MinION™ Nanopore Sequencing of Skin Microbiome 16S and 16S-23S rRNA Gene Amplicons

Human skin microbiome dysbiosis can have clinical consequences. Characterizing taxonomic composition of bacterial communities associated with skin disorders is important for dermatological advancement in both diagnosis and novel treatments. This study aims to analyze and improve the accuracy of taxonomic classification of skin bacteria with MinION™ nanopore sequencing using a defined skin mock community and a skin microbiome sample. We compared the Oxford Nanopore Technologies recommended procedures and concluded that their protocols highly bias the relative abundance of certain skin microbiome genera, most notably a large overrepresentation of Staphylococcus and underrepresentation of Cutibacterium and Corynebacterium. We demonstrated that changes in the amplification protocols improved the accuracy of the taxonomic classification for these three main skin bacterial genera. This study shows that MinION™ nanopore could be an efficient technology for full-length 16S rRNA sequencing; however, the analytical advantage is strongly influenced by the methodologies. The suggested alternatives in the sample processing improved characterization of a complex skin microbiome community using MinION™ nanopore sequencing.

TaxaTarget: Fast, Sensitive, and Precise Classification of Microeukaryotes in Metagenomic Data

BackgroundMicrobial eukaryotes are nearly ubiquitous in microbiomes on Earth and contribute to many integral ecological functions. Metagenomics is a proven tool for studying the microbial diversity, functions, and ecology of microbiomes, but has been underutilized for microeukaryotes due to the computational challenges they present. For taxonomic classification, the use of a eukaryotic marker gene database can improve the computational efficiency, precision and sensitivity. However, state-of-the-art tools which use marker gene databases implement universal thresholds for classification rather than dynamically learning the thresholds from the database structure, impacting the accuracy of the classification process.ResultsHere we introduce taxaTarget, a method for the taxonomic classification of microeukaryotes in metagenomic data. Using a database of eukaryotic marker genes and a supervised learning approach for training, we learned the discriminatory power and classification thresholds for each 20 amino acid region of each marker gene in our database. This approach provided improved sensitivity and precision compared to other state-of-the-art approaches, with rapid runtimes and low memory usage. Additionally, taxaTarget was better able to detect the presence of multiple closely related species as well as species with no representative sequences in the database. One of the greatest challenges faced during the development of taxaTarget was the general sparsity of available sequences for microeukaryotes. Several algorithms were implemented, including threshold padding, which effectively handled the missing training data and reduced classification errors. Using taxaTarget on metagenomes from human fecal microbiomes, a broader range of genera were detected, including multiple parasites that the other tested tools missed.ConclusionData-driven methods for learning classification thresholds from the structure of an input database can provide granular information about the discriminatory power of the sequences and improve the sensitivity and precision of classification. These methods will help facilitate a more comprehensive analysis of metagenomic data and expand our knowledge about the diverse eukaryotes in microbial communities.

First Edition of Hahnemann's Organon: Celebrating its 200th Anniversary

With this special issue, IJHDR celebrates the 200th anniversary of the first edition of Hahnemann’s Organon, published in 1810. By 1796, in a previous foundational article published in Hufeland’s prestigious Journal of Practical Medicine [1], after criticizing with fine reasoning the usual ways of studying the “curative properties of medicinal substances”, namely according to their chemical actions, their effects on animals, their external aspect or sensitive properties, their taxonomic classification, or the random use of multiple drugs by the so called empiricists, Hahnemann stated what can be regarded as one of the cornerstones of homeopathy: “The true physician, whose sole aim is to perfect his art, can avail himself of no other information respecting medicines, than – first, what is the pure action of each by itself on the human body? Second, what do observations of its action in this or that simple or complex disease teach us?” ... ... To conclude, despite uncertainties and difficulties homeopaths still have to struggle with, we can only feel grateful to Hahnemann’s countless efforts to introduce homeopathy and everything what it means in medicine history and development.

A Metagenomics Method for the Quantitative Detection of Pathogens Causing Ventilator-Associated Pneumonia

Background The management of ventilator-associated and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen(s). To do it, we propose a metagenomics next-generation sequencing (mNGS) assay that includes an internal sample processing control (SPC) for the quantitative detection of 20 relevant bacterial species of interest (SOI) from bronchoalveolar lavage (BAL) samples. Results To avoid very major errors in identification of respiratory pathogens due to “false negative” cases, each sample was spiked with Bacillus subtilis, at a precisely defined concentration, using rehydrated BioBall®. This SPC ensured detection and quantification of the pathogen(s) at defined minimum concentrations. In the presented mNGS workflow, absolute quantification of Staphylococcus aureus was as accurate as quantitative PCR. We defined a metagenomics threshold at 5.3E+3 genome equivalent unit per milliliter of the sample for each SOI, to distinguish colonization from higher amounts of pathogens that may be associated with infection. Complete mNGS process and metrics were assessed on 40 clinical samples, showing 100 % sensitivity compared to microbial culture. However, 19 out of the 29 (66 %) SOI detections above the metagenomics threshold were not associated with bacterial growth above classical culture-based clinical thresholds. Taxonomic classification of 7 (37 %) of these “false positive” detections were confirmed by finding specific 16S/MetaPhlAn2 markers, the 12 other (63 %) “false positive” detections did not yield enough reads to check their taxonomic classification. Conclusions Our SPC design and analytical workflow allowed efficient detection and absolute quantification of pathogens from BAL samples, even when the bacterial DNA quantity was largely below manufacturer’s recommendations for NGS. The frequent "false positive" detection suggested the presence of non culturable cells within the tested BAL samples. Finally, mNGS detected mixed infections including bacterial species that were not reported by routine cultures.

The Most Widespread Phage in Animals: Genomics and Taxonomic Classification of Phage WO

Wolbachia are the most common obligate, intracellular bacteria in animals. They exist worldwide in arthropod and nematode hosts in which they commonly act as reproductive parasites or mutualists, respectively. Bacteriophage WO, the largest of Wolbachia's mobile elements, includes reproductive parasitism genes, serves as a hotspot for genetic divergence and genomic rearrangement of the bacterial chromosome, and uniquely encodes a Eukaryotic Association Module with eukaryotic-like genes and an ensemble of putative host interaction genes. Despite WO's relevance to genome evolution, selfish genetics, and symbiotic applications, relatively little is known about its origin, host range, diversification, and taxonomic classification. Here we analyze the most comprehensive set of 150 Wolbachia and phage WO assemblies to provide a framework for discretely organizing and naming integrated phage WO genomes. We demonstrate that WO is principally in arthropod Wolbachia with relatives in diverse endosymbionts and metagenomes, organized into four variants related by gene synteny, often oriented opposite the origin of replication in the Wolbachia chromosome, and the large serine recombinase is an ideal typing tool to assign taxonomic classification of the four variants. We identify a novel, putative lytic cassette and WO's association with a conserved eleven gene island, termed Undecim Cluster, that is enriched with virulence-like genes. Finally, we evaluate WO-like Islands in the Wolbachia genome and discuss a new model in which Octomom, a notable WO-like Island, arose from a split with WO. Together, these findings establish the first comprehensive Linnaean taxonomic classification of endosymbiont phages that includes distinguishable genera of phage WO, a family of non-Wolbachia phages from aquatic environments, and an order that captures the collective relatedness of these viruses.

Deep learning based on stacked sparse autoencoder applied to viral genome classification of SARS-CoV-2 virus

Since December 2019, the world has been intensely affected by the COVID-19 pandemic, caused by the SARS-CoV-2 virus, first identified in Wuhan, China. In the case of a novel virus identification, the early elucidation of taxonomic classification and origin of the virus genomic sequence is essential for strategic planning, containment, and treatments. Deep learning techniques have been successfully used in many viral classification problems associated with viral infections diagnosis, metagenomics, phylogenetic, and analysis. This work proposes to generate an efficient viral genome classifier for the SARS-CoV-2 virus using the deep neural network (DNN) based on the stacked sparse autoencoder (SSAE) technique. We performed four different experiments to provide different levels of taxonomic classification of the SARS-CoV-2 virus. The confusion matrix presented the validation and test sets and the ROC curve for the validation set. In all experiments, the SSAE technique provided great performance results. In this work, we explored the utilization of image representations of the complete genome sequences as the SSAE input to provide a viral classification of the SARS-CoV-2. For that, a dataset based on k-mers image representation, with k=6, was applied. The results indicated the applicability of using this deep learning technique in genome classification problems.