Non-target analysis of Danish wastewater treatment plant effluent: Statistical analysis of chemical fingerprinting as a step toward a future monitoring tool.

In an attempt to discover and characterize the plethora of xenobiotic substances, this study investigates chemical compounds released into the environment with wastewater effluents. A novel non-targeted screening methodology based on ultra-high resolution Orbitrap mass spectrometry and nanoflow ultra-high performance liquid chromatography together with a newly optimized data-processing pipeline were applied to effluent samples from two state-of-the-art and one small wastewater treatment facility. In total, 785 molecular structures were obtained, of which 38 were identified as single compounds, while 480 structures were identified at a putative level. Most of these substances were therapeutics and drugs, present as parent compounds and metabolites. Using R packages Phyloseq and MetacodeR, originally developed for bioinformatics, significant differences in xenobiotic presence in the wastewater effluents between the three sites were demonstrated.

Comprehensive Guideline for Microbiome Analysis Using R.

The need for a comprehensive consolidated guide for R packages and tools that are used in microbiome data analysis is significant; thus, we aim to provide a detailed step-by-step dissection of the most used R packages and tools in the field of microbiome data integration and analysis. The guideline aims to be a user-friendly simplification and tutorial on five main packages, namely phyloseq, MegaR, DADA2, Metacoder, and microbiomeExplorer due to their high efficiency and benefit in microbiome data analysis.

Benchmarking microbial DNA enrichment protocols from human intestinal biopsies.

Shotgun metagenomic sequencing is a powerful tool for studying bacterial communities in their natural habitats or sites of infection, without the need for cultivation. However, low microbial signals in metagenomic sequencing can be overwhelmed by host DNA contamination, resulting in decreased sensitivity for microbial read detection. Several commercial kits and other methods have been developed to enrich bacterial sequences; however, these assays have not been tested extensively for human intestinal tissues yet. Therefore, the objective of this study was to assess the effectiveness of various wet-lab and software-based approaches for depleting host DNA from microbiome samples. Four different microbiome DNA enrichment methods, namely the NEBNext Microbiome DNA Enrichment kit, Molzym Ultra-Deep Microbiome Prep, QIAamp DNA Microbiome kit, and Zymo HostZERO microbial DNA kit, were evaluated, along with a software-controlled adaptive sampling (AS) approach by Oxford Nanopore Technologies (ONT) providing microbial signal enrichment by aborting unwanted host DNA sequencing. The NEBNext and QIAamp kits proved to be effective in shotgun metagenomic sequencing studies, as they efficiently reduced host DNA contamination, resulting in 24% and 28% bacterial DNA sequences, respectively, compared to <1% in the AllPrep controls. Additional optimization steps using further detergents and bead-beating steps improved the efficacy of less efficient protocols but not of the QIAamp kit. In contrast, ONT AS increased the overall number of bacterial reads resulting in a better bacterial metagenomic assembly with more bacterial contigs with greater completeness compared to non-AS approaches. Additionally, AS also allowed for the recovery of antimicrobial resistance markers and the identification of plasmids, demonstrating the potential utility of AS for targeted sequencing of microbial signals in complex samples with high amounts of host DNA. However, ONT AS resulted in relevant shifts in the observed bacterial abundance, including 2 to 5 times more Escherichia coli reads. Furthermore, a modest enrichment of Bacteroides fragilis and Bacteroides thetaiotaomicron was also observed with AS. Overall, this study provides insight into the efficacy and limitations of various methods for reducing host DNA contamination in human intestinal samples to improve the utility of metagenomic sequencing.

MBECS: Microbiome Batch Effects Correction Suite.

Despite the availability of batch effect correcting algorithms (BECA), no comprehensive tool that combines batch correction and evaluation of the results exists for microbiome datasets. This work outlines the Microbiome Batch Effects Correction Suite development that integrates several BECAs and evaluation metrics into a software package for the statistical computation framework R.

The Microbial Diversity of Cabbage Pest Delia radicum Across Multiple Life Stages.

The cabbage root fly Delia radicum is a worldwide pest that causes yield losses of many common cabbage crops. The bacteria associated with D. radicum are suggested to influence the pest status of their host. In this study, we characterized insect-associated bacteria of D. radicum across multiple life stages and of their diet plant (turnip, Brassica rapa subsp. rapa) by sequencing the V3-V4 region of 16S rRNA genes using the Illumina MiSeq platform. In total, over 1.2M paired-end reads were obtained, identifying 1006 bacterial amplicon sequence variants (ASVs) in samples obtained from the eggs, larvae, pupae and adults of D. radicum, as well as turnips that were either fresh or infested with D. radicum larvae. The microbial community in D. radicum was dominated by Wolbachia, a common endosymbiont of arthropods which we found in all of the investigated insect samples, with the pupal stage having the highest relative abundance. Moderate amounts of Firmicutes were found only in adult D. radicum flies, but not in previous life stages. Actinobacteria were mostly found on the eggs and on the skin of fresh plants on which the eggs were deposited. These plants also harbored a large amount of Pseudomonas. The bacterial diversity of the healthy turnip was low, whereas the microbial community of decaying turnips that were heavily infested by D. radicum larvae and showing symptoms of advanced soft rot was characterized by a high bacterial diversity. Taken together, this work provides insights into the bacterial communities associated with the cabbage pest D. radicum and its associated disease symptoms.

MetaDEGalaxy: Galaxy workflow for differential abundance analysis of 16s metagenomic data.

Metagenomic sequencing is an increasingly common tool in environmental and biomedical sciences yet analysis workflows remain immature relative to other field such as DNASeq and RNASeq analysis pipelines.  While software for detailing the composition of microbial communities using 16S rRNA marker genes is constantly improving, increasingly researchers are interested in identifying changes exhibited within microbial communities under differing environmental conditions. In order to gain maximum value from metagenomic sequence data we must improve the existing analysis environment by providing accessible and scalable computational workflows able to generate reproducible results. Here we describe a complete end-to-end open-source metagenomics workflow running within Galaxy for 16S differential abundance analysis. The workflow accepts 454 or Illumina sequence data (either overlapping or non-overlapping paired end reads) and outputs lists of the operational taxonomic unit (OTUs) exhibiting the greatest change under differing conditions. A range of analysis steps and graphing options are available giving users a high-level of control over their data and analyses. Additionally, users are able to input complex sample-specific metadata information which can be incorporated into differential analysis and used for grouping / colouring within graphs.  Detailed tutorials containing sample data and existing workflows are available for three different input types: overlapping and non-overlapping read pairs as well as for pre-generated Biological Observation Matrix (BIOM) files. Using the Galaxy platform we developed MetaDEGalaxy, a complete metagenomics differential abundance analysis workflow. MetaDEGalaxy is designed for bench scientists working with 16S data who are interested in comparative metagenomics.  MetaDEGalaxy builds on momentum within the wider Galaxy metagenomics community with the hope that more tools will be added as existing methods mature.