Low-biomass Microbiome Research Articles

Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.

As investigations of low-biomass microbial communities have become more common, so too has the recognition of major challenges affecting these analyses. These challenges have been shown to compromise biological conclusions and have contributed to several controversies. Here, we review some of the most common and influential challenges in low-biomass microbiome research. We highlight key approaches to alleviate these potential pitfalls, combining experimental planning strategies and data analysis methods.

Intervertebral disc microbiome in Modic changes: Lack of result replication underscores the need for a consensus in low-biomass microbiome analysis.

The emerging field of the disc microbiome challenges traditional views of disc sterility, which opens new avenues for novel clinical insights. However, the lack of methodological consensus in disc microbiome studies introduces discrepancies. The aims of this study were to (1) compare the disc microbiome of non-Modic (nonMC), Modic type 1 change (MC1), and MC2 discs to findings from prior disc microbiome studies, and (2) investigate if discrepancies to prior studies can be explained with bioinformatic variations. Sequencing of 16S rRNA in 70 discs (24 nonMC, 25 MC1, and 21 MC2) for microbiome profiling. The experimental setup included buffer contamination controls and was performed under aseptic conditions. Methodology and results were contrasted with previous disc microbiome studies. Critical bioinformatic steps that were different in our best-practice approach and previous disc microbiome studies (taxonomic lineage assignment, prevalence cut-off) were varied and their effect on results were compared. There was limited overlap of results with a previous study on MC disc microbiome. No bacterial genera were shared using the same bioinformatic parameters. Taxonomic lineage assignment using "amplicon sequencing variants" was more sensitive and detected 48 genera compared to 22 with "operational taxonomic units" (previous study). Increasing filter cut-off from 4% to 50% (previous study) reduced genera from 48 to 4 genera. Despite these differences, both studies observed dysbiosis with an increased abundance of gram-negative bacteria in MC discs as well as a lower beta-diversity. Cutibacterium was persistently detected in all groups independent of the bioinformatic approach, emphasizing its prevalence. There is dysbiosis in MC discs. Bioinformatic parameters impact results yet cannot explain the different findings from this and a previous study. Therefore, discrepancies are likely caused by different sample preparations or true biologic differences. Harmonized protocols are required to advance understanding of the disc microbiome and its clinical implications.

Correlation between the facial skin microbiome and sensitive skin using the 2bRAD-M technique.

This study aimed to expound on the correlation between facial skin microbiome and sensitive skin (SS) using a novel sequencing technique. We applied the 2bRAD sequencing for the microbiome, which enables accurate characterization of the low-biomass microbiome at species resolution to profile facial skin microbes in SS and non-SS groups. Further, the bacterial colonies were isolated and cultured from skin surfaces to study the pro-inflammatory effect on human keratinocytes by ELISA. We accordingly identified 1142 genera and 4436 strains. In the SS group, the proportions of Actinomyces and Microbotryomycetes were significantly increased, whereas that of Acidimicrobiia was decreased. Kruskal-Wallis analysis revealed significant differences in 11 genera and 35 species, among which the proportions of Dermabacter, Chryseobacterium, Rhodotorula and Peptoniphilus A were increased in the SS group. Analysis of the top 10 genera revealed increased proportions of Cutibacterium, Corynebacterium and Staphylococcus. Moreover, the proportion of Dermabacter hominis was significantly increased by 18.9-fold in the SS group, whereas those of many Streptococcus strains were significantly decreased. Focus on the isolated bacterial colonies from skin surfaces, more yellow colonies were found in SS group when cultured in Tryptic Soy Broth medium for 48 h, and more interleukin-8 was detected on keratinocytes after yellow colonies stimulation, such as S.capitis, M.luteus. This study suggests that more SS-associated microorganisms can be identified using the 2bRAD technique even with a small sample size. Dermabacter hominis and Chryseobacterium was firstly reported with a significantly increase in SS, and the S.capitis, as well as M.luteus, but not S.aureus, may be associated with skin inflammation.

2bRAD-M reveals the difference in microbial distribution between cancerous and benign ovarian tissues.

The development of ovarian cancer is closely related to various factors, such as environmental, genetic and microbiological factors. In previous research, bacteria were identified in human tumors by 16S rRNA sequencing. However, the microbial biomass in tumor tissue is too low and cannot be accurately identified by 16S rRNA sequencing. In our study, we employ 2bRAD sequencing for Microbiome (2bRAD-M), a new sequencing technology capable of accurately characterizing the low biomass microbiome (bacteria, fungi and archaea) at species resolution. Here we surveyed 20 ovarian samples, including 10 ovarian cancer samples and 10 benign ovarian samples. The sequencing results showed that a total of 373 microbial species were identified in both two groups, of which 90 species shared in the two groups. The Meta statistic indicated that Chlamydophila_abortus and CAG-873_sp900550395 were increased in the ovarian cancer tissues, while Lawsonella_clevelandensis_A, Ralstonia_sp001078575, Brevundimonas_aurantiaca, Ralstonia_sp900115545, Ralstonia_pickettii, Corynebacterium_kefirresidentii, Corynebacterium_sp000478175, Brevibacillus_D_fluminis, Ralstonia_sp000620465, and Ralstonia_mannitolilytica were more abundant in the benign ovarian tissues. This is the first use of 2bRAD-M technique to provide an important hint for better understanding of the ovarian cancer microbiome.

Circulating Microbial Cell-Free DNA in Health and Disease.

Human blood contains low biomass of circulating microbial cell-free DNA (cfmDNA) that predominantly originates from bacteria. Numerous studies have detected circulating cfmDNA in patients with infectious and non-infectious diseases, and in healthy individuals. Remarkable differences were found in the microbial composition of healthy subjects and patients compared to cohorts with various diseases or even patients with diversified prognoses, implying that these alterations may be associated with disease development. Although the function of circulating cfmDNA needs to be elucidated (whether it acts as a bystander of dysbiosis or a key player in disease development), several studies have demonstrated its potential as a non-invasive biomarker that may improve diagnosis and treatment efficacy. The origin of circulating cfmDNA is still the subject of much deliberation, but studies have identified members of various microbiome niches, including the gut, oral cavity, airways, and skin. Further studies investigating the origin and function of circulating cfmDNA are needed. Moreover, low-biomass microbiome studies are prone to contamination, therefore stringent negative experimental control reactions and decontamination frameworks are advised in order to detect genuine circulating cfmDNA.

The renal pelvis urobiome in the unilateral kidney stone patients revealed by 2bRAD-M

BackgroundThe pathogenesis of kidney stone disease (KSD) is not fully understood, and potential contributing factors remain to be explored. Several studies have revealed that the urinary microbiome (urobiome) of stone formers was distinct from that of healthy individuals using 16S rRNA gene sequencing, most of which only provided microbial identification at the genus level. 2bRAD sequencing for Microbiome (2bRAD-M) is a novel sequencing technique that enables accurate characterization of the low-biomass microbiome at the species resolution. We aimed to apply 2bRAD-M to profile the renal pelvis urobiome of unilateral kidney stone patients and compared the urobiome with and without stone(s).MethodA total of 30 patients with unilateral stones were recruited, and their renal pelvis urine from both sides was collected. A ureteroscope was inserted into the renal pelvis with stone(s) and a ureteral catheter was placed into the ureteroscope to collect renal pelvis urine. This procedure was repeated again with new devices to collect the urine of the other side. 2bRAD-M was performed to characterize the renal pelvis urobiome of unilateral stone formers to explore whether microbial differences existed between the stone side and the non-stone side.ResultsThe microbial community composition of the stone side was similar to that of the non-stone side. Paired comparison showed that Corynebacterium was increased and Prevotella and Lactobacillus were decreased in the stone side. Four species (Prevotella bivia, Lactobacillus iners, Corynebacterium aurimucosum, and Pseudomonas sp_286) were overrepresented in the non-stone side. 24 differential taxa were also identified between two groups by linear discriminant analysis effect size (LEfSe). Extensive and close connections among genera and species were observed in the correlation analysis. Moreover, a random forest classifier was constructed using specific enriched species, which can distinguish the stone side from the non-stone side with an accuracy of 71.2%.ConclusionThis first 2bRAD-M microbiome survey gave an important hint towards the potential role of urinary dysbiosis in KSD and provided a better understanding of mechanism of stone formation.

The Role of Microbiota in the Immunopathogenesis of Endometrial Cancer.

The female reproductive tract hosts a specific microbiome, which plays a crucial role in sustaining equilibrium and good health. In the majority of reproductive women, the microbiota (all bacteria, viruses, fungi, and other single-celled organisms within the human body) of the vaginal and cervical microenvironment are dominated by Lactobacillus species, which benefit the host through symbiotic relationships, in comparison to the uterus, fallopian tubes, and ovaries, which may contain a low-biomass microbiome with a diverse mixture of microorganisms. Although disruption to the balance of the microbiota develops, the altered immune and metabolic signaling may cause an impact on diseases such as cancer. These pathophysiological modifications in the gut–uterus axis may spark gynecological cancers. New information displays that gynecological and gastrointestinal tract dysbiosis (disruption of the microbiota homeostasis) can play an active role in the advancement and metastasis of gynecological neoplasms, such as cervical, endometrial, and ovarian cancers. Understanding the relationship between microbiota and endometrial cancer is critical for prognosis, diagnosis, prevention, and the development of innovative treatments. Identifying a specific microbiome may become an effective method for characterization of the specific microbiota involved in endometrial carcinogenesis. The aim of this study was to summarize the current state of knowledge that describes the correlation of microbiota with endometrial cancer with regard to the formation of immunological pathologies.

Benchmarking laboratory processes to characterise low-biomass respiratory microbiota

The low biomass of respiratory samples makes it difficult to accurately characterise the microbial community composition. PCR conditions and contaminating microbial DNA can alter the biological profile. The objective of this study was to benchmark the currently available laboratory protocols to accurately analyse the microbial community of low biomass samples. To study the effect of PCR conditions on the microbial community profile, we amplified the 16S rRNA gene of respiratory samples using various bacterial loads and different number of PCR cycles. Libraries were purified by gel electrophoresis or AMPure XP and sequenced by V2 or V3 MiSeq reagent kits by Illumina sequencing. The positive control was diluted in different solvents. PCR conditions had no significant influence on the microbial community profile of low biomass samples. Purification methods and MiSeq reagent kits provided nearly similar microbiota profiles (paired Bray–Curtis dissimilarity median: 0.03 and 0.05, respectively). While profiles of positive controls were significantly influenced by the type of dilution solvent, the theoretical profile of the Zymo mock was most accurately analysed when the Zymo mock was diluted in elution buffer (difference compared to the theoretical Zymo mock: 21.6% for elution buffer, 29.2% for Milli-Q, and 79.6% for DNA/RNA shield). Microbiota profiles of DNA blanks formed a distinct cluster compared to low biomass samples, demonstrating that low biomass samples can accurately be distinguished from DNA blanks. In summary, to accurately characterise the microbial community composition we recommend 1. amplification of the obtained microbial DNA with 30 PCR cycles, 2. purifying amplicon pools by two consecutive AMPure XP steps and 3. sequence the pooled amplicons by V3 MiSeq reagent kit. The benchmarked standardized laboratory workflow presented here ensures comparability of results within and between low biomass microbiome studies.

Detection of cell-free microbial DNA using a contaminant-controlled analysis framework

BackgroundThe human microbiome plays an important role in cancer. Accumulating evidence indicates that commensal microbiome-derived DNA may be represented in minute quantities in the cell-free DNA of human blood and could possibly be harnessed as a new cancer biomarker. However, there has been limited use of rigorous experimental controls to account for contamination, which invariably affects low-biomass microbiome studies.ResultsWe apply a combination of 16S-rRNA-gene sequencing and droplet digital PCR to determine if the specific detection of cell-free microbial DNA (cfmDNA) is possible in metastatic melanoma patients. Compared to matched stool and saliva samples, the absolute concentration of cfmDNA is low but significantly above the levels detected from negative controls. The microbial community of plasma is strongly influenced by laboratory and reagent contaminants introduced during the DNA extraction and sequencing processes. Through the application of an in silico decontamination strategy including the filtering of amplicon sequence variants (ASVs) with batch dependent abundances and those with a higher prevalence in negative controls, we identify known gut commensal bacteria, such as Faecalibacterium, Bacteroides and Ruminococcus, and also other uncharacterised ASVs. We analyse additional plasma samples, highlighting the potential of this framework to identify differences in cfmDNA between healthy and cancer patients.ConclusionsTogether, these observations indicate that plasma can harbour a low yet detectable level of cfmDNA. The results highlight the importance of accounting for contamination and provide an analytical decontamination framework to allow the accurate detection of cfmDNA for future biomarker studies in cancer and other diseases.

Ultra-accurate microbial amplicon sequencing with synthetic long reads

BackgroundOut of the many pathogenic bacterial species that are known, only a fraction are readily identifiable directly from a complex microbial community using standard next generation DNA sequencing. Long-read sequencing offers the potential to identify a wider range of species and to differentiate between strains within a species, but attaining sufficient accuracy in complex metagenomes remains a challenge.MethodsHere, we describe and analytically validate LoopSeq, a commercially available synthetic long-read (SLR) sequencing technology that generates highly accurate long reads from standard short reads.ResultsLoopSeq reads are sufficiently long and accurate to identify microbial genes and species directly from complex samples. LoopSeq perfectly recovered the full diversity of 16S rRNA genes from known strains in a synthetic microbial community. Full-length LoopSeq reads had a per-base error rate of 0.005%, which exceeds the accuracy reported for other long-read sequencing technologies. 18S-ITS and genomic sequencing of fungal and bacterial isolates confirmed that LoopSeq sequencing maintains that accuracy for reads up to 6 kb in length. LoopSeq full-length 16S rRNA reads could accurately classify organisms down to the species level in rinsate from retail meat samples, and could differentiate strains within species identified by the CDC as potential foodborne pathogens.ConclusionsThe order-of-magnitude improvement in length and accuracy over standard Illumina amplicon sequencing achieved with LoopSeq enables accurate species-level and strain identification from complex- to low-biomass microbiome samples. The ability to generate accurate and long microbiome sequencing reads using standard short read sequencers will accelerate the building of quality microbial sequence databases and removes a significant hurdle on the path to precision microbial genomics.4PHa9SHy5rS1yXExpPUL7_Video abstract.

Whole lung tissue is the preferred sampling method for amplicon-based characterization of murine lung microbiota

BackgroundLow-biomass microbiome studies (such as those of the lungs, placenta, and skin) are vulnerable to contamination and sequencing stochasticity, which obscure legitimate microbial signal. While human lung microbiome studies have rigorously identified sampling strategies that reliably capture microbial signal from these low-biomass microbial communities, the optimal sampling strategy for characterizing murine lung microbiota has not been empirically determined. Performing accurate, reliable characterization of murine lung microbiota and distinguishing true microbial signal from noise in these samples will be critical for further mechanistic microbiome studies in mice.ResultsUsing an analytic approach grounded in microbial ecology, we compared bacterial DNA from the lungs of healthy adult mice collected via two common sampling approaches: homogenized whole lung tissue and bronchoalveolar lavage (BAL) fluid. We quantified bacterial DNA using droplet digital PCR, characterized bacterial communities using 16S rRNA gene sequencing, and systematically assessed the quantity and identity of bacterial DNA in both specimen types. We compared bacteria detected in lung specimens to each other and to potential source communities: negative (background) control specimens and paired oral samples. By all measures, whole lung tissue in mice contained greater bacterial signal and less evidence of contamination than did BAL fluid. Relative to BAL fluid, whole lung tissue exhibited a greater quantity of bacterial DNA, distinct community composition, decreased sample-to-sample variation, and greater biological plausibility when compared to potential source communities. In contrast, bacteria detected in BAL fluid were minimally different from those of procedural, reagent, and sequencing controls.ConclusionsAn ecology-based analytical approach discriminates signal from noise in this low-biomass microbiome study and identifies whole lung tissue as the preferred specimen type for murine lung microbiome studies. Sequencing, analysis, and reporting of potential source communities, including negative control specimens and contiguous biological sites, are crucial for biological interpretation of low-biomass microbiome studies, independent of specimen type.Aaejar9WP4PeeefSRNqdytVideo abstract

Multiomics analysis reveals the presence of a microbiome in the gut of fetal lambs

ObjectiveMicrobial exposure is critical to neonatal and infant development, growth and immunity. However, whether a microbiome is present in the fetal gut prior to birth remains debated. In this study,...

Repeatability and reproducibility assessment in a large-scale population-based microbiota study: case study on human milk microbiota

BackgroundQuality control including assessment of batch variabilities and confirmation of repeatability and reproducibility are integral component of high throughput omics studies including microbiome research. Batch effects can mask true biological results and/or result in irreproducible conclusions and interpretations. Low biomass samples in microbiome research are prone to reagent contamination; yet, quality control procedures for low biomass samples in large-scale microbiome studies are not well established.ResultsIn this study, we have proposed a framework for an in-depth step-by-step approach to address this gap. The framework consists of three independent stages: (1) verification of sequencing accuracy by assessing technical repeatability and reproducibility of the results using mock communities and biological controls; (2) contaminant removal and batch variability correction by applying a two-tier strategy using statistical algorithms (e.g. decontam) followed by comparison of the data structure between batches; and (3) corroborating the repeatability and reproducibility of microbiome composition and downstream statistical analysis. Using this approach on the milk microbiota data from the CHILD Cohort generated in two batches (extracted and sequenced in 2016 and 2019), we were able to identify potential reagent contaminants that were missed with standard algorithms and substantially reduce contaminant-induced batch variability. Additionally, we confirmed the repeatability and reproducibility of our results in each batch before merging them for downstream analysis.ConclusionThis study provides important insight to advance quality control efforts in low biomass microbiome research. Within-study quality control that takes advantage of the data structure (i.e. differential prevalence of contaminants between batches) would enhance the overall reliability and reproducibility of research in this field.Dksa_EC4VEZpmNY5pbL1BCVideo abstract

Evidence for contamination as the origin for bacteria found in human placenta rather than a microbiota.

Until recently the in utero environment of pregnant women was considered sterile. Recent high-sensitivity molecular techniques and high-throughput sequencing lead to some evidence for a low-biomass microbiome associated with the healthy placenta. Other studies failed to reveal evidence for a consistent presence of bacteria using either culture or molecular based techniques. Comparing conflicting “placental microbiome” studies is complicated by the use of varied and inconsistent protocols. Given this situation, we undertook an evaluation of the in utero environment sterility using several controlled methods, in the same study, to evaluate the presence or absence of bacteria and to explain contradictions present in the literature. Healthy pregnant women (n = 38) were recruited in three maternity wards. Placenta were collected after cesarean section with or without Alexis® and vaginal delivery births. For this study we sampled fetal membranes, umbilical cord and chorionic villi. Bacterial presence was analyzed using bacterial culture and qPCR on 34 fetal membranes, umbilical cord and chorionic villi samples. Shotgun metagenomics was performed on seven chorionic villi samples. We showed that the isolation of meaningful quantities of viable bacteria or bacterial DNA was possible only outside the placenta (fetal membranes and umbilical cords) highlighting the importance of sampling methods in studying the in utero environment. Bacterial communities described by metagenomics analysis were similar in chorionic villi samples and in negative controls and were dependent on the database chosen for the analysis. We conclude that the placenta does not harbor a specific, consistent and functional microbiota.

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.

Abstract IA14: Characterizing the tumor microbiome and its effects on response to therapy

Abstract The presence of bacteria in human solid tumors has been documented for over 100 years. Still, it is only in recent years that a more thorough characterization of this low-biomass microbiome has been pursued. In my lecture, I will describe our efforts to characterize the type of bacteria present across multiple human solid tumors as well as their intratumor location. I will also describe our efforts to study the effect that intratumor bacteria may have on different tumor phenotypes in general and on response to therapy in particular. Citation Format: Ravid Straussman. Characterizing the tumor microbiome and its effects on response to therapy [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr IA14.

The microbiome and gynaecological cancer development, prevention and therapy.

The female reproductive tract (FRT), similar to other mucosal sites, harbours a site-specific microbiome, which has an essential role in maintaining health and homeostasis. In the majority of women of reproductive age, the microbiota of the lower FRT (vagina and cervix) microenvironment is dominated by Lactobacillus species, which benefit the host through symbiotic relationships. By contrast, the upper FRT (uterus, Fallopian tubes and ovaries) might be sterile in healthy individuals or contain a low-biomass microbiome with a diverse mixture of microorganisms. When dysbiosis occurs, altered immune and metabolic signalling can affect hallmarks of cancer, including chronic inflammation, epithelial barrier breach, changes in cellular proliferation and apoptosis, genome instability, angiogenesis and metabolic dysregulation. These pathophysiological changes might lead to gynaecological cancer. Emerging evidence shows that genital dysbiosis and/or specific bacteria might have an active role in the development and/or progression and metastasis of gynaecological malignancies, such as cervical, endometrial and ovarian cancers, through direct and indirect mechanisms, including modulation of oestrogen metabolism. Cancer therapies might also alter microbiota at sites throughout the body. Reciprocally, microbiota composition can influence the efficacy and toxic effects of cancer therapies, as well as quality of life following cancer treatment. Modulation of the microbiome via probiotics or microbiota transplant might prove useful in improving responsiveness to cancer treatment and quality of life. Elucidating these complex host-microbiome interactions, including the crosstalk between distal and local sites, will translate into interventions for prevention, therapeutic efficacy and toxic effects to enhance health outcomes for women with gynaecological cancers.

Endometrial microbiota - do they mean more than we have expected?

Low biomass microbiome has an increasing importance in today's fertility studies. There are more and more indications for incorporating upper gynecological tract microbiome content in patients diagnostic and in vitro fertilization process, as doing so may help to evaluate chances for a positive outcome. An abnormal endometrial microbiota has been associated with implantation failure, pregnancy loss, and other gynecological and obstetrical conditions. Furthermore it has been shown, that using molecular methods in addition to routine diagnostics may help diagnose chronic endometritis or even indicate cancerogenic changes. Understanding the significance of microbiome in endometrium may completely change therapeutic approach in treatment of this part of reproductive tract. Next generation sequencing (NGS) has allowed to isolate culturable and unculturable bacteria from female reproductive tract and is a cheaper and quicker alternative for other widely known and used methods.

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.

Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations.

Next-generation sequencing approaches in microbiome research have allowed surveys of microbial communities, their genomes, and their functions with higher sensitivity than ever before. However, this sensitivity is a double-edged sword because these tools also efficiently detect contaminant DNA and cross-contamination, which can confound the interpretation of microbiome data. Therefore, there is an urgent need to integrate key controls into microbiome research to improve the integrity of microbiome studies. Here, we review how contaminant DNA and cross-contamination arise within microbiome studies and discuss their negative impacts, especially during the analysis of low microbial biomass samples. We then identify several key measures that researchers can implement to reduce the impact of contaminant DNA and cross-contamination during microbiome research. We put forward a set of minimal experimental criteria, the 'RIDE' checklist, to improve the validity of future low microbial biomass research.