Systematic Bias Introduced by Genomic DNA Template Dilution in 16S rRNA Gene-Targeted Microbiota Profiling in Human Stool Homogenates

The Skin Microbiome: Current Techniques, Challenges, and Future Directions

Skin acts as a barrier that promotes the colonization of bacteria, fungi, archaea, and viruses whose membership and function may differ depending on the various specialized niches or micro-environments of the skin. The group of microorganisms inhabiting the skin, also known as the skin microbiome, offers protection against pathogens while actively interacting with the host's immune system. Some members of the skin microbiome can also act as opportunistic pathogens. The skin microbiome is influenced by factors such as skin site, birth mode, genetics, environment, skin products, and skin conditions. The association(s) of the skin microbiome with health and disease has (have) been identified and characterized via culture-dependent and culture-independent methods. Culture-independent methods (such as high-throughput sequencing), in particular, have expanded our understanding of the skin microbiome's role in maintaining health or promoting disease. However, the intrinsic challenges associated with the low microbial biomass and high host content of skin microbiome samples have hindered advancements in the field. In addition, the limitations of current collection and extraction methods and biases derived from sample preparation and analysis have significantly influenced the results and conclusions of many skin microbiome studies. Therefore, the present review discusses the technical challenges associated with the collection and processing of skin microbiome samples, the advantages and disadvantages of current sequencing approaches, and potential future areas of focus for the field.

Possibilities and limitations of using low biomass samples for urologic disease and microbiome research

With the dogma of sterile urine no longer held as truth, numerous studies have implicated distinct changes in microbial diversity and composition to diseased subgroups in both benign and malignant urological diseases, ranging from overactive bladder to bladder and prostate cancer. Further facilitated by novel and effective techniques of urine culture and sequencing, analysis of the genitourinary microbiome holds high potential to identify biomarkers for disease and prognosis. However, the low biomass of samples included in microbiome studies of the urinary tract challenge researchers to draw definitive conclusions, confounded by technical and procedural considerations that must be addressed. Lack of samples and adequate true negative controls can lead to overestimation of microbial influence with clinical relevance. As such, results from currently available studies and assessment of their limitations required a thorough understanding. The purpose of this narrative review was to summarize notable microbiome studies in the field of urology with a focus on significant findings and limitations of study design. Methodological considerations in future research are also discussed.

The Effects of DNA Extraction Kits and Primers on Prokaryotic and Eukaryotic Microbial Community in Freshwater Sediments

DNA based sequencing technology has revolutionized the field of microbial ecology and environmental studies. However, biases can be introduced at all experimental steps and, thus, affect the interpretation of microbial community. So far, previous studies on the biases introduced from the key steps of DNA extraction and primer sets mainly focused on the bacterial communities in soil or sediment samples, while little is known about the effect on the eukaryotic microbial communities. Here, we studied the effects of three different DNA extraction kits on both prokaryotic and micro-eukaryotic communities by 16S and 18S rRNA gene amplicon sequencing, and further disentangled the influence of primer choice on the micro-eukaryotic communities. Our results showed that the FastDNA SPIN Kit for Soil and DNeasy PowerSoil Kit produced much higher DNA yield with good reproducibility, and observed more eukaryotic OTUs compared to the MinkaGene DNA extraction kit, but all three kits exhibited comparable ability in recovering bacterial alpha diversity. Of the two primer sets, both targeting the V4 region of the 18S rRNA gene, the TAR primer set detected higher number of unique OTUs than the EK primer set, while the EK primer set resulted in longer amplicons and better reproducibility between replicates. Based on our findings, we recommend using the DNeasy PowerSoil Kit with the EK primer set to capture the abundant micro-eukaryotic taxa from freshwater sediment samples. If a more complete picture of the eukaryotic microbial community is desired, the TAR primer set in combination with the FastDNA SPIN Kit is more efficient in this study.

Effect of storage temperature and flash-freezing on salivary microbiota profiles based on 16S rRNA-targeted sequencing

The sample storage environment affects gut microbial profiles as assessed using 16S rRNA sequencing. However, the influence of storage condition on human salivary microbial profiles has not been well characterized. Here, we performed an observational study to assess the robustness of microbiota profiles in three different storage environments (-80°C after flash-freezing, -80°C, and -15°C; all for 14 days) compared to immediate DNA extraction using the MiSeq Illumina platform. Notably, the 16S rRNA V1-V2 region amplicon sequencing revealed no difference in microbiota profiles between the immediate extraction and each of three storage conditions. An almost perfect correlation was shown between the immediate extraction and the -15°C storage group for relative abundance at the genus and operational taxonomic unit levels. The intraindividual UniFrac distances among storage methods were significantly shorter than those of interindividual distances. None of the amount of extracted DNA, the α-diversity indices, or the relative abundance at the phylum/genus/operational taxonomic unit level differed among storage methods. These findings indicate that a storage temperature of -15°C without flash-freezing may be optimal in terms of cost advantage and simplicity in 16S rRNA sequencing-based salivary microbial research.

ddPCR allows 16S rRNA gene amplicon sequencing of very small DNA amounts from low-biomass samples

BACKGROUND

One limiting factor of short amplicon 16S rRNA gene sequencing approaches is the use of low DNA amounts in the amplicon generation step. Especially for low-biomass samples, insufficient or even commonly undetectable DNA amounts can limit or prohibit further analysis in standard protocols.

RESULTS

Using a newly established protocol, very low DNA input amounts were found sufficient for reliable detection of bacteria using 16S rRNA gene sequencing compared to standard protocols. The improved protocol includes an optimized amplification strategy by using a digital droplet PCR. We demonstrate how PCR products are generated even when using very low concentrated DNA, unable to be detected by using a Qubit. Importantly, the use of different 16S rRNA gene primers had a greater effect on the resulting taxonomical profiles compared to using high or very low initial DNA amounts.

CONCLUSION

Our improved protocol takes advantage of ddPCR and allows faithful amplification of very low amounts of template. With this, samples of low bacterial biomass become comparable to those with high amounts of bacteria, since the first and most biasing steps are the same. Besides, it is imperative to state DNA concentrations and volumes used and to include negative controls indicating possible shifts in taxonomical profiles. Despite this, results produced by using different primer pairs cannot be easily compared.

Building up a clinical microbiota profiling: a quality framework proposal

Extensive characterization of the human microbiota has revealed promising relationships between microbial composition and health or disease, generating interest in biomarkers derived from microbiota profiling. However, microbiota complexity and technical challenges strongly influencing the results limit the generalization of microbiota profiling and question its clinical utility. In addition, no quality management scheme has been adapted to the specificities of microbiota profiling, notably due to the heterogeneity in methods and results. In this review, we discuss possible adaptation of classical quality management tools routinely used in diagnostic laboratories to microbiota profiling and propose a specific framework. Multiple quality controls are needed to cover all steps, from sampling to data processing. Standard operating procedures, primarily developed for wet lab analyses, must be adapted to the use of bioinformatic tools. Finally, requirements for test validation and proficiency testing must take into account expected discrepancies in results due to the heterogeneity of the processes. The proposed quality management framework should support the implementation of routine microbiota profiling by clinical laboratories to support patient care. Furthermore, its use in research laboratories would improve publication reproducibility as well as transferability of methods and results to routine practice.

Linking Inflammatory Bowel Disease Symptoms to Changes in the Gut Microbiome Structure and Function

Inflammatory bowel disease (IBD) is a chronic disease of the gastrointestinal tract that is often characterized by abdominal pain, rectal bleeding, inflammation, and weight loss. Many studies have posited that the gut microbiome may play an integral role in the onset and exacerbation of IBD. Here, we present a novel computational analysis of a previously published IBD dataset. This dataset consists of shotgun sequence data generated from fecal samples collected from individuals with IBD and an internal control group. Utilizing multiple external controls, together with appropriate techniques to handle the compositionality aspect of sequence data, our computational framework can identify and corroborate differences in the taxonomic profiles, bacterial association networks, and functional capacity within the IBD gut microbiome. Our analysis identified 42 bacterial species that are differentially abundant between IBD and every control group (one internal control and two external controls) with at least a twofold difference. Of the 42 species, 34 were significantly elevated in IBD, relative to every other control. These 34 species were still present in the control groups and appear to play important roles, according to network centrality and degree, in all bacterial association networks. Many of the species elevated in IBD have been implicated in modulating the immune response, mucin degradation, antibiotic resistance, and inflammation. We also identified elevated relative abundances of protein families related to signal transduction, sporulation and germination, and polysaccharide degradation as well as decreased relative abundance of protein families related to menaquinone and ubiquinone biosynthesis. Finally, we identified differences in functional capacities between IBD and healthy controls, and subsequently linked the changes in the functional capacity to previously published clinical research and to symptoms that commonly occur in IBD.

Refinement of 16S rRNA gene analysis for low biomass biospecimens

High-throughput phylogenetic 16S rRNA gene analysis has permitted to thoroughly delve into microbial community complexity and to understand host-microbiota interactions in health and disease. The analysis comprises sample collection and storage, genomic DNA extraction, 16S rRNA gene amplification, high-throughput amplicon sequencing and bioinformatic analysis. Low biomass microbiota samples (e.g. biopsies, tissue swabs and lavages) are receiving increasing attention, but optimal standardization for analysis of low biomass samples has yet to be developed. Here we tested the lower bacterial concentration required to perform 16S rRNA gene analysis using three different DNA extraction protocols, three different mechanical lysing series and two different PCR protocols. A mock microbiota community standard and low biomass samples (10⁸, 10⁷, 10⁶, 10⁵ and 10⁴ microbes) from two healthy donor stools were employed to assess optimal sample processing for 16S rRNA gene analysis using paired-end Illumina MiSeq technology. Three DNA extraction protocols tested in our study performed similar with regards to representing microbiota composition, but extraction yield was better for silica columns compared to bead absorption and chemical precipitation. Furthermore, increasing mechanical lysing time and repetition did ameliorate the representation of bacterial composition. The most influential factor enabling appropriate representation of microbiota composition remains sample biomass. Indeed, bacterial densities below 10⁶ cells resulted in loss of sample identity based on cluster analysis for all tested protocols. Finally, we excluded DNA extraction bias using a genomic DNA standard, which revealed that a semi-nested PCR protocol represented microbiota composition better than classical PCR. Based on our results, starting material concentration is an important limiting factor, highlighting the need to adapt protocols for dealing with low biomass samples. Our study suggests that the use of prolonged mechanical lysing, silica membrane DNA isolation and a semi-nested PCR protocol improve the analysis of low biomass samples. Using the improved protocol we report a lower limit of 10⁶ bacteria per sample for robust and reproducible microbiota analysis.

Omic methodologies for assessing metal(-loid)s-host-microbiota interplay: A review

Omic methodologies have become key analytical tools in a wide number of research topics such as systems biology, environmental analysis, biomedicine or food analysis. They are especially useful when they are combined providing a new perspective and a holistic view of the analytical problem. Methodologies for microbiota analysis have been mostly focused on genome sequencing. However, information provided by these metagenomic studies is limited to the identification of the presence of genes, taxa and their inferred functionality. To achieve a deeper knowledge of microbial functionality in health and disease, especially in dysbiosis conditions related to metal and metalloid exposure, the introduction of additional meta-omic approaches including metabolomics, metallomics, metatranscriptomics and metaproteomics results essential. The possible impact of metals and metalloids on the gut microbiota and their effects on gut-brain axis (GBA) only begin to be figured out. To this end new analytical workflows combining powerful tools are claimed such as high resolution mass spectrometry and heteroatom-tagged proteomics for the absolute quantification of metal-containing biomolecules using the metal as a "tag" in a sensitive and selective detector (e.g. ICP-MS). This review focus on current analytical methodologies related with the analytical techniques and procedures available for metallomics and microbiota analysis with a special attention on their advantages and drawbacks.

Processing human urine and ureteral stents for 16S rRNA amplicon sequencing

Ureteral stents are commonly used medical devices that harbor a unique and patient-specific microbial community. This protocol describes an optimized procedure for high-quality DNA extraction from both urine and ureteral stent samples for the purpose of downstream microbiota characterization by amplicon sequencing. Detailed instruction is provided for 16S rRNA gene V4 region sequencing with the Illumina platform, which enables accurate and reproducible microbiota profiling of low bacterial abundance urine and stent samples.

For complete details on the use and execution of this protocol, please refer to Al et al. (2020).

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The urinary tract harbors low-abundance microbiota capable of influencing health

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Traditional sample preparation yields variable results vulnerable to contamination

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Protocol optimizes urological specimen processing for 16S rRNA amplicon sequencing

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The method ensures high-quality, reproducible urological microbiota data generation

Effect of stevia on the gut microbiota and glucose tolerance in a murine model of diet-induced obesity

Artificial sweeteners have been shown to induce glucose intolerance by altering the gut microbiota; however, little is known about the effect of stevia. Here, we investigate whether stevia supplementation induces glucose intolerance by altering the gut microbiota in mice, hypothesizing that stevia would correct high fat diet-induced glucose intolerance and alter the gut microbiota. Mice were split into four treatment groups: low fat, high fat, high fat + saccharin and high fat + stevia. After 10 weeks of treatment, mice consuming a high fat diet (60% kcal from fat) developed glucose intolerance and gained more weight than mice consuming a low fat diet. Stevia supplementation did not impact body weight or glucose intolerance. Differences in species richness and relative abundances of several phyla were observed in low fat groups compared to high fat, stevia and saccharin. We identified two operational taxonomic groups that contributed to differences in beta-diversity between the stevia and saccharin groups: Lactococcus and Akkermansia in females and Lactococcus in males. Our results demonstrate that stevia does not rescue high fat diet-induced changes in glucose tolerance or the microbiota, and that stevia results in similar alterations to the gut microbiota as saccharin when administered in concordance with a high fat diet.

The seminal microbiome in health and disease

Owing to the fact that there are more microbial than human cells in our body and that humans contain more microbial than human genes, the microbiome has huge potential to influence human physiology, both in health and in disease. The use of next-generation sequencing technologies has helped to elucidate functional, quantitative and mechanistic aspects of the complex microorganism-host interactions that underlie human physiology and pathophysiology. The microbiome of semen is a field of increasing scientific interest, although this microbial niche is currently understudied compared with other areas of microbiome research. However, emerging evidence is beginning to indicate that the seminal microbiome has important implications for the reproductive health of men, the health of the couple and even the health of offspring, owing to transfer of microorganisms to the partner and offspring. As this field expands, further carefully designed and well-powered studies are required to unravel the true nature and role of the seminal microbiome.

The Impact of DNA Polymerase and Number of Rounds of Amplification in PCR on 16S rRNA Gene Sequence Data

PCR amplification of 16S rRNA genes is a critical yet underappreciated step in the generation of sequence data to describe the taxonomic composition of microbial communities. Numerous factors in the design of PCR can impact the sequencing error rate, the abundance of chimeric sequences, and the degree to which the fragments in the product represent their abundance in the original sample (i.e., bias). We compared the performance of high fidelity polymerases and various numbers of rounds of amplification when amplifying a mock community and human stool samples. Although it was impossible to derive specific recommendations, we did observe general trends. Namely, using a polymerase with the highest possible fidelity and minimizing the number of rounds of PCR reduced the sequencing error rate, fraction of chimeric sequences, and bias. Evidence of bias at the sequence level was subtle and could not be ascribed to the fragments' fraction of bases that were guanines or cytosines. When analyzing mock community data, the amount that the community deviated from the expected composition increased with the number of rounds of PCR. This bias was inconsistent for human stool samples. Overall, the results underscore the difficulty of comparing sequence data that are generated by different PCR protocols. However, the results indicate that the variation in human stool samples is generally larger than that introduced by the choice of polymerase or number of rounds of PCR.IMPORTANCE A steep decline in sequencing costs drove an explosion in studies characterizing microbial communities from diverse environments. Although a significant amount of effort has gone into understanding the error profiles of DNA sequencers, little has been done to understand the downstream effects of the PCR amplification protocol. We quantified the effects of the choice of polymerase and number of PCR cycles on the quality of downstream data. We found that these choices can have a profound impact on the way that a microbial community is represented in the sequence data. The effects are relatively small compared to the variation in human stool samples; however, care should be taken to use polymerases with the highest possible fidelity and to minimize the number of rounds of PCR. These results also underscore that it is not possible to directly compare sequence data generated under different PCR conditions.

Issues and current standards of controls in microbiome research

Good scientific practice is important in all areas of science. In recent years this has gained more and more attention, especially considering the 'scientific reproducibility crisis'. While most researchers are aware of the issues with good scientific practice, not all of these issues are necessarily clear, and the details can be very complicated. For many years it has been accepted to perform and publish sequencing based microbiome studies without including proper controls. Although in recent years more scientists realize the necessity of implementing controls, this poses a problem due to the complexity of the field. Another concern is the inability to properly interpret the information gained from controls in microbiome studies. Here, we will discuss these issues and provide a comprehensive overview of problematic points regarding controls in microbiome research, and of the current standards in this area.

Investigation of Detection Limits and the Influence of DNA Extraction and Primer Choice on the Observed Microbial Communities in Drinking Water Samples Using 16S rRNA Gene Amplicon Sequencing

In recent years, 16S rRNA gene amplicon sequencing has been widely adopted for analyzing the microbial communities in drinking water (DW). However, no comprehensive attempts have been made to illuminate the inherent method biases specifically relating to DW communities. In this study, we investigated the impact of DNA extraction and primer choice on the observed microbial community, and furthermore estimated the detection limit of the 16S rRNA gene amplicon sequencing in these experimental settings. Of the two DNA extraction kits investigated, the PowerWater DNA Isolation Kit resulted in higher yield, better reproducibility and more OTUs identified compared to the FastDNA SPIN Kit for Soil, which is also commonly used within DW microbiome research. The use of three separate primer-sets targeting the V1-3, V3-4, and V4 region of the 16S rRNA gene revealed large differences in OTU abundances, with some of the primers unable to detect entire phyla. Estimations of the detection limit were based on bacteria-free water samples (1 L) spiked with Escherichia coli cells in different concentrations [10¹–10⁶ cells/ml]. E.coli could be detected in all samples, however, samples with ∼10¹ cells/ml had several contaminating OTUs constituting approximately 8% of the read abundances. Based on our findings, we recommend using the PowerWater DNA Isolation Kit for DNA extraction in combination with PCR amplification of the V3-4 or V4 region for DW samples if a broad overview of the microbial community is to be obtained.