; however, the effect of library preparation and DNA sequencing in metagenomics has not been thoroughly assessed. Here, we provide evidence that library preparation and sequencing platform introduce systematic biases in the metagenomic-based characterization of microbial communities. These findings suggest that library preparation and sequencing are important parameters to keep consistent when aiming to detect small changes in microbiome community structure. Overall, we recommend that all samples in a microbiome study are processed in the same way to limit unwanted variations that could lead to false conclusions. Furthermore, if we are to obtain a more holistic insight from microbiome data generated around the world, we will need to provide more detailed sample metadata, including information about the different sample processing procedures, together with the DNA sequencing data at the public repositories.

Storage of biological specimens is crucial in the life and medical sciences. Storage conditions for samples can be different for a number of reasons, and it is unclear what effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer, −80°C; freezer, −20°C; refrigerator, 5°C; room temperature, 22°C) and storage times (immediate sample processing, 0 h; next day, 16 h; over weekend, 64 h; longer term, 4, 8, and 12 months) as well as repeated sample freezing and thawing (2 to 4 freeze-thaw cycles). We examined two different pig feces and sewage samples, unspiked and spiked with a mock community, in triplicate, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were, in some situations, similarly affected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We provide recommendations for sample storage and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings. IMPORTANCE Previous research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and found significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a data set that could also be used for benchmarking algorithms to identify and correct for unwanted batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing be published together with DNA sequencing data at the International Nucleotide Sequence Database Collaboration (ENA/EBI, NCBI, DDBJ) or other repositories.

Storage of biological specimens is crucial in the life and medical sciences. The storage conditions for samples can be different for a number of reasons, and it is unclear which effect this can have on the inferred microbiome composition in metagenomics analyses. Here, we assess the effect of common storage temperatures (deep freezer: -80°C, freezer: -20°C, fridge: 5°C, room temperature: 22°C) and storage times, (immediate sample processing: 0h, next day: 16h, over weekend: 64h, and longer term: 4, 8, 12 months), as well as repeated sample freezing and thawing (2-4 freeze-thaw cycles). We examine two different pig feces and sewage samples, unspiked and spiked with a mock community, and in triplicates, respectively, amounting to a total of 438 samples (777 Gbp; 5.1 billion reads). Storage conditions had a significant and systematic effect on the taxonomic and functional composition of microbiomes. Distinct microbial taxa and antimicrobial resistance classes were in some situations similarly effected across samples, while others were not, suggesting an impact of individual inherent sample characteristics. With an increasing number of freeze-thaw cycles, an increasing abundance of Firmicutes, Actinobacteria, and eukaryotic microorganisms was observed. We include recommendations for sample storage, and strongly suggest including more detailed information in the metadata together with the DNA sequencing data in public repositories to better facilitate meta-analyses and reproducibility of findings.IMPORTANCEPrevious research has reported effects of DNA isolation, library preparation, and sequencing technology on metagenomics-based microbiome composition; however, the effect of biospecimen storage conditions has not been thoroughly assessed. We examined the effect of common sample storage conditions on metagenomics-based microbiome composition and find significant and, in part, systematic effects. Repeated freeze-thaw cycles could be used to improve the detection of microorganisms with more rigid cell walls, including parasites. We provide a dataset that could also be used for benchmarking algorithms to identify and correct for batch effects. Overall, the findings suggest that all samples of a microbiome study should be stored in the same way. Furthermore, there is a need to mandate more detailed information about sample storage and processing published together with the DNA sequencing data at INSDC (ENA/EBI, NCBI, DDBJ) or other repositories.

An inter-laboratory proficiency test was organized to assess the ability of participants to perform shotgun metagenomic sequencing of cold smoked salmon, experimentally spiked with a mock community composed of six bacteria, one parasite, one yeast, one DNA, and two RNA viruses. Each participant applied its in-house wet-lab workflow(s) to obtain the metagenomic dataset(s), which were then collected and analyzed using MG-RAST. A total of 27 datasets were analyzed. Sample pre-processing, DNA extraction protocol, library preparation kit, and sequencing platform, influenced the abundance of specific microorganisms of the mock community. Our results highlight that despite differences in wet-lab protocols, the reads corresponding to the mock community members spiked in the cold smoked salmon, were both detected and quantified in terms of relative abundance, in the metagenomic datasets, proving the suitability of shotgun metagenomic sequencing as a genomic tool to detect microorganisms belonging to different domains in the same food matrix. The implementation of standardized wet-lab protocols would highly facilitate the comparability of shotgun metagenomic sequencing dataset across laboratories and sectors. Moreover, there is a need for clearly defining a sequencing reads threshold, to consider pathogens as detected or undetected in a food sample. Published version

Microbial metagenomics utilising next generation sequencing is a powerful experimental approach enabling detailed and potentially complete descriptions of the microbial world around and within us. Selecting how to perform feature data normalization, transformation and calculate ß-diversity is a critical step in the analysis of metagenomic data, but also a step for which a multitude of methods are available. Researchers need to have a broad overview and understand the many methods that exist in the field and the consequences from applying them. In this perspectives article, some of the most widely used metagenomic feature data normalizations, transformations and ß-diversity metrics are discussed in the context of multivariate visualizations. We provide a framework that other researchers can utilize to evaluate how robust their test data are when applying different normalizations, transformations and ß-diversity metrics, and visually compare the results of the methods. We constructed an in silico test dataset to evaluate the setup and clarify how the theoretical discussion is transferable to this data. We urge other researchers to implement their own test data, normalization, transformation, ß-diversity metric and visualization methods, in the hope that it will advance better decision making both in study design and analysis strategy.

This study aimed to elucidate aspects of the epidemiology of Blastocystis in Nigerian school children, including the distribution of subtypes (STs) and ST alleles. A total of 199 genomic DNAs extracted from fecal samples from 199 Nigerian children aged 2–14 years were tested by real-time polymerase chain reaction for Blastocystis. Positive DNAs were submitted to barcoding by PCR and sequencing to obtain information on STs and ST alleles. A total of 167 (84%) samples were positive for Blastocystis, with prevalence increasing by age. No association between Blastocystis colonization and gender (P = 0.51) or type/presence of toilet facilities (P = 0.21) was observed. Blastocystis carriers were more prone to using water collected from wells than from sachets (P = 0.0044). Moreover, Blastocystis positivity was associated with positivity for fecal-orally transmitted protozoa (P = 0.018) and helminths (P < 0.0001). A clear inverse association of Blastocystis colonization and malaria infection was observed (P < 0.0001); however, malaria-positive children being younger than malaria-negative children, this finding was attributed to the age effect of Blastocystis colonization. ST data were available for 127/167 (76%) samples. Fifty-one children were positive for ST1, while 42 and 33 children were colonized with ST2 and ST3, respectively; a single case of ST7 was observed. By and large, the ST alleles identified for ST1 and ST2 did not differ from those observed in humans in other regions of the world; meanwhile, the distribution of ST3 alleles was remarkably distinct and potentially specific to humans in sub-Saharan Africa.

Species of Sarcocystis are Apicomplexan parasites requiring intermediate and definitive hosts to complete their life cycle. Humans are one of many natural host species and may serve as both intermediate and definitive hosts. However, the extent and public health significance of human Sarcocystis infection are incompletely known. In this minireview, we provide an update on the epidemiology and diagnosis of human sarcocystosis and propose some tools that could contribute to a better understanding of the clinical significance and epidemiology of Sarcocystis infections.

One of the most common zoonotic helminth infections is caused by species in the genus Toxocara, particularly Toxocara canis and T. cati (Syn. T. mystax). However, their relative contribution to toxocarosis in humans remains largely unknown because causative larvae are seldom recovered and uncertainties regarding the validity of existing serological assays. In this study, we used sera from a pig model experimentally infected with T. canis and T. cati to evaluate whether a Western blot could discriminate between the two species. No proteins were observed that could be used as a diagnostic tool. In addition, a heterogenic protein pattern between individual hosts was found, which was most pronounced in the T. cati-infected pigs. There is therefore an urgent need to optimize and validate current methods or develop new species-specific serological methods in order to implement appropriate control measures.

Of the so-called nonpathogenic intestinal protozoa, Endolimax nana belongs to the ones least well described. Most data on E. nana have emerged from general surveys of intestinal parasites in selected cohorts and mostly in the absence of any particular focus on Endolimax. Hence, the genus of Endolimax remains largely unexplored in terms of morphology, taxonomy, genetic diversity, host specificity, and epidemiology. In this review, we seek to provide an overview of the work that has been performed on the parasite since the genus Endolimax was described by Kuenen and Swellengrebel in 1917 and suggest activities that may pave the way for a better understanding of E. nana in a clinical and public health context.