Contamination detection and microbiome exploration with GRIMER

micRoclean: an R package for decontaminating low-biomass 16S-rRNA microbiome data

In 16S-rRNA microbiome studies, cross-contamination and environmental contamination can obscure true biological signal. This contamination is particularly problematic in low-biomass studies, which are characterized by samples with a small amount of microbial DNA. Although multiple methods and packages for decontaminating microbiome data exist, there is no consensus on the most appropriate tool for decontamination based on the individual research study design and how to quantify the impact of removing identified contaminants to avoid over-filtering. To address these gaps, we introduce micRoclean, an open-source R package that contains two distinct microbiome decontamination pipelines with guidance on which to select based on the downstream goals of the research study and study design. This package integrates and expands on existing packages for microbiome decontamination and analysis for convenience of users. Furthermore, micRoclean also implements a filtering loss statistic to quantify the impact of decontamination on the overall covariance structure of the data. In this paper, we demonstrate the utility of micRoclean through implementation on example data, illustrating that micRoclean effectively and intuitively decontaminates microbiome data. Further, we demonstrate through a multi-batch simulated microbiome sample that micRoclean matches or outperforms tools with similar objectives. This package is freely available from GitHub repository rachelgriffard/micRoclean.

Impact of microbiological molecular methodologies on adaptive sampling using nanopore sequencing in metagenomic studies

INTRODUCTION

Metagenomics, the genomic analysis of all species present within a mixed population, is an important tool used for the exploration of microbiomes in clinical and environmental microbiology. Whilst the development of next-generation sequencing, and more recently third generation long-read approaches such as nanopore sequencing, have greatly advanced the study of metagenomics, recovery of unbiased material from microbial populations remains challenging. One promising advancement in genomic sequencing from Oxford Nanopore Technologies (ONT) is adaptive sampling, which enables real-time enrichment or depletion of target sequences. As sequencing technologies continue to develop, and advances such as adaptive sampling become common techniques within the microbiological toolkit, it is essential to evaluate the benefits of such advancements to metagenomic studies, and the impact of methodological choices on research outcomes.

AIM AND METHODS

Given the rapid development of sequencing tools and chemistry, this study aimed to demonstrate the impacts of choice of DNA extraction kit and sequencing chemistry on downstream metagenomic analyses. We first explored the quality and accuracy of 16S rRNA amplicon sequencing for DNA extracted from the ZymoBIOMICS Microbial Community Standard, using a range of commercially available DNA extraction kits to understand the effects of different kit biases on assessment of microbiome composition. We next compared the quality and accuracy of metagenomic analyses for two nanopore-based ligation chemistry kits with differing levels of base-calling error; the older and more error-prone (~ 97% accuracy) LSK109 chemistry, and newer more accurate (~ 99% accuracy) LSK112 Q20 + chemistry. Finally, we assessed the impact of the nanopore sequencing chemistry version on the output of the novel adaptive sampling approach for real-time enrichment of the genome for the yeast Saccharomyces cerevisiae from the microbial community.

RESULTS

Firstly, DNA extraction kit methodology impacted the composition of the yield, with mechanical bead-beating methodologies providing the least biased picture due to efficient lysis of Gram-positive microbes present in the community standard, with differences in bead-beating methodologies also producing variation in composition. Secondly, whilst use of the Q20 + nanopore sequencing kit chemistry improved the base-calling data quality, the resulting metagenomic assemblies were not significantly improved based on common metrics and assembly statistics. Most importantly, we demonstrated the effective application of adaptive sampling for enriching a low-abundance genome within a metagenomic sample. This resulted in a 5-7-fold increase in target enrichment compared to non-adaptive sequencing, despite a reduction in overall sequencing throughput due to strand-rejection processes. Interestingly, no significant differences in adaptive sampling enrichment efficiency were observed between the older and newer ONT sequencing chemistries, suggesting that adaptive sampling performs consistently across different library preparation kits.

CONCLUSION

Our findings underscore the importance of selecting a DNA extraction methodology that minimises bias to ensure an accurate representation of microbial diversity in metagenomic studies. Additionally, despite the improved base-calling accuracy provided by newer Q20 + sequencing chemistry, we demonstrate that even older ONT sequencing chemistries can achieve reliable metagenomic sequencing results, enabling researchers to confidently use these approaches depending on their specific experimental needs. Critically, we highlight the significant potential of ONT's adaptive sampling technology for targeted enrichment of specific genomes within metagenomic samples. This approach offers broad applicability for enriching target organisms or genetic elements (e.g., pathogens or plasmids) or depleting unwanted DNA (e.g., host DNA) in diverse sample types from environmental and clinical studies. However, researchers should carefully weigh the benefits of adaptive sampling against the potential trade-offs in sequencing throughput, particularly for low-abundance targets, where strand rejection can lead to pore blocking. These results provide valuable guidance for optimising adaptive sampling in metagenomic workflows to achieve specific research objectives.

Bacterial DNA Contamination of Commercial PCR Enzymes: Considerations for Microbiome Protocols and Analysis

The microbiome remains a top area of research, and it is now common to examine any organic and inorganic samples for bacterial colonization. However, due to the ubiquity of bacteria in the environment, separating the low-burden colonization of bacteria from the possible contamination of laboratory reagents remains problematic. When examining samples of expected low bacterial burden, it is common to first amplify any bacterial DNA present through PCR before sequencing. In this work, we examined nine different commercial PCR enzymes and their reaction components as possible sources of bacterial DNA contamination. We found contaminating bacterial DNA in seven of the nine reactions, and this DNA was shown to come from a variety of species. Importantly, we were able to perform these studies solely with endpoint PCR and Sanger sequencing, which are more accessible and affordable than high-throughput, short-read sequencing and real-time PCR. This work confirms that there needs to be an increased emphasis on including control reactions in microbiome studies so that contaminating DNA sequences can be identified and addressed, and that this can be achieved with minimal resources.

Experimental tests challenge the evidence of a healthy human blood microbiome

The advent of next-generation sequencing (NGS) technologies has made it possible to investigate microbial communities in various environments, including different sites within the human body. Therefore, the previously established belief of the sterile nature of several body sites, including human blood, has now been challenged. However, metagenomics investigation of areas with an anticipated low microbial biomass may be susceptible to misinterpretation. Here, we critically evaluate the results of 16S targeted amplicon sequencing performed on total DNA collected from healthy donors' blood samples while incorporating specific negative controls aimed at addressing potential bias to supplement and strengthen the research in this area. We prepared negative controls by increasing the initial DNA quantity through sequences that can be recognized and subsequently discarded. We found that only three organisms were sporadically present among the samples, and this was mostly attributable to bacteria ubiquitously present in laboratory reagents. Despite not fully confirming or denying the existence of healthy blood microbiota, our results suggest that living bacteria, or at least their residual DNA sequences, are not a common feature of human blood in healthy people. Finally, our study poses relevant questions on the design of controls in this research area that must be considered in order to avoid misinterpreted results that appear to contaminate current high-throughput research.

Microbiome and floral associations of a wild bee using biodiversity survey collections

The health of bees can be assessed through their microbiome, which serves as a biomarker indicating the presence of both beneficial and harmful microorganisms within a bee community. This study presents the characterisation of the bacterial, fungal, and plant composition on the cuticle of adult bicoloured sweat bees (Agapostemon virescens). These bees were collected using various methods such as pan traps, blue vane traps and sweep netting across the northern extent of their habitat range. Non-destructive methods were employed to extract DNA from the whole pinned specimens of these wild bees. Metabarcoding of the 16S rRNA, ITS and rbcL regions was then performed. The study found that the method of collection influenced the detection of certain microbial and plant taxa. Among the collection methods, sweep net samples showed the lowest fungal alpha diversity. However, minor differences in bacterial or fungal beta diversity suggest that no single method is significantly superior to others. Therefore, a combination of techniques can cater to a broader spectrum of microbial detection. The study also revealed regional variations in bacterial, fungal and plant diversity. The core microbiome of A. virescens comprises two bacteria, three fungi and a plant association, all of which are commonly detected in other wild bees. These core microbes remained consistent across different collection methods and locations. Further extensive studies of wild bee microbiomes across various species and landscapes will help uncover crucial relationships between pollinator health and their environment.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.

Current Trends and Challenges of Microbiome Research in Bladder Cancer

PURPOSE OF THE REVIEW

Microbiome research has provided valuable insights into the associations between microbial communities and bladder cancer. However, this field faces significant challenges that hinder the interpretation, generalization, and translation of findings into clinical practice. This review aims to elucidate these challenges and highlight the importance of addressing them for the advancement of microbiome research in bladder cancer.

RECENT FINDINGS

Recent findings underscore the complexities involved in microbiome research, particularly in the context of bladder cancer. Challenges include low microbial biomass in urine samples, potential contamination issues during collection and processing, variability in sequencing methods and primer selection, and the difficulty of establishing causality between microbiota and bladder cancer. Studies have shown the impact of sample storage conditions and DNA isolation kits on microbiome analysis, emphasizing the need for standardization. Additionally, variations in urine collection methods can introduce contamination and affect results. The choice of 16S rRNA gene amplicon sequencing or shotgun metagenomic sequencing introduces technical challenges, including primer selection and sequencing read length. Establishing causality between the microbiota and bladder cancer requires experimental methods like fecal microbiota transplantation and human microbiota-associated murine models, which face their own set of challenges. Translating microbiome research into therapeutic applications is hindered by methodological variability, incomplete understanding of bioactive molecules, imperfect animal models, and the inherent heterogeneity of microbiome communities among individuals. Microbiome research in bladder cancer presents significant challenges stemming from technical and conceptual complexities. Addressing these challenges through standardization, improved experimental models, and advanced analytical approaches is essential for advancing our understanding of the microbiome's role in bladder cancer and its potential clinical applications. Achieving this goal can lead to improved patient outcomes and novel therapeutic strategies in the future.

Contamination detection and microbiome exploration with GRIMER

BACKGROUND

Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination.

RESULTS

We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles.

CONCLUSION

GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer.