Sensitive, real-time PCR detects low-levels of contamination by Legionella pneumophila in commercial reagents

Challenges for pathologists in implementing clinical microbiome diagnostic testing

Recent research has established that the microbiome plays potential roles in the pathogenesis of numerous chronic diseases, including carcinomas. This discovery has led to significant interest in clinical microbiome testing among physicians, translational investigators, and the lay public. As novel, inexpensive methodologies to interrogate the microbiota become available, research labs and commercial vendors have offered microbial assays. However, these tests still have not infiltrated the clinical laboratory space. Here, we provide an overview of the challenges of implementing microbiome testing in clinical pathology. We discuss challenges associated with preanalytical and analytic sample handling and collection that can influence results, choosing the appropriate testing methodology for the clinical context, establishing reference ranges, interpreting the data generated by testing and its value in making patient care decisions, regulation, and cost considerations of testing. Additionally, we suggest potential solutions for these problems to expedite the establishment of microbiome testing in the clinical laboratory.

Adaptive Filtering Framework to Remove Nonspecific and Low-Efficiency Reactions in Multiplex Digital PCR Based on Sigmoidal Trends

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics of amplification curves and the thermodynamics of melting curves, and it demonstrates that filtering out nonspecific or low-efficient reactions can significantly improve the classification accuracy for cutting-edge multiplexing methodologies.

Toward efficient and high-fidelity metagenomic data from sub-nanogram DNA: evaluation of library preparation and decontamination methods

BACKGROUND

Shotgun metagenomic sequencing has greatly expanded the understanding of microbial communities in various biological niches. However, it is still challenging to efficiently convert sub-nanogram DNA to high-quality metagenomic libraries and obtain high-fidelity data, hindering the exploration of niches with low microbial biomass.

RESULTS

To cope with this challenge comprehensively, we evaluated the performance of various library preparation methods on 0.5 pg-5 ng synthetic microbial community DNA, characterized contaminants, and further applied different in silico decontamination methods. First, we discovered that whole genome amplification prior to library construction led to worse outcomes than preparing libraries directly. Among different non-WGA-based library preparation methods, we found the endonuclease-based method being generally good for different amounts of template and the tagmentation-based method showing specific advantages with 0.5 pg template, based on evaluation metrics including fidelity, proportion of designated reads, and reproducibility. The load of contaminating DNA introduced by library preparation varied from 0.01 to 15.59 pg for different kits and accounted for 0.05 to 45.97% of total reads. A considerable fraction of the contaminating reads were mapped to human commensal and pathogenic microbes, thus potentially leading to erroneous conclusions in human microbiome studies. Furthermore, the best performing in silico decontamination method in our evaluation, Decontam-either, was capable of recovering the real microbial community from libraries where contaminants accounted for less than 10% of total reads, but not from libraries with heavy and highly varied contaminants.

CONCLUSIONS

This study demonstrates that high-quality metagenomic data can be obtained from samples with sub-nanogram microbial DNA by combining appropriate library preparation and in silico decontamination methods and provides a general reference for method selection for samples with varying microbial biomass.

Bacteria in the amniotic fluid without inflammation: early colonization vs. contamination

OBJECTIVES

Intra-amniotic infection, defined by the presence of microorganisms in the amniotic cavity, is often accompanied by intra-amniotic inflammation. Occasionally, laboratories report the growth of bacteria or the presence of microbial nucleic acids in amniotic fluid in the absence of intra-amniotic inflammation. This study was conducted to determine the clinical significance of the presence of bacteria in amniotic fluid samples in the absence of intra-amniotic inflammation.

METHODS

A retrospective cross-sectional study included 360 patients with preterm labor and intact membranes who underwent transabdominal amniocentesis for evaluation of the microbial state of the amniotic cavity as well as intra-amniotic inflammation. Cultivation techniques were used to isolate microorganisms, and broad-range polymerase chain reaction coupled with electrospray ionization mass spectrometry (PCR/ESI-MS) was utilized to detect the nucleic acids of bacteria, viruses, and fungi.

RESULTS

Patients whose amniotic fluid samples evinced microorganisms but did not indicate inflammation had a similar perinatal outcome to those without microorganisms or inflammation [amniocentesis-to-delivery interval (p=0.31), spontaneous preterm birth before 34 weeks (p=0.83), acute placental inflammatory lesions (p=1), and composite neonatal morbidity (p=0.8)].

CONCLUSIONS

The isolation of microorganisms from a sample of amniotic fluid in the absence of intra-amniotic inflammation is indicative of a benign condition, which most likely represents contamination of the specimen during the collection procedure or laboratory processing rather than early colonization or infection.

Elucidating biofilm diversity on water lily leaves through 16S rRNA amplicon analysis: Comparison of four DNA extraction kits

PREMISE

Within a broader study on leaf fossilization in freshwater environments, a long-term study on the development and microbiome composition of biofilms on the foliage of aquatic plants has been initiated to understand how microbes and biofilms contribute to leaf decay and preservation. Here, water lily leaves are employed as a study model to investigate the relationship between bacterial microbiomes, biodegradation, and fossilization. We compare four DNA extraction kits to reduce biases in interpretation and to identify the most suitable kit for the extraction of DNA from bacteria associated with biofilms on decaying water lily leaves for 16S rRNA amplicon analysis.

METHODS

We extracted surface-associated DNA from Nymphaea leaves in early stages of decay at two water depth levels using four commercially available kits to identify the most suitable protocol for bacterial extraction, applying a mock microbial community standard to enable a reliable comparison of the kits.

RESULTS

Kit 4, the FastDNA Spin Kit for Soil, resulted in high DNA concentrations with better quality and yielded the most accurate depiction of the mock community. Comparison of the leaves at two water depths showed no significant differences in community composition.

DISCUSSION

The success of Kit 4 may be attributed to its use of bead beating with a homogenizer, which was more efficient in the lysis of Gram-positive bacteria than the manual vortexing protocols used by the other kits. Our results show that microbial composition on leaves during early decay remains comparable and may change only in later stages of decomposition.

Host-pathogen transcriptomics: Trypanosoma cruzi as a model for studying RNA contamination

Cellular infection assays constitute essential tools to understand host-pathogen interactions, particularly for intracellular microorganisms that are produced in cell lines are needed to propagate the microorganism. In this work, we demonstrate that RNA derived from Vero cells is an important contaminant to consider in order to avoid false positive results in transcriptomic experiments. We study the cross-contamination on a Trypanosoma cruzi cell infection model, the etiological agent of Chagas disease. We implemented the most frequently used trypanosome-purification protocols and, for all of them, we detected RNAs derived from Vero cells in trypomastigote extracts. For some of the protocols we also detected Vero RNAs in infected human cells. We also found this type of contamination in microarray experiments of human samples infected with T. cruzi. Concerning Illumina RNA-Seq data, we found that the contamination with Vero cells is probably introducing spurious results. Finally, we recommend a protocol to purify trypomastigotes, which showed a high percentage of trypomastigote recovery and the absence of Vero contamination in infected human samples. Avoiding this type of contamination should be an important factor to consider during experimental design, in order to minimize false positive results in transcriptomic studies as well as RNA contamination in vaccine production. SIGNIFICANCE: Transcriptomic studies are widely used to understand host-pathogen interactions. When the pathogen is an intracellular microorganism, an additional mammalian cell system can be needed to propagate it. In this work we demonstrate that pathogens purified from infected monolayers can carry RNAs from these mammalian cells, and that this ambient RNA contamination is probably producing false positive results in subsequent transcriptomic studies performed with qRT-PCR, microarrays or Next Generation Sequencing.

Urinary Microbiota-Are We Ready for Prime Time? A Literature Review of Study Methods' Critical Steps in Avoiding Contamination and Minimizing Biased Results

Within the last few years, there have been an increased number of clinical studies involving urinary microbiota. Low-biomass microbiome sequencing (e.g., urine, lung, placenta, blood) is easily biased by contamination or cross-contamination. So far, a few critical steps, from sampling urine to processing and analyzing, have been described (e.g., urine collection modality, sample volume size, snap freezing, negative controls usage, laboratory risks for contamination assessment, contamination of negative results reporting, exploration and discussion of the impact of contamination for the final results, etc.) We performed a literature search (Pubmed, Scopus and Embase) and reviewed the published articles related to urinary microbiome, evaluating how the aforementioned critical steps to obtain unbiased, reliable results have been taken or have been reported. We identified different urinary microbiome evaluation protocols, with non-homogenous reporting systems, which can make gathering results into consistent data for similar topics difficult and further burden the already so complex emerging field of urinary microbiome. We concluded that to ease the progress in this field, a joint approach from researchers, authors and publishers would be necessary in order to create mandatory reporting systems which would allow to recognize pitfalls and avoid compromising a promising field of research.

Microbiome applications for pathology: challenges of low microbial biomass samples during diagnostic testing

The human microbiome can play key roles in disease, and diagnostic testing will soon have the ability to examine these roles in the context of clinical applications. Currently, most diagnostic testing in pathology applications focuses on a small number of disease‐causing microbes and dismisses the whole microbial community that causes or is modulated by disease. Microbiome modifications have already provided clinically relevant insights in gut and oral diseases, such as irritable bowel disease, but there are currently limitations when clinically examining microbiomes outside of these body sites. This is critical, as the majority of microbial samples used in pathology originate from body sites that contain low concentrations of microbial DNA, including skin, tissue, blood, and urine. These samples, also known as low microbial biomass samples, are difficult to examine without careful consideration and precautions to mitigate contamination and biases. Here, we present the limitations when analysing low microbial biomass samples using current protocols and techniques and highlight the advantages that microbiome testing can offer diagnostics in the future, if the proper precautions are implemented. Specifically, we discuss the sources of contamination and biases that may result in false assessments for these sample types. Finally, we provide recommendations to mitigate contamination and biases from low microbial biomass samples during diagnostic testing, which will be especially important to effectively diagnose and treat patients using microbiome analyses.

Revealing the secret life of skin - with the microbiome you never walk alone

The human skin microbiome has recently become a focus for both the dermatological and cosmetic fields. Understanding the skin microbiota, that is the collection of vital microorganisms living on our skin, and how to maintain its delicate balance is an essential step to gain insight into the mechanisms responsible for healthy skin and its appearance. Imbalances in the skin microbiota composition (dysbiosis) are associated with several skin conditions, either pathological such as eczema, acne, allergies or dandruff or non‐pathological such as sensitive skin, irritated skin or dry skin. Therefore, the development of approaches which preserve or restore the natural, individual balance of the microbiota represents a novel target not only for dermatologists but also for skincare applications. This review gives an overview on the current knowledge on the skin microbiome, the currently available sampling and analysis techniques as well as a description of current approaches undertaken in the skincare segment to help restoring and balancing the structure and functionality of the skin microbiota.

Technical pitfalls that bias comparative microbial community analyses of aquatic disease Ian Hewson

The accessibility of high-throughput DNA sequencing technologies has attracted the application of comparative microbial analyses to study diseases. These studies present a window into host microbiome diversity and composition that can be used to address ecological theory in the context of host biology and behavior. Recently, comparative microbiome studies have been used to study non-vertebrate aquatic diseases to elucidate microorganisms potentially involved in disease processes or in disease prevention. These investigations suffer from many well-described biases, especially prior to sequence analyses, that could lead to misleading conclusions. Microbiome-focused studies of aquatic metazoan diseases provide valuable documentation of microbial ecology, although, they are only a starting point for establishing disease etiology, which demands quantitative validation through targeted approaches. The microbiome approach to understanding disease is most useful after laboratory diagnostics guided by pathology have failed to identify a causative agent. This opinion piece presents several technical pitfalls which may affect wider interpretation of microbe-host interactions through comparative microbial community analyses and provides recommendations, based on studies in non-aquatic systems, for incorporation into future aquatic disease research.

Usability of rectal swabs for microbiome sampling in a cohort study of hematological and oncological patients

Objectives

Large-scale clinical studies investigating associations between intestinal microbiota signatures and human diseases usually rely on stool samples. However, the timing of repeated stool sample collection cannot be predefined in longitudinal settings. Rectal swabs, being straightforward to obtain, have the potential to overcome this drawback. Therefore, we assessed the usability of rectal swabs for microbiome sampling in a cohort of hematological and oncological patients.

Study design

We used a pipeline for intestinal microbiota analysis from deep rectal swabs which was established and validated with test samples and negative controls. Consecutively, a cohort of patients from hematology and oncology wards was established and weekly deep rectal swabs taken during their admissions and re-admissions.

Results

Validation of our newly developed pipeline for intestinal microbiota analysis from rectal swabs revealed consistent and reproducible results. Over a period of nine months, 418 rectal swabs were collected longitudinally from 41 patients. Adherence to the intended sampling protocol was 97%. After DNA extraction, sequencing, read pre-processing and filtering of chimeric sequences, 405 of 418 samples (96.9%) were eligible for further analyses. Follow-up samples and those taken under current antibiotic exposure showed a significant decrease in alpha diversity as compared to baseline samples. Microbial domination occurred most frequently by Enterococcaceae (99 samples, 24.4%) on family level and Enterococcus (90 samples, 22.2%) on genus level. Furthermore, we noticed a high abundance of potential skin commensals in 99 samples (24.4%).

Summary

Deep rectal swabs were shown to be reliable for microbiome sampling and analysis, with practical advantages related to high sampling adherence, easy timing, transport and storage. The relatively high abundance of putative skin commensals in this patient cohort may be of potential interest and should be further investigated. Generally, previous findings on alpha diversity dynamics obtained from stool samples were confirmed.

Plasmid DNA contaminant in molecular reagents

Background noise in metagenomic studies is often of high importance and its removal requires extensive post-analytic, bioinformatics filtering. This is relevant as significant signals may be lost due to a low signal-to-noise ratio. The presence of plasmid residues, that are frequently present in reagents as contaminants, has not been investigated so far, but may pose a substantial bias. Here we show that plasmid sequences from different sources are omnipresent in molecular biology reagents. Using a metagenomic approach, we identified the presence of the (pol) of equine infectious anemia virus in human samples and traced it back to the expression plasmid used for generation of a commercial reverse transcriptase. We found fragments of multiple other expression plasmids in human samples as well as commercial polymerase preparations. Plasmid contamination sources included production chain of molecular biology reagents as well as contamination of reagents from environment or human handling of samples and reagents. Retrospective analyses of published metagenomic studies revealed an inaccurate signal-to-noise differentiation. Hence, the plasmid sequences that seem to be omnipresent in molecular biology reagents may misguide conclusions derived from genomic/metagenomics datasets and thus also clinical interpretations. Critical appraisal of metagenomic data sets for the possibility of plasmid background noise is required to identify reliable and significant signals.

High Microbial Diversity Despite Extremely Low Biomass in a Deep Karst Aquifer

Despite the importance of karst aquifers as a source of drinking water, little is known about the role of microorganisms in maintaining the quality of this water. One of the limitations in exploring the microbiology of these environments is access, which is usually limited to wells and surface springs. In this study, we compared the microbiology of the Madison karst aquifer sampled via the potentiometric lakes of Wind Cave with surface sampling wells and a spring. Our data indicated that only the Streeter Well (STR), which is drilled into the same hydrogeologic domain as the Wind Cave Lakes (WCL), allowed access to water with the same low biomass (1.56-9.25 × 10³ cells mL^-1). Filtration of ∼300 L of water from both of these sites through a 0.2 μm filter allowed the collection of sufficient cells for DNA extraction, PCR amplification of 16S rRNA gene sequences, and identification through pyrosequencing. The results indicated that bacteria (with limited archaea and no detectable eukaryotic organisms) dominated both water samples; however, there were significant taxonomic differences in the bacterial populations of the samples. The STR sample was dominated by a single phylotype within the Gammaproteobacteria (Order Acidithiobacillales), which dramatically reduced the overall diversity and species richness of the population. In WCL, despite less organic carbon, the bacterial population was significantly more diverse, including significant contributions from the Gammaproteobacteria, Firmicutes, Chloroflexi, Actinobacteria, Planctomycetes, Fusobacter, and Omnitrophica phyla. Comparisons with similar oligotrophic environments suggest that karst aquifers have a greater species richness than comparable surface environs. These data also demonstrate that Wind Cave provides a unique opportunity to sample a deep, subterranean aquifer directly, and that the microbiology of such aquifers may be more complex than previously anticipated.

Identification and removal of contaminating microbial DNA from PCR reagents: impact on low-biomass microbiome analyses

Reagent-derived contamination can compromise the integrity of microbiome data, particularly in low microbial biomass samples. This contamination has recently been attributed to the 'kitome' (contamination introduced by the DNA extraction kit), prior to which attention was mostly paid to potential contamination introduced by PCR reagents. In this study, we assessed the proportion to which our DNA extraction kit and PCR master mix introduce contaminating microbial DNA to bacterial microbial profiles generated by 16S rRNA gene sequencing. Utilizing a commercial dsDNase treatment protocol to decontaminate the PCR master mix, we demonstrated that the vast majority of contaminating DNA was derived from the PCR master mix. Importantly, this contamination was almost completely eliminated using the simple dsDNase treatment, resulting in a 99% reduction in contaminating bacterial reads. We suggest that dsDNase treatment of PCR reagents should be explored as a simple and effective way of reducing contamination in low-biomass microbiome studies and producing more robust and reliable data. SIGNIFICANCE AND IMPACT OF THE STUDY: Reagent contamination with microbial DNA is a major problem in microbiome studies of low microbial biomass samples. Levels of such contaminating DNA often outweigh what is present in the sample and heavily confound subsequent data analysis. Previous studies have suggested this contamination is primarily derived from DNA extraction kits. Here, we identified the PCR master mix as the primary source of contamination, and showed that enzymatic removal of the contamination drastically reduced the blank signal and improved precision. Decontamination of PCR master mixes may have the potential to improve the sensitivity and accuracy of low-biomass microbiome studies.

Limited Correlation of Shotgun Metagenomics Following Host Depletion and Routine Diagnostics for Viruses and Bacteria in Low Concentrated Surrogate and Clinical Samples

The etiologic cause of encephalitis, meningitis or meningo-encephalitis is unknown in up to 70% of cases. Clinical shotgun metagenomics combined with host depletion is a promising technique to identify infectious etiologies of central nervous system (CNS) infections. We developed a straightforward eukaryotic host nucleic acid depletion method that preserves intact viruses and bacteria for subsequent shotgun metagenomics screening of clinical samples, focusing on cerebrospinal fluid (CSF). A surrogate CSF sample for a CNS infection paradigm was used to evaluate the proposed depletion method consisting of selective host cell lysis, followed by enzymatic degradation of the liberated genomic DNA for final depletion with paramagnetic beads. Extractives were subjected to reverse transcription, followed by whole genome amplification and next generation sequencing. The effectiveness of the host depletion method was demonstrated in surrogate CSF samples spiked with three 1:100 dilutions of Influenza A H3N2 virus (qPCR Ct-values 20.7, 28.8, >42/negative). Compared to the native samples, host depletion increased the amount of the virus subtype reads by factor 7127 and 132, respectively, while in the qPCR negative sample zero vs. 31 (1.4E-4 %) virus subtype reads were detected (native vs. depleted). The workflow was applied to thirteen CSF samples of patients with meningo-/encephalitis (two bacterial, eleven viral etiologies), a serum of an Andes virus infection and a nose swab of a common cold patient. Unlike surrogate samples, host depletion of the thirteen human CSF samples and the nose swab did not result in more reads indicating presence of damaged pathogens due to, e.g., host immune response. Nevertheless, previously diagnosed pathogens in the human CSF samples (six viruses, two bacteria), the serum, and the nose swab (Human rhinovirus A31) were detected in the depleted and/or the native samples. Unbiased evaluation of the taxonomic profiles supported the diagnosed pathogen in two native CSF samples and the native and depleted serum and nose swab, while detecting various contaminations that interfered with pathogen identification at low concentration levels. In summary, damaged pathogens and contaminations complicated analysis and interpretation of clinical shotgun metagenomics data. Still, proper consideration of these issues may enable future application of metagenomics for clinical diagnostics.

Correcting for batch effects in case-control microbiome studies

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.

Batch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fisher’s method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets.

Analysis of the cervical microbiome and potential biomarkers from postpartum HIV-positive women displaying cervical intraepithelial lesions

The cervical microbiota composition and diversity of HIV-positive women in the postpartum period is unknown. Using a high-throughput bacterial 16S rRNA gene sequencing, we identified four community state types (CSTs). CST III (Lactobacillusdominant) and CST IV (IV-A, IV-B.1, IV-B.2; high-diversity) were found in 41% and 59% of samples, respectively. We did not find association of any CST to postpartum period (six or twelve months), HPV infection or cytology (normal or lesion). However, five bacterial genera were associated with cervical lesions (Gardnerella, Aerococcus, Schlegelella, Moryella and Bifidobacterium), with significant odds ratio (OR) of 40 (2.28–706) for the presence of Moryella and 3.5 (1.36–8.9) for Schlegelella. Longitudinal analysis of samples at postpartum that regressed (lesion to normal), progressed (normal to lesion) and maintained the cytology (lesion or normal) evidenced Gardnerella with a significantly higher abundance in regressing lesions. In the current study, we report the first data on the cervical microbiota of HIV-positive women in the postpartum period. Consistent with previous studies of HIV-negative cohorts, HIV-positive women present a stable cervical microbiota of high-diversity in the postpartum period. Our results highlight that specific microbiota species may serve as sensors for changes in the cervical microenvironment associated with cervical lesions.

Optimizing methods and dodging pitfalls in microbiome research

Research on the human microbiome has yielded numerous insights into health and disease, but also has resulted in a wealth of experimental artifacts. Here, we present suggestions for optimizing experimental design and avoiding known pitfalls, organized in the typical order in which studies are carried out. We first review best practices in experimental design and introduce common confounders such as age, diet, antibiotic use, pet ownership, longitudinal instability, and microbial sharing during cohousing in animal studies. Typically, samples will need to be stored, so we provide data on best practices for several sample types. We then discuss design and analysis of positive and negative controls, which should always be run with experimental samples. We introduce a convenient set of non-biological DNA sequences that can be useful as positive controls for high-volume analysis. Careful analysis of negative and positive controls is particularly important in studies of samples with low microbial biomass, where contamination can comprise most or all of a sample. Lastly, we summarize approaches to enhancing experimental robustness by careful control of multiple comparisons and to comparing discovery and validation cohorts. We hope the experimental tactics summarized here will help researchers in this exciting field advance their studies efficiently while avoiding errors.

Considerations For Optimizing Microbiome Analysis Using a Marker Gene

Next-generation sequencing technologies have found a widespread use in the study of host–microbe interactions due to the increase in their throughput and their ever-decreasing costs. The analysis of human-associated microbial communities using a marker gene, particularly the 16S rRNA, has been greatly benefited from these technologies – the human gut microbiome research being a remarkable example of such analysis that has greatly expanded our understanding of microbe-mediated human health and disease, metabolism, and food absorption. 16S studies go through a series of in vitro and in silico steps that can greatly influence their outcomes. However, the lack of a standardized workflow has led to uncertainties regarding the transparency and reproducibility of gut microbiome studies. We, here, discuss the most common challenges in the archetypical 16S rRNA workflow, including the extraction of total DNA, its use as template in PCR with primers that amplify specific hypervariable regions of the gene, amplicon sequencing, the denoising and removal of low-quality reads, the detection and removal of chimeric sequences, the clustering of high-quality sequences into operational taxonomic units, and their taxonomic classification. We recommend the essential technical information that should be conveyed in publications for reproducibility of results and encourage non-experts to include procedures and available tools that mitigate most of the problems encountered in microbiome analysis.

Sensitivity of Next-Generation Sequencing Metagenomic Analysis for Detection of RNA and DNA Viruses in Cerebrospinal Fluid: The Confounding Effect of Background Contamination

Next-generation sequencing (NGS) followed by metagenomic enables the detection and identification of known as well as novel pathogens. It could be potentially useful in the diagnosis of encephalitis, caused by a variety of microorganisms. The aim of the present study was to evaluate the sensitivity of isothermal RNA amplification (Ribo-SPIA) followed by NGS metagenomic analysis in the detection of human immunodeficiency virus (HIV) and herpes simplex virus (HSV) in cerebrospinal fluid (CSF). Moreover, we analyzed the contamination background. We detected 102 HIV copies and 103 HSV copies. The analysis of control samples (two water samples and one CSF sample from an uninfected patient) revealed the presence of human DNA in the CSF sample (91 % of all reads), while the dominating sequences in water were qualified as 'other', related to plants, plant viruses, and synthetic constructs, and constituted 31 % and 60 % of all reads. Bacterial sequences represented 5.9 % and 21.4 % of all reads in water samples and 2.3 % in the control CSF sample. The bacterial sequences corresponded mainly to Psychrobacter, Acinetobacter, and Corynebacterium genera. In conclusion, Ribo-SPIA amplification followed by NGS metagenomic analysis is sensitive for detection of RNA and DNA viruses. Contamination seems common and thus the results should be confirmed by other independent methods such as RT-PCR and PCR. Despite these reservations, NGS seems to be a promising method for the diagnosis of viral infections.

Inherent bacterial DNA contamination of extraction and sequencing reagents may affect interpretation of microbiota in low bacterial biomass samples

Background

The advent and use of highly sensitive molecular biology techniques to explore the microbiota and microbiome in environmental and tissue samples have detected the presence of contaminating microbial DNA within reagents. These microbial DNA contaminants may distort taxonomic distributions and relative frequencies in microbial datasets, as well as contribute to erroneous interpretations and identifications.

Results

We herein report on the occurrence of bacterial DNA contamination within commonly used DNA extraction kits and PCR reagents and the effect of these contaminates on data interpretation. When compared to previous reports, we identified an additional 88 bacterial genera as potential contaminants of molecular biology grade reagents, bringing the total number of known contaminating microbes to 181 genera. Many of the contaminants detected are considered normal inhabitants of the human gastrointestinal tract and the environment and are often indistinguishable from those genuinely present in the sample.

Conclusions

Laboratories working on bacterial populations need to define contaminants present in all extraction kits and reagents used in the processing of DNA. Any unusual and/or unexpected findings need to be viewed as possible contamination as opposed to unique findings.

Electronic supplementary material

The online version of this article (doi:10.1186/s13099-016-0103-7) contains supplementary material, which is available to authorized users.

An accurate and efficient experimental approach for characterization of the complex oral microbiota

BACKGROUND

Currently, taxonomic interrogation of microbiota is based on amplification of 16S rRNA gene sequences in clinical and scientific settings. Accurate evaluation of the microbiota depends heavily on the primers used, and genus/species resolution bias can arise with amplification of non-representative genomic regions. The latest Illumina MiSeq sequencing chemistry has extended the read length to 300 bp, enabling deep profiling of large number of samples in a single paired-end reaction at a fraction of the cost. An increasingly large number of researchers have adopted this technology for various microbiome studies targeting the 16S rRNA V3-V4 hypervariable region.

RESULTS

To expand the applicability of this powerful platform for further descriptive and functional microbiome studies, we standardized and tested an efficient, reliable, and straightforward workflow for the amplification, library construction, and sequencing of the 16S V1-V3 hypervariable region using the new 2 × 300 MiSeq platform. Our analysis involved 11 subgingival plaque samples from diabetic and non-diabetic human subjects suffering from periodontitis. The efficiency and reliability of our experimental protocol was compared to 16S V3-V4 sequencing data from the same samples. Comparisons were based on measures of observed taxonomic richness and species evenness, along with Procrustes analyses using beta(β)-diversity distance metrics. As an experimental control, we also analyzed a total of eight technical replicates for the V1-V3 and V3-V4 regions from a synthetic community with known bacterial species operon counts. We show that our experimental protocol accurately measures true bacterial community composition. Procrustes analyses based on unweighted UniFrac β-diversity metrics depicted significant correlation between oral bacterial composition for the V1-V3 and V3-V4 regions. However, measures of phylotype richness were higher for the V1-V3 region, suggesting that V1-V3 offers a deeper assessment of population diversity and community ecology for the complex oral microbiota.

CONCLUSION

This study provides researchers with valuable experimental evidence for the selection of appropriate 16S amplicons for future human oral microbiome studies. We expect that the tested 16S V1-V3 framework will be widely applicable to other types of microbiota, allowing robust, time-efficient, and inexpensive examination of thousands of samples for population, phylogenetic, and functional crossectional and longitutidal studies.

Reagent and laboratory contamination can critically impact sequence-based microbiome analyses

Background

The study of microbial communities has been revolutionised in recent years by the widespread adoption of culture independent analytical techniques such as 16S rRNA gene sequencing and metagenomics. One potential confounder of these sequence-based approaches is the presence of contamination in DNA extraction kits and other laboratory reagents.

Results

In this study we demonstrate that contaminating DNA is ubiquitous in commonly used DNA extraction kits and other laboratory reagents, varies greatly in composition between different kits and kit batches, and that this contamination critically impacts results obtained from samples containing a low microbial biomass. Contamination impacts both PCR-based 16S rRNA gene surveys and shotgun metagenomics. We provide an extensive list of potential contaminating genera, and guidelines on how to mitigate the effects of contamination.

Conclusions

These results suggest that caution should be advised when applying sequence-based techniques to the study of microbiota present in low biomass environments. Concurrent sequencing of negative control samples is strongly advised.

Electronic supplementary material

The online version of this article (doi:10.1186/s12915-014-0087-z) contains supplementary material, which is available to authorized users.

Decomposition of waste DNA with extended autoclaving under unsaturated steam

Carryover and false-positive amplification of undesired DNA sequences are serious problems in research and diagnostic testing using PCR. One possible source of DNA cross-contamination can be the autoclave if DNA contained in waste is not effectively decomposed and contaminates the autoclave. To assess this possibility, we used a 2682 bp PCR product as a model waste DNA and quantified the amplifiability of an 84 bp short fragment derived from the model waste DNA in the steam and the residual bottom water after autoclaving. Autoclaving under the standard conditions of 121°C for 20 min did not sufficiently remove amplifiability from the model DNA and was found to be a possible source of laboratory contamination. However, the amplifiable template was removed after autoclaving at 121°C for 80 min. Fragmentation and hydrolysis may occur during autoclaving, and the presence of atmospheric oxygen facilitated the decomposition. These findings will help researchers develop better strategies for disposing of DNA waste.

Evaluation of methods to concentrate and purify ocean virus communities through comparative, replicated metagenomics

Viruses have global impact through mortality, nutrient cycling and horizontal gene transfer, yet their study is limited by complex methodologies with little validation. Here, we use triplicate metagenomes to compare common aquatic viral concentration and purification methods across four combinations as follows: (i) tangential flow filtration (TFF) and DNase + CsCl, (ii) FeCl₃ precipitation and DNase, (iii) FeCl₃ precipitation and DNase + CsCl and (iv) FeCl₃ precipitation and DNase + sucrose. Taxonomic data (30% of reads) suggested that purification methods were statistically indistinguishable at any taxonomic level while concentration methods were significantly different at family and genus levels. Specifically, TFF-concentrated viral metagenomes had significantly fewer abundant viral types (Podoviridae and Phycodnaviridae) and more variability among Myoviridae than FeCl₃-precipitated viral metagenomes. More comprehensive analyses using protein clusters (66% of reads) and k-mers (100% of reads) showed 50–53% of these data were common to all four methods, and revealed trace bacterial DNA contamination in TFF-concentrated metagenomes and one of three replicates concentrated using FeCl₃ and purified by DNase alone. Shared k-mer analyses also revealed that polymerases used in amplification impact the resulting metagenomes, with TaKaRa enriching for ‘rare’ reads relative to PfuTurbo. Together these results provide empirical data for making experimental design decisions in culture-independent viral ecology studies.

Reagent decontamination to eliminate false-positives in Escherichia coli qPCR

The application of real-time quantitative PCR (qPCR) for the detection of low concentrations of Escherichia coli as well as universal 16S rDNA has been hindered by false-positives due to endogenous contamination of PCR reagents with E. coli and other bacterial DNA. We optimized a DNase I decontamination method to eliminate false-positives in a qPCR assay targeting the uidA gene in E. coli. In contrast to previous methods reported in the literature, our decontamination method did not cause PCR inhibition. We determined that residual DNase I activity was the cause of the inhibition in the previous methods, and eliminated it by ensuring complete inactivation prior to qPCR. DNase inactivation was accomplished by adding dithiothreitol (DTT) and then heating for 30 min at 80 degrees C. The optimized DNase method was compared to another decontamination method, ultrafiltration, and to untreated controls. We detected contamination in 85% of the untreated commercial PCR master mix samples at a level of about 10 copies per well (12.5 microL of master mix). Both decontamination methods could eliminate up to 100 copies of added contaminant DNA and did not cause PCR inhibition, resulting in a reduction of the detection limit to 10 copies per reaction well.

Identification of bacteria from clinical samples using Bartonella alpha-Proteobacteria growth medium

In an effort to overcome historical problems associated with the isolation of Bartonella species from animal and human blood samples, our laboratory developed a novel, chemically modified, insect-based, liquid culture medium (Bartonella alpha-Proteobacteria growth medium, BAPGM). In this study, we describe the isolation of non-Bartonella bacteria from aseptically obtained human blood and tissue samples that were inoculated into BAPGM pre-enrichment culture medium, and were obtained during attempts to define each individuals Bartonella infection status. After incubation for at least 7 days in liquid BAPGM, pre-enriched inoculums were sub-cultured onto a BAPGM/blood agar plate. Bacterial DNA was extracted from pooled plated colonies and amplified using conventional PCR targeting the 16S rRNA gene. Subsequently, amplicons were cloned, sequenced and compared to GenBank database sequences using the BLAST program. Regardless of the patient's Bartonella status, seventeen samples generated only one 16S rDNA sequence, representing the following genera: Arthrobacter, Bacillus, Bartonella, Dermabacter, Methylobacterium, Propionibacterium, Pseudomonas, Staphylococcus and bacteria listed as "non-cultured" in the GenBank database. Alkalibacterium, Arthrobacter, Erwinia, Kineococcus, Methylobacterium, Propionibacterium, Sphingomonas, and Staphylococcus were isolated from nine Bartonella-infected individuals. Co-isolation of Acinetobacter, Sphingomonas, Staphylococcus spp. and bacteria listed as "non-cultured" in the GenBank database was achieved for four samples in which Bartonella spp. were not detected. Despite the phylogenetic limitations of using partial 16S rRNA gene sequencing for species and strain identification, the investigational methodology described in this study may provide a complementary approach for the isolation and identification of bacteria from patient samples.

Increased internal and external bacterial load during Drosophila aging without life-span trade-off

The role of microbial load during aging of the adult fruit fly Drosophila melanogaster is incompletely understood. Here we show dramatic increases in aerobic and anaerobic bacterial load during aging, both inside the body and on the surface. Scanning electron microscopy and cell staining analyses of the surface of aged flies detected structures resembling abundant small bacteria and bacterial biofilms. Bacteria cultured from laboratory flies included aerobic species Acetobacter aceti, Acetobacter tropicalis, and Acetobacter pasteurianus and anaerobic species Lactobacillus plantarum and Lactobacillus sp. MR-2; Lactobacillus homohiochii, Lactobacillus fructivorans, and Lactobacillus brevis were identified by DNA sequencing. Reducing bacterial load and antimicrobial peptide gene expression by axenic culture or antibiotics had no effect on life span. We conclude that Drosophila can tolerate a significant bacterial load and mount a large innate immune response without a detectable trade-off with life span; furthermore, microbes do not seem to limit life span under optimized laboratory conditions.