Contamination in Low Microbial Biomass Microbiome Studies: Issues and Recommendations

Studying the gut virome in the metagenomic era: challenges and perspectives

The human gut harbors a complex ecosystem of microorganisms, including bacteria and viruses. With the rise of next-generation sequencing technologies, we have seen a quantum leap in the study of human-gut-inhabiting bacteria, yet the viruses that infect these bacteria, known as bacteriophages, remain underexplored. In this review, we focus on what is known about the role of bacteriophages in human health and the technical challenges involved in studying the gut virome, of which they are a major component. Lastly, we discuss what can be learned from studies of bacteriophages in other ecosystems.

Comparison of DNA Extraction Methods for Optimal Recovery of Metagenomic DNA from Human and Environmental Samples

Metagenomics is the study of gene pool of an entire community in a particular niche. This provides valuable information about the functionality of host-microbe interaction in a biological ecosystem. Efficient metagenomic DNA extraction is a critical pre-requisite for a successful sequencing run in a metagenomic study. Although isolation of human stool metagenomic DNA is fairly standardized, the same protocol does not work as efficiently in fecal DNA from other organisms. In this study, we report a comparison of manual and commercial DNA extraction methods for diverse samples such as human stool, fish gut and soil. Fishes are known to have variable microbial diversity based on their food habits, so the study included two different varieties of fishes. A modified protocol for effective isolation of metagenomic DNA from human milk samples is also reported, highlighting critical precautions. Recent studies have emphasized the importance of studying functionality of human milk metagenome to understand its influence on infants' health. While manual method works well with most samples and therefore can be a method of choice for testing new samples, broad-range commercial kit offers advantage of high purity and quality. DNA extraction of different samples would go a long way in unraveling the unexplored association between microbes and host in a biological system.

DNA extraction for human microbiome studies: the issue of standardization

Among the laboratory and bioinformatic processing steps for human microbiome studies, a lack of consistency in DNA extraction methodologies is hindering the ability to compare results between studies and sometimes leading to errant conclusions. The purpose of this article is to highlight the issues related to DNA extraction methods and to suggest minimum standard requirements that should be followed to ensure consistency and reproducibility.

Molecular and Microbial Microenvironments in Chronically Diseased Lungs Associated with Cystic Fibrosis

To visualize the personalized distributions of pathogens and chemical environments, including microbial metabolites, pharmaceuticals, and their metabolic products, within and between human lungs afflicted with cystic fibrosis (CF), we generated three-dimensional (3D) microbiome and metabolome maps of six explanted lungs from three cystic fibrosis patients. These 3D spatial maps revealed that the chemical environments differ between patients and within the lungs of each patient. Although the microbial ecosystems of the patients were defined by the dominant pathogen, their chemical diversity was not. Additionally, the chemical diversity between locales in the lungs of the same individual sometimes exceeded interindividual variation. Thus, the chemistry and microbiome of the explanted lungs appear to be not only personalized but also regiospecific. Previously undescribed analogs of microbial quinolones and antibiotic metabolites were also detected. Furthermore, mapping the chemical and microbial distributions allowed visualization of microbial community interactions, such as increased production of quorum sensing quinolones in locations where Pseudomonas was in contact with Staphylococcus and Granulicatella, consistent with in vitro observations of bacteria isolated from these patients. Visualization of microbe-metabolite associations within a host organ in early-stage CF disease in animal models will help elucidate the complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.IMPORTANCE Microbial infections are now recognized to be polymicrobial and personalized in nature. Comprehensive analysis and understanding of the factors underlying the polymicrobial and personalized nature of infections remain limited, especially in the context of the host. By visualizing microbiomes and metabolomes of diseased human lungs, we reveal how different the chemical environments are between hosts that are dominated by the same pathogen and how community interactions shape the chemical environment or vice versa. We highlight that three-dimensional organ mapping methods represent hypothesis-building tools that allow us to design mechanistic studies aimed at addressing microbial responses to other microbes, the host, and pharmaceutical drugs.

Consistent and correctable bias in metagenomic sequencing experiments

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.

Consistent and correctable bias in metagenomic sequencing experiments

Marker-gene and metagenomic sequencing have profoundly expanded our ability to measure biological communities. But the measurements they provide differ from the truth, often dramatically, because these experiments are biased toward detecting some taxa over others. This experimental bias makes the taxon or gene abundances measured by different protocols quantitatively incomparable and can lead to spurious biological conclusions. We propose a mathematical model for how bias distorts community measurements based on the properties of real experiments. We validate this model with 16S rRNA gene and shotgun metagenomics data from defined bacterial communities. Our model better fits the experimental data despite being simpler than previous models. We illustrate how our model can be used to evaluate protocols, to understand the effect of bias on downstream statistical analyses, and to measure and correct bias given suitable calibration controls. These results illuminate new avenues toward truly quantitative and reproducible metagenomics measurements.

Challenges in the construction of knowledge bases for human microbiome-disease associations

The last few years have seen tremendous growth in human microbiome research, with a particular focus on the links to both mental and physical health and disease. Medical and experimental settings provide initial sources of information about these links, but individual studies produce disconnected pieces of knowledge bounded in context by the perspective of expert researchers reading full-text publications. Building a knowledge base (KB) consolidating these disconnected pieces is an essential first step to democratize and accelerate the process of accessing the collective discoveries of human disease connections to the human microbiome. In this article, we survey the existing tools and development efforts that have been produced to capture portions of the information needed to construct a KB of all known human microbiome-disease associations and highlight the need for additional innovations in natural language processing (NLP), text mining, taxonomic representations, and field-wide vocabulary standardization in human microbiome research. Addressing these challenges will enable the construction of KBs that help identify new insights amenable to experimental validation and potentially clinical decision support.

Improved single-swab sample preparation for recovering bacterial and phage DNA from human skin and wound microbiomes

Background

Characterization of the skin and wound microbiome is of high biomedical interest, but is hampered by the low biomass of typical samples. While sample preparation from other microbiomes (e.g., gut) has been the subject of extensive optimization, procedures for skin and wound microbiomes have received relatively little attention. Here we describe an improved method for obtaining both phage and microbial DNA from a single skin or wound swab, characterize the yield of DNA in model samples, and demonstrate the utility of this approach with samples collected from a wound clinic.

Results

We find a substantial improvement when processing wound samples in particular; while only one-quarter of wound samples processed by a traditional method yielded sufficient DNA for downstream analysis, all samples processed using the improved method yielded sufficient DNA. Moreover, for both skin and wound samples, community analysis and viral reads obtained through deep sequencing of clinical swab samples showed significant improvement with the use of the improved method.

Conclusion

Use of this method may increase the efficiency and data quality of microbiome studies from low-biomass samples.

Electronic supplementary material

The online version of this article (10.1186/s12866-019-1586-4) contains supplementary material, which is available to authorized users.

Evolutionary and ecological consequences of gut microbial communities

Animals are distinguished by having guts: organs that must extract nutrients from food while barring invasion by pathogens. Most guts are colonized by non-pathogenic microorganisms, but the functions of these microbes, or even the reasons why they occur in the gut, vary widely among animals. Sometimes these microorganisms have co-diversified with hosts; sometimes they live mostly elsewhere in the environment. Either way, gut microorganisms often benefit hosts. Benefits may reflect evolutionary "addiction" whereby hosts incorporate gut microorganisms into normal developmental processes. But benefits often include novel ecological capabilities; for example, many metazoan clades exist by virtue of gut communities enabling new dietary niches. Animals vary immensely in their dependence on gut microorganisms, from lacking them entirely, to using them as food, to obligate dependence for development, nutrition, or protection. Many consequences of gut microorganisms for hosts can be ascribed to microbial community processes and the host's ability to shape these processes.

Microbial translocation into amniotic fluid of vervet monkeys is common and unrelated to adverse infant outcomes

Amniotic fluid was collected from pregnant female African green monkeys (n = 20). Analyses indicate microbial translocation into amniotic fluid during pregnancy is typical, and microbial load reduces across gestation. Microbial translocation does not relate to infant outcome or maternal factors. Lastly, we demonstrate that sample contamination is easily introduced and detectable.

Crypt- and Mucosa-Associated Core Microbiotas in Humans and Their Alteration in Colon Cancer Patients

Due to the huge number of bacteria constituting the human colon microbiota, alteration in the balance of its constitutive taxa (i.e., dysbiosis) is highly suspected of being involved in colorectal oncogenesis. Indeed, bacterial signatures in association with CRC have been described. These signatures may vary if bacteria are identified in feces or in association with tumor tissues. Here, we show that bacteria colonize human colonic crypts in tissues obtained from patients with CRC and with normal colonoscopy results. Aerobic nonfermentative Proteobacteria previously identified as constitutive of the crypt-specific core microbiota in murine colonic samples are similarly prevalent in human colonic crypts in combination with other anaerobic taxa. We also show that bacterial signatures characterizing the crypts of colonic tumors vary depending whether right-side or left-side tumors are analyzed.

We have previously identified a crypt-specific core microbiota (CSCM) in the colons of healthy laboratory mice and related wild rodents. Here, we confirm that a CSCM also exists in the human colon and appears to be altered during colon cancer. The colonic microbiota is suggested to be involved in the development of colorectal cancer (CRC). Because the microbiota identified in fecal samples from CRC patients does not directly reflect the microbiota associated with tumor tissues themselves, we sought to characterize the bacterial communities from the crypts and associated adjacent mucosal surfaces of 58 patients (tumor and normal homologous tissue) and 9 controls with normal colonoscopy results. Here, we confirm that bacteria colonize human colonic crypts in both control and CRC tissues, and using laser-microdissected tissues and 16S rRNA gene sequencing, we further show that right and left crypt- and mucosa-associated bacterial communities are significantly different. In addition to Bacteroidetes and Firmicutes, and as with murine proximal colon crypts, environmental nonfermentative Proteobacteria are found in human colonic crypts. Fusobacterium and Bacteroides fragilis are more abundant in right-side tumors, whereas Parvimonas micra is more prevalent in left-side tumors. More precisely, Fusobacterium periodonticum is more abundant in crypts from cancerous samples in the right colon than in associated nontumoral samples from adjacent areas but not in left-side colonic samples. Future analysis of the interaction between these bacteria and the crypt epithelium, particularly intestinal stem cells, will allow deciphering of their possible oncogenic potential.

Full-length 16S rRNA gene classification of Atlantic salmon bacteria and effects of using different 16S variable regions on community structure analysis

Understanding fish-microbial relationships may be of great value for fish producers as fish growth, development and welfare are influenced by the microbial community associated with the rearing systems and fish surfaces. Accurate methods to generate and analyze these microbial communities would be an important tool to help improve understanding of microbial effects in the industry. In this study, we performed taxonomic classification and determination of operational taxonomic units on Atlantic salmon microbiota by taking advantage of full-length 16S rRNA gene sequences. Skin mucus was dominated by the genera Flavobacterium and Psychrobacter. Intestinal samples were dominated by the genera Carnobacterium, Aeromonas, Mycoplasma and by sequences assigned to the order Clostridiales. Applying Sanger sequencing on the full-length bacterial 16S rRNA gene from the pool of 46 isolates obtained in this study showed a clear assignment of the PacBio full-length bacterial 16S rRNA gene sequences down to the genus level. One of the bottlenecks in comparing microbial profiles is that different studies use different 16S rRNA gene regions. Comparisons of sequence assignments between full-length and in silico derived variable 16S rRNA gene regions showed different microbial profiles with variable effects between phylogenetic groups and taxonomic ranks.

Quantifying and Understanding Well-to-Well Contamination in Microbiome Research

Microbial sequences inferred as belonging to one sample may not have originated from that sample. Such contamination may arise from laboratory or reagent sources or from physical exchange between samples. This study seeks to rigorously assess the behavior of this often-neglected between-sample contamination. Using unique bacteria, each assigned a particular well in a plate, we assess the frequency at which sequences from each source appear in other wells. We evaluate the effects of different DNA extraction methods performed in two laboratories using a consistent plate layout, including blanks and low-biomass and high-biomass samples. Well-to-well contamination occurred primarily during DNA extraction and, to a lesser extent, in library preparation, while barcode leakage was negligible. Laboratories differed in the levels of contamination. Extraction methods differed in their occurrences and levels of well-to-well contamination, with plate methods having more well-to-well contamination and single-tube methods having higher levels of background contaminants. Well-to-well contamination occurred primarily in neighboring samples, with rare events up to 10 wells apart. This effect was greatest in samples with lower biomass and negatively impacted metrics of alpha and beta diversity. Our work emphasizes that sample contamination is a combination of cross talk from nearby wells and background contaminants. To reduce well-to-well effects, samples should be randomized across plates, samples of similar biomasses should be processed together, and manual single-tube extractions or hybrid plate-based cleanups should be employed. Researchers should avoid simplistic removals of taxa or operational taxonomic units (OTUs) appearing in negative controls, as many will be microbes from other samples rather than reagent contaminants.IMPORTANCE Microbiome research has uncovered magnificent biological and chemical stories across nearly all areas of life science, at times creating controversy when findings reveal fantastic descriptions of microbes living and even thriving in what were once thought to be sterile environments. Scientists have refuted many of these claims because of contamination, which has led to robust requirements, including the use of controls, for validating accurate portrayals of microbial communities. In this study, we describe a previously undocumented form of contamination, well-to-well contamination, and show that this sort of contamination primarily occurs during DNA extraction rather than PCR, is highest with plate-based methods compared to single-tube extraction, and occurs at a higher frequency in low-biomass samples. This finding has profound importance in the field, as many current techniques to "decontaminate" a data set simply rely on an assumption that microbial reads found in blanks are contaminants from "outside," namely, the reagents or consumables.

A Lot on Your Plate? Well-to-Well Contamination as an Additional Confounder in Microbiome Sequence Analyses

DNA sequence-based microbiome studies can be impacted by a range of different methodological artefacts. Contamination originating from laboratory kits and reagents can lead to erroneous results, particularly in samples containing a low microbial biomass. Minich and colleagues (mSystems 4:e00186-19, 2019, https://doi.org/10.1128/mSystems.00186-19) report on a different form of contamination, cross-contamination between samples that are processed together. They find that transfer of material between samples in 96-well plates is a common occurrence. The DNA extraction step, particularly when carried out automatedly, is identified as the major source of this contamination type. Well-to-well contamination distorts diversity measures, with low-biomass samples particularly affected. This report has important implications for attempts to decontaminate microbiome sequencing results. As contamination is derived from both external sources and crossover between samples, it is not appropriate to simply remove sequence variants that are detected in negative-control blanks, and more-nuanced decontamination approaches may be required.

Microbiome analysis as a platform R&D tool for parasitic nematode disease management

The relationship between bacterial communities and their host is being extensively investigated for the potential to improve the host's health. Little is known about the interplay between the microbiota of parasites and the health of the infected host. Using nematode co-infection of lambs as a proof-of-concept model, the aim of this study was to characterise the microbiomes of nematodes and that of their host, enabling identification of candidate nematode-specific microbiota member(s) that could be exploited as drug development tools or for targeted therapy. Deep sequencing techniques were used to elucidate the microbiomes of different life stages of two parasitic nematodes of ruminants, Haemonchus contortus and Teladorsagia circumcincta, as well as that of the co-infected ovine hosts, pre- and post infection. Bioinformatic analyses demonstrated significant differences between the composition of the nematode and ovine microbiomes. The two nematode species also differed significantly. The data indicated a shift in the constitution of the larval nematode microbiome after exposure to the ovine microbiome, and in the ovine intestinal microbial community over time as a result of helminth co-infection. Several bacterial species were identified in nematodes that were absent from their surrounding abomasal environment, the most significant of which included Escherichia coli/Shigella. The ability to purposefully infect nematode species with engineered E. coli was demonstrated in vitro, validating the concept of using this bacterium as a nematode-specific drug development tool and/or drug delivery vehicle. To our knowledge, this is the first description of the concept of exploiting a parasite's microbiome for drug development and treatment purposes.

A new home for microbes

Modern microorganisms growing in fossils provide major challenges for researchers trying to detect ancient molecules in the same fossils.

Whole genome sequencing revealed microbiome in lung adenocarcinomas presented as ground-glass nodules

Background

Emerging evidence has suggested that dysbiosis of the microbiota may play vital roles in tumorigenesis. However, the interplay between the microbiome and lung cancer remains undetermined. In this study, we characterize the microbiome in the early stage of lung cancer, which presented as ground-glass nodules (GGNs).

Methods

We sequenced the whole genomes from 10 GGN lesions and 5 adjacent normal lung tissue samples. After being filtered with human genome sequences, the sequence reads were mapped to prokaryotic genomes refSeq and non-redundant protein database for taxa and gene functions profiling, respectively.

Results

Mycobacterium, Corynebacterium, and Negativicoccus were the core microbiota found in all GGNs and the normal tissue samples. The microbiota composition did not show significant difference between GGNs and normal tissues except the adenocarcinoma (AD) (P=0.047). A significant β diversity in microbiome gene functions was found among different patients. Two individual gene functions, the Secondary Metabolism (1.32 fold with P=0.01) and the Serine Threonine protein kinase (4.23 fold, P<0.001), were significantly increased in GGNs over normal tissue samples.

Conclusions

This study helps shed light on the implication of the microbiome in early stage lung cancer, which encourages the further study and development of innovative strategies for early prevention and treatment of lung cancer.

Not all animals need a microbiome

It is often taken for granted that all animals host and depend upon a microbiome, yet this has only been shown for a small proportion of species. We propose that animals span a continuum of reliance on microbial symbionts. At one end are the famously symbiont-dependent species such as aphids, humans, corals and cows, in which microbes are abundant and important to host fitness. In the middle are species that may tolerate some microbial colonization but are only minimally or facultatively dependent. At the other end are species that lack beneficial symbionts altogether. While their existence may seem improbable, animals are capable of limiting microbial growth in and on their bodies, and a microbially independent lifestyle may be favored by selection under some circumstances. There is already evidence for several ‘microbiome-free’ lineages that represent distantly related branches in the animal phylogeny. We discuss why these animals have received such little attention, highlighting the potential for contaminants, transients, and parasites to masquerade as beneficial symbionts. We also suggest ways to explore microbiomes that address the limitations of DNA sequencing. We call for further research on microbiome-free taxa to provide a more complete understanding of the ecology and evolution of macrobe-microbe interactions.

Contribution of the Mucosal Microbiota to Bovine Respiratory Health

Recognizing the respiratory tract as a dynamic and complex ecosystem has enhanced our understanding of the pathophysiology of bovine respiratory disease (BRD). There is widespread evidence showing that disease-predisposing factors often disrupt the respiratory microbial ecosystem, provoking atypical colonization patterns and a progressive dysbiosis. The ecological factors that shape the respiratory microbiota, and the influence of these complex communities on bovine respiratory health, are a rich area for research exploration. Here, we review the current status of understanding of the bovine respiratory microbiota, the factors that influence its development and stability, its role in maintaining mucosal homeostasis, and ultimately its contribution to bovine health and disease. Finally, we explore the limitations of current research approaches to the microbiome and discuss potential directions for future research that can help us better understand the role of the respiratory microbiota in the health, welfare, and productivity of livestock.

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.

Laboratory contamination over time during low-biomass sample analysis

Bacteria are not only ubiquitous on earth but can also be incredibly diverse within clean laboratories and reagents. The presence of both living and dead bacteria in laboratory environments and reagents is especially problematic when examining samples with low endogenous content (e.g., skin swabs, tissue biopsies, ice, water, degraded forensic samples or ancient material), where contaminants can outnumber endogenous microorganisms within samples. The contribution of contaminants within high‐throughput studies remains poorly understood because of the relatively low number of contaminant surveys. Here, we examined 144 negative control samples (extraction blank and no‐template amplification controls) collected in both typical molecular laboratories and an ultraclean ancient DNA laboratory over 5 years to characterize long‐term contaminant diversity. We additionally compared the contaminant content within a home‐made silica‐based extraction method, commonly used to analyse low endogenous content samples, with a widely used commercial DNA extraction kit. The contaminant taxonomic profile of the ultraclean ancient DNA laboratory was unique compared to modern molecular biology laboratories, and changed over time according to researcher, month and season. The commercial kit also contained higher microbial diversity and several human‐associated taxa in comparison to the home‐made silica extraction protocol. We recommend a minimum of two strategies to reduce the impacts of laboratory contaminants within low‐biomass metagenomic studies: (a) extraction blank controls should be included and sequenced with every batch of extractions and (b) the contributions of laboratory contamination should be assessed and reported in each high‐throughput metagenomic study.

Towards precision quantification of contamination in metagenomic sequencing experiments

Metagenomic next-generation sequencing (mNGS) experiments involving small amounts of nucleic acid input are highly susceptible to erroneous conclusions resulting from unintentional sequencing of occult contaminants, especially those derived from molecular biology reagents. Recent work suggests that, for any given microbe detected by mNGS, an inverse linear relationship between microbial sequencing reads and sample mass implicates that microbe as a contaminant. By associating sequencing read output with the mass of a spike-in control, we demonstrate that contaminant nucleic acid can be quantified in order to identify the mass contributions of each constituent. In an experiment using a high-resolution (n = 96) dilution series of HeLa RNA spanning 3-logs of RNA mass input, we identified a complex set of contaminants totaling 9.1 ± 2.0 attograms. Given the competition between contamination and the true microbiome in ultra-low biomass samples such as respiratory fluid, quantification of the contamination within a given batch of biological samples can be used to determine a minimum mass input below which sequencing results may be distorted. Rather than completely censoring contaminant taxa from downstream analyses, we propose here a statistical approach that allows separation of the true microbial components from the actual contribution due to contamination. We demonstrate this approach using a batch of n = 97 human serum samples and note that despite E. coli contamination throughout the dataset, we are able to identify a patient sample with significantly more E. coli than expected from contamination alone. Importantly, our method assumes no prior understanding of possible contaminants, does not rely on any prior collection of environmental or reagent-only sequencing samples, and does not censor potentially clinically relevant taxa, thus making it a generalized approach to any kind of metagenomic sequencing, for any purpose, clinical or otherwise.

What is new and relevant for sequencing-based microbiome research? A mini-review

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Sample storage and nucleic acid isolation influence microbiota compositions.

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Error-corrected amplicon sequence variants (ASVs) improve 16S rRNA analysis.

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Contamination and host cells confound and complicate microbiota analysis.

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Quantitative and active microbiota analyses can complement existing methods.

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Open data and protocol sharing increases transparency and reproducibility.

Microbiome research has transformed the scientific landscape, as reflected by the exponential increase in microbiome-related publications from many different disciplines. Host-associated microbial communities play a role for almost all aspects of human, animal and plant biology and health. Consequently, there are tremendous expectations for the development of new clinical, agricultural and biotechnological applications of microbiome research. However, the field continues to be largely shaped by descriptive studies, the mechanistic understanding of microbiome functions for their hosts remains fragmentary, and direct applications of microbiome research are lacking. The aim of this review is therefore to provide a general introduction to the technical opportunities and challenges of microbiome research, as well as to make experimental and bioinformatic recommendations, i.e. (i) to avoid, reduce and assess the confounding effects of sample storage, nucleic acid isolation and microbial contamination; (ii) to minimize non-microbial contributions in host-associated microbiome samples; (iii) to sharpen the focus on physiologically relevant microbiome features by distinguishing signals from metabolically active and inactive or dead microbes and by adopting quantitative methods; and (iv) to enforce open data and protocol policies in order increase the transparency, reproducibility and credibility of the field.

Assessing alignment-based taxonomic classification of ancient microbial DNA

The field of palaeomicrobiology-the study of ancient microorganisms-is rapidly growing due to recent methodological and technological advancements. It is now possible to obtain vast quantities of DNA data from ancient specimens in a high-throughput manner and use this information to investigate the dynamics and evolution of past microbial communities. However, we still know very little about how the characteristics of ancient DNA influence our ability to accurately assign microbial taxonomies (i.e. identify species) within ancient metagenomic samples. Here, we use both simulated and published metagenomic data sets to investigate how ancient DNA characteristics affect alignment-based taxonomic classification. We find that nucleotide-to-nucleotide, rather than nucleotide-to-protein, alignments are preferable when assigning taxonomies to short DNA fragment lengths routinely identified within ancient specimens (<60 bp). We determine that deamination (a form of ancient DNA damage) and random sequence substitutions corresponding to ∼100,000 years of genomic divergence minimally impact alignment-based classification. We also test four different reference databases and find that database choice can significantly bias the results of alignment-based taxonomic classification in ancient metagenomic studies. Finally, we perform a reanalysis of previously published ancient dental calculus data, increasing the number of microbial DNA sequences assigned taxonomically by an average of 64.2-fold and identifying microbial species previously unidentified in the original study. Overall, this study enhances our understanding of how ancient DNA characteristics influence alignment-based taxonomic classification of ancient microorganisms and provides recommendations for future palaeomicrobiological studies.

Complex Microbiome in Brain Abscess Revealed by Whole-Genome Culture-Independent and Culture-Based Sequencing

Brain abscess is a severe infectious disease with high mortality and mobility. Although culture-based techniques have been widely used for the investigation of microbial composition of brain abscess, these approaches are inherent biased. Recent studies using 16S ribosomal sequencing approaches revealed high complexity of the bacterial community involved in brain abscess but fail to detect fungal and viral composition. In the study, both culture-independent nanopore metagenomic sequencing and culture-based whole-genome sequencing using both the Illumina and the Nanopore platforms were conducted to investigate the microbial composition and genomic characterization in brain abscess. Culture-independent metagenomic sequencing revealed not only a larger taxonomic diversity of bacteria but also the presence of fungi and virus communities. The culture-based whole-genome sequencing identified a novel species in Prevotella and reconstructs a Streptococcus constellatus with a high GC-skew genome. Antibiotic-resistance genes CfxA and ErmF associated with resistance to penicillin and clindamycin were also identified in culture-based and culture-free sequencing. This study implies current understanding of brain abscess need to consider the broader diversity of microorganisms.