The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

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.

Aedes spp. and Their Microbiota: A Review

Aedes spp. are a major public health concern due to their ability to be efficient vectors of dengue, Chikungunya, Zika, and other arboviruses. With limited vaccines available and no effective therapeutic treatments against arboviruses, the control of Aedes spp. populations is currently the only strategy to prevent disease transmission. Host-associated microbes (i.e., microbiota) recently emerged as a promising field to be explored for novel environmentally friendly vector control strategies. In particular, gut microbiota is revealing its impact on multiple aspects of Aedes spp. biology, including vector competence, thus being a promising target for manipulation. Here we describe the technological advances, which are currently expanding our understanding of microbiota composition, abundance, variability, and function in the two main arboviral vectors, the mosquitoes Aedes aegypti and Aedes albopictus. Aedes spp. microbiota is described in light of its tight connections with the environment, with which mosquitoes interact during their various developmental stages. Unraveling the dynamic interactions among the ecology of the habitat, the mosquito and the microbiota have the potential to uncover novel physiological interdependencies and provide a novel perspective for mosquito control.

Assessing the comparability of different DNA extraction and amplification methods in gut microbial community profiling

Automated, high-throughput technologies are becoming increasingly common in microbiome studies to decrease costs and increase efficiency. However, in microbiome studies, small differences in methodology – including storage conditions, wet lab methods, sequencing platforms and data analysis – can influence the reproducibility and comparability of data across studies. There has been limited testing of the effects of high-throughput methods, including microfluidic PCR technologies. In this paper, we compare two extraction methods (the QIAamp DNA Stool Mini Kit and the MoBio PowerSoil DNA Isolation kit), two taq polymerase enzymes (MyTaq HS Red Mix and Accustart II PCR ToughMix), two primer sets (V3–V4 and V4–V5) and two amplification methods (a common two-step PCR protocol and amplicon library preparation on the Fluidigm Access Array system that allows automated multiplexing of primers). Gut microbial community profiles were significantly affected by all variables. While there were no significant differences in alpha diversity measured between the two extraction methods, there was an effect of extraction method on community composition measured by unweighted UniFrac distances. Both amplification method and primers had a significant effect on both alpha diversity and community composition. The relative abundance of Actinobacteria was significantly lower when using the MoBio kit or Fluidigm amplification method, and the relative abundance of Firmicutes was lower when using the Qiagen kit. Microbial community profiles based on Fluidigm-generated amplicon libraries were not comparable to those generated with more commonly used methods. Researchers should carefully consider the limitations and biases that different extraction and amplification methods can introduce into their results. Additionally, more thorough benchmarking of automated and multiplexing methods is necessary to determine the magnitude of the potential trade-off between the quality and the quantity of data.

How should we store avian faecal samples for microbiota analyses? Comparing efficacy and cost-effectiveness

Analyses of bacterial DNA in faecal samples are becoming ever more common, yet we still do not know much about bird microbiomes. These challenges partly lie in the unique chemical nature of their faeces, and in the choice of sample storage method, which affects DNA preservation and the resulting microbiome composition. However, there is little information available on how best to preserve avian faeces for microbial analyses. This study evaluates five widely used methods for preserving nucleic acids and inferring microbiota profiles, for their relative efficacy, cost, and practicality. We tested the five methods (in-situ bead-beating with a TerraLyzer instrument, silica-bead desiccation, ethanol, refrigeration and RNAlater buffer) on 50 fresh faecal samples collected from captive House sparrows (Passer domesticus). In line with other studies, we find that different storage methods lead to distinct bacterial profiles. Storage method had a large effect on community composition and the relative abundance of dominant phyla such as Firmicutes and Proteobacteria, with the most significant changes observed for refrigerated samples. Furthermore, differences in the abundance of aerobic or facultatively aerobic taxa, particularly in refrigerated samples and those stored in ethanol, puts limits on comparisons of bacterial communities across different storage methods. Finally, the methods that did not include in-situ bead-beating did not recover comparable levels of microbiota to the samples that were immediately processed and preserved using a TerraLyzer device. However, this method is also less practical and more expensive under field work circumstances. Our study is the most comprehensive analysis to date on how storage conditions affect subsequent molecular assays applied to avian faeces and provides guidance on cost and practicality of methods under field conditions.

Nitrate-Utilizing Microorganisms Resistant to Multiple Metals from the Heavily Contaminated Oak Ridge Reservation

Contamination of environments with nitrate generated by industrial processes and the use of nitrogen-containing fertilizers is a growing problem worldwide. While nitrate can be removed from contaminated areas by microbial denitrification, nitrate frequently occurs with other contaminants, such as heavy metals, that have the potential to impede the process. Here, nitrate-reducing microorganisms were enriched and isolated from both groundwater and sediments at the Oak Ridge Reservation (ORR) using concentrations of nitrate and metals (Al, Mn, Fe, Co, Ni, Cu, Cd, and U) similar to those observed in a contaminated environment at ORR. Seven new metal-resistant, nitrate-reducing strains were characterized, and their distribution across both noncontaminated and contaminated areas at ORR was examined. While the seven strains have various pH ranges for growth, carbon source preferences, and degrees of resistance to individual and combinations of metals, all were able to reduce nitrate at similar rates both in the presence and absence of the mixture of metals found in the contaminated ORR environment. Four strains were identified in groundwater samples at different ORR locations by exact 16S RNA sequence variant analysis, and all four were found in both noncontaminated and contaminated areas. By using environmentally relevant metal concentrations, we successfully isolated multiple organisms from both ORR noncontaminated and contaminated environments that are capable of reducing nitrate in the presence of extreme mixed-metal contamination.IMPORTANCE Nitrate contamination is a global issue that affects groundwater quality. In some cases, cocontamination of groundwater with nitrate and mixtures of heavy metals could decrease microbially mediated nitrate removal, thereby increasing the duration of nitrate contamination. Here, we used metal and nitrate concentrations that are present in a contaminated site at the Oak Ridge Reservation to isolate seven metal-resistant strains. All were able to reduce nitrate in the presence of high concentrations of a mixture of heavy metals. Four of seven strains were located in pristine as well as contaminated sites at the Oak Ridge Reservation. Further study of these nitrate-reducing strains will uncover mechanisms of resistance to multiple metals that will increase our understanding of the effect of nitrate and metal contamination on groundwater microbial communities.

Scales of persistence: transmission and the microbiome

Historically microbiomes have been studied on the scale of the individual host, giving little consideration for the role of extra-host microbial populations in microbiome assembly. However, work in recent years has brought to light the importance of inter-host transmission and its influence on microbiome composition and dynamics. We now appreciate that microbiomes do not exist in isolation, but exchange constituents with the microbial communities of other hosts and the environment. Moving forward, fully understanding the role of transmission in microbiome assembly and dynamics will require a high-resolution view of the colonization and persistence patterns of particular microbial lineages (i.e. strains) across individuals and the environment. Yet, accomplishing this level of resolution will be an immense challenge, requiring improved sampling and bioinformatics approaches as well as employment of tractable experimental models. Insight gained from these investigations will contribute to our understanding of microbiome composition and variation, and lead to improved strategies for modulating microbiomes to improve human health.

Human Virome and Disease: High-Throughput Sequencing for Virus Discovery, Identification of Phage-Bacteria Dysbiosis and Development of Therapeutic Approaches with Emphasis on the Human Gut

The virome is comprised of endogenous retroviruses, eukaryotic viruses, and bacteriophages and is increasingly being recognized as an essential part of the human microbiome. The human virome is associated with Type-1 diabetes (T1D), Type-2 diabetes (T2D), Inflammatory Bowel Disease (IBD), Human Immunodeficiency Virus (HIV) infection, and cancer. Increasing evidence also supports trans-kingdom interactions of viruses with bacteria, small eukaryotes and host in disease progression. The present review focuses on virus ecology and biology and how this translates mostly to human gut virome research. Current challenges in the field and how the development of bioinformatic tools and controls are aiding to overcome some of these challenges are also discussed. Finally, the present review also focuses on how human gut virome research could result in translational and clinical studies that may facilitate the development of therapeutic approaches.

Insights into the Microbiome of Breast Implants and Periprosthetic Tissue in Breast Implant-Associated Anaplastic Large Cell Lymphoma

Though rare, breast implant-associated anaplastic large cell lymphoma (BIA-ALCL), a CD30+ T-cell lymphoma associated with textured breast implants, has adversely impacted our perception of the safety of breast implants. Its etiology unknown, one hypothesis suggests an initiating inflammatory stimulus, possibly infectious, triggers BIA-ALCL. We analyzed microbiota of breast, skin, implant and capsule in BIA-ALCL patients (n = 7), and controls via culturing methods, 16S rRNA microbiome sequencing, and immunohistochemistry. Alpha and beta diversity metrics and relative abundance of Gram-negative bacteria were calculated, and phylogenetic trees constructed. Staphylococcus spp., the most commonly cultured microbes, were identified in both the BIA-ALCL and contralateral control breast. The diversity of bacterial microbiota did not differ significantly between BIA-ALCL and controls for any material analyzed. Further, there were no significant differences in the relative abundance of Gram-negative bacteria between BIA-ALCL and control specimens. Heat maps suggested substantial diversity in the composition of the bacterial microbiota of the skin, breast, implant and capsule between patients with no clear trend to distinguish BIA-ALCL from controls. While we identified no consistent differences between patients with BIA-ALCL-affected and contralateral control breasts, this study provides insights into the composition of the breast microbiota in this population.

DAIRYdb: a manually curated reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products

Background

Reads assignment to taxonomic units is a key step in microbiome analysis pipelines. To date, accurate taxonomy annotation of 16S reads, particularly at species rank, is still challenging due to the short size of read sequences and differently curated classification databases. The close phylogenetic relationship between species encountered in dairy products, however, makes it crucial to annotate species accurately to achieve sufficient phylogenetic resolution for further downstream ecological studies or for food diagnostics. Curated databases dedicated to the environment of interest are expected to improve the accuracy and resolution of taxonomy annotation.

Results

We provide a manually curated database composed of 10’290 full-length 16S rRNA gene sequences from prokaryotes tailored for dairy products analysis (https://github.com/marcomeola/DAIRYdb).

The performance of the DAIRYdb was compared with the universal databases Silva, LTP, RDP and Greengenes. The DAIRYdb significantly outperformed all other databases independently of the classification algorithm by enabling higher accurate taxonomy annotation down to the species rank. The DAIRYdb accurately annotates over 90% of the sequences of either single or paired hypervariable regions automatically.

The manually curated DAIRYdb strongly improves taxonomic annotation accuracy for microbiome studies in dairy environments. The DAIRYdb is a practical solution that enables automatization of this key step, thus facilitating the routine application of NGS microbiome analyses for microbial ecology studies and diagnostics in dairy products.

Electronic supplementary material

The online version of this article (10.1186/s12864-019-5914-8) contains supplementary material, which is available to authorized users.

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.

Bacterial alkylquinolone signaling contributes to structuring microbial communities in the ocean

BACKGROUND

Marine bacteria form complex relationships with eukaryotic hosts, from obligate symbioses to pathogenic interactions. These interactions can be tightly regulated by bioactive molecules, creating a complex system of chemical interactions through which these species chemically communicate thereby directly altering the host's physiology and community composition. Quorum sensing (QS) signals were first described in a marine bacterium four decades ago, and since then, we have come to discover that QS mediates processes within the marine carbon cycle, affects the health of coral reef ecosystems, and shapes microbial diversity and bacteria-eukaryotic host relationships. Yet, only recently have alkylquinolone signals been recognized for their role in cell-to-cell communication and the orchestration of virulence in biomedically relevant pathogens. The alkylquinolone, 2-heptyl-4-quinolone (HHQ), was recently found to arrest cell growth without inducing cell mortality in selected phytoplankton species at nanomolar concentrations, suggesting QS molecules like HHQ can influence algal physiology, playing pivotal roles in structuring larger ecological frameworks.

RESULTS

To understand how natural communities of phytoplankton and bacteria respond to HHQ, field-based incubation experiments with ecologically relevant concentrations of HHQ were conducted over the course of a stimulated phytoplankton bloom. Bulk flow cytometry measurements indicated that, in general, exposure to HHQ caused nanoplankton and prokaryotic cell abundances to decrease. Amplicon sequencing revealed HHQ exposure altered the composition of particle-associated and free-living microbiota, favoring the relative expansion of both gamma- and alpha-proteobacteria, and a concurrent decrease in Bacteroidetes. Specifically, Pseudoalteromonas spp., known to produce HHQ, increased in relative abundance following HHQ exposure. A search of representative bacterial genomes from genera that increased in relative abundance when exposed to HHQ revealed that they all have the genetic potential to bind HHQ.

CONCLUSIONS

This work demonstrates HHQ has the capacity to influence microbial community organization, suggesting alkylquinolones have functions beyond bacterial communication and are pivotal in driving microbial community structure and phytoplankton growth. Knowledge of how bacterial signals alter marine communities will serve to deepen our understanding of the impact these chemical interactions have on a global scale.

Microbiota data from low biomass milk samples is markedly affected by laboratory and reagent contamination

Discoveries of bacterial communities in environments that previously have been described as sterile have in recent years radically challenged the view of these environments. In this study we aimed to use 16S rRNA sequencing to describe the composition and temporal stability of the bacterial microbiota in bovine milk from healthy udder quarters, an environment previously believed to be sterile. Sequencing of the 16S rRNA gene is a technique commonly used to describe bacterial composition and diversity in various environments. With the increased use of 16S rRNA gene sequencing, awareness of methodological pitfalls such as biases and contamination has increased although not in equal amount. Evaluation of the composition and temporal stability of the microbiota in 288 milk samples was largely hampered by background contamination, despite careful and aseptic sample processing. Sequencing of no template control samples, positive control samples, with defined levels of bacteria, and 288 milk samples with various levels of bacterial growth, revealed that the data was influenced by contaminating taxa, primarily Methylobacterium. We observed an increasing impact of contamination with decreasing microbial biomass where the contaminating taxa became dominant in samples with less than 104 bacterial cells per mL. By applying a contamination filtration on the sequence data, the amount of sequences was substantially reduced but only a minor impact on number of identified taxa and by culture known endogenous taxa was observed. This suggests that data filtration can be useful for identifying biologically relevant associations in milk microbiota data.

Bovine milk microbiome: a more complex issue than expected

The aim of this study was to analyze bacterial profiles of bovine mastitic milk samples and samples from healthy quarters using Next Generation Sequencing of amplicons from 16S rRNA genes and to compare results with microbiological results by PCR assays of the same samples. A total of 49 samples were collected from one single dairy herd during the same day. The samples were divided in two sample sets, which were used in this study. The DNA extraction as well as the library preparation and sequencing of these two sets were performed separately, and results of the two datasets were then compared. The vast majority of genera detected appeared with low read numbers and/or in only a few samples. Results of PCR and microbiome analyses of samples infected with major pathogens Staphylococcus aureus or Streptococcus uberis were consistent as these genera also covered the majority of reads detected in the microbiome analysis. Analysis of alpha diversity revealed a much higher species richness in set 1 than in set 2. The dominating bacterial genera with the highest read numbers clearly differed between datasets, especially in PCR negative samples and samples positive for minor pathogens. In addition to this, linear discriminant analysis (LDA) was conducted between the two sets to identify significantly different genera/family level microbes. The genus Methylobacterium was much more common in set 2 compared to set 1, and genus Streptococcus more common in set 1. Our results indicate amplification of contaminating bacteria in excess in samples with no or minor amounts of pathogen DNA in dataset 2. There is a need for critical assessment of results of milk microbiome analyses.

Understanding the impacts of intermittent supply on the drinking water microbiome

Increasing access to piped water in low-income and middle-income countries combined with the many factors that threaten our drinking water supply infrastructure mean that intermittent water supply (IWS) will remain a common practice around the world. Common features of IWS include water stagnation, pipe drainage, intrusion, backflow, first flush events, and household storage. IWS has been shown to cause degradation as measured by traditional microbial water quality indicators. In this review, we build on new insights into the microbial ecology of continuous water supply systems revealed by sequencing methods to speculate about how intermittent supply conditions may further influence the drinking water microbiome, and identify priorities for future research.

Invited review: Application of meta-omics to understand the dynamic nature of the rumen microbiome and how it responds to diet in ruminants

Ruminants are unique among livestock due to their ability to efficiently convert plant cell wall carbohydrates into meat and milk. This ability is a result of the evolution of an essential symbiotic association with a complex microbial community in the rumen that includes vast numbers of bacteria, methanogenic archaea, anaerobic fungi and protozoa. These microbes produce a diverse array of enzymes that convert ingested feedstuffs into volatile fatty acids and microbial protein which are used by the animal for growth. Recent advances in high-throughput sequencing and bioinformatic analyses have helped to reveal how the composition of the rumen microbiome varies significantly during the development of the ruminant host, and with changes in diet. These sequencing efforts are also beginning to explain how shifts in the microbiome affect feed efficiency. In this review, we provide an overview of how meta-omics technologies have been applied to understanding the rumen microbiome, and the impact that diet has on the rumen microbial community.

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.

Metagenomic analysis of colorectal cancer datasets identifies cross-cohort microbial diagnostic signatures and a link with choline degradation

Several studies have investigated links between the gut microbiome and colorectal cancer (CRC), but questions remain about the replicability of biomarkers across cohorts and populations. We performed a meta-analysis of five publicly available datasets and two new cohorts and validated the findings on two additional cohorts, considering in total 969 fecal metagenomes. Unlike microbiome shifts associated with gastrointestinal syndromes, the gut microbiome in CRC showed reproducibly higher richness than controls (P < 0.01), partially due to expansions of species typically derived from the oral cavity. Meta-analysis of the microbiome functional potential identified gluconeogenesis and the putrefaction and fermentation pathways as being associated with CRC, whereas the stachyose and starch degradation pathways were associated with controls. Predictive microbiome signatures for CRC trained on multiple datasets showed consistently high accuracy in datasets not considered for model training and independent validation cohorts (average area under the curve, 0.84). Pooled analysis of raw metagenomes showed that the choline trimethylamine-lyase gene was overabundant in CRC (P = 0.001), identifying a relationship between microbiome choline metabolism and CRC. The combined analysis of heterogeneous CRC cohorts thus identified reproducible microbiome biomarkers and accurate disease-predictive models that can form the basis for clinical prognostic tests and hypothesis-driven mechanistic studies.

Effects of HIV viremia on the gastrointestinal microbiome of young MSM

OBJECTIVE

We employed a high-dimensional covariate adjustment method in microbiome analysis to better control for behavioural and clinical confounders, and in doing so examine the effects of HIV on the rectal microbiome.

DESIGN

Three hundred and eighty-three MSM were grouped into four HIV viremia categories: HIV negative (n = 200), HIV-positive undetectable (HIV RNA < 20 copies/ml; n = 66), HIV-positive suppressed (RNA 20-200 copies/ml; n = 72) and HIV-positive viremic (RNA > 200 copies/ml; n = 45).

METHODS

We performed 16S rRNA gene sequencing on rectal swab samples and used inverse probability of treatment-weighted marginal structural models to examine differences in microbial composition by HIV viremia category.

RESULTS

HIV viremia explained a significant amount of variability in microbial composition in both unadjusted and covariate-adjusted analyses (R = 0.011, P = 0.02). Alterations in bacterial taxa were more apparent with increasing viremia. Relative to the HIV-negative group, HIV-positive undetectable participants showed depletions in Brachyspira, Campylobacter and Parasutterella, while suppressed participants demonstrated depletions in Barnesiella, Brachyspira and Helicobacter. The microbial signature of viremic men was most distinct, showing enrichment in inflammatory genera Peptoniphilus, Porphyromonas and Prevotella and depletion of Bacteroides, Brachyspira and Faecalibacterium, among others.

CONCLUSION

Our study shows that, after accounting for the influence of multiple confounding factors, HIV is associated with dysbiosis in the gastrointestinal microbiome in a dose-dependent manner. This analytic approach may allow for better identification of true microbial associations by limiting the effects of confounding, and thus improve comparability across future studies.

Rare Plankton Subcommunities Are Far More Affected by DNA Extraction Kits Than Abundant Plankton

Advances in high-throughput sequencing technologies allow a more complete study of microbial plankton community composition and diversity, especially in the rare microbial biosphere. The DNA extraction of plankton is a key step for such studies; however, little is known about its influences on the abundant or rare microbial biosphere. Our aim was to quantify the influences of different DNA extraction kits on abundant and rare plankton in the surface waters of a reservoir and provide a reference for the comparisons between microbial community studies with different extraction methods. We evaluated the influence of five common commercial kits on DNA quality, microbial community diversity and composition, and the reproducibility of methods using both 16S and 18S rRNA genes amplicon sequencing. Our data showed that results of Fast DNA Spin Kit for Soil (MPF) had higher α diversity for bacteria and high DNA quality, indicating that it is the most suitable approach for bacterioplankton diversity study. However, DNeasy Blood & Tissue Kit (QD) and QIAamp DNA Mini Kit (QQ) methods could produce results that are easier to replicate for bacteria and eukaryotes, respectively, and were more comparable between studies. The use of different DNA extraction kits had larger influence on the rare taxa compared with abundant taxa. Therefore, the comparability between studies that employed different extraction methods can be improved by removing low-abundance or less-representative OTUs. Collectively, this study provides a comprehensive assessment of the biases associated with DNA extraction for plankton communities from a freshwater reservoir. Our results may guide researchers in experimental design choices for DNA-based ecological studies in aquatic ecosystem.

Uncovering the Uncultivated Majority in Antarctic Soils: Toward a Synergistic Approach

Although Antarctica was once believed to be a sterile environment, it is now clear that the microbial communities inhabiting the Antarctic continent are surprisingly diverse. Until the beginning of the new millennium, little was known about the most abundant inhabitants of the continent: prokaryotes. From then on, however, the rising use of deep sequencing techniques has led to a better understanding of the Antarctic prokaryote diversity and provided insights in the composition of prokaryotic communities in different Antarctic environments. Although these cultivation-independent approaches can produce millions of sequences, linking these data to organisms is hindered by several problems. The largest difficulty is the lack of biological information on large parts of the microbial tree of life, arising from the fact that most microbial diversity on Earth has never been characterized in laboratory cultures. These unknown prokaryotes, also known as microbial dark matter, have been dominantly detected in all major environments on our planet. Laboratory cultures provide access to the complete genome and the means to experimentally verify genomic predictions and metabolic functions and to provide evidence of horizontal gene transfer. Without such well-documented reference data, microbial dark matter will remain a major blind spot in deep sequencing studies. Here, we review our current understanding of prokaryotic communities in Antarctic ice-free soils based on cultivation-dependent and cultivation-independent approaches. We discuss advantages and disadvantages of both approaches and how these strategies may be combined synergistically to strengthen each other and allow a more profound understanding of prokaryotic life on the frozen continent.

Tracing seafood at high spatial resolution using NGS-generated data and machine learning: Comparing microbiome versus SNPs

Developing reliable tools to trace food origin represents a major goal for producers and control authorities. Here, we test the hypothesis whether NGS-generated data could provide a reliable tool to ensure seafood traceability. As a test case, we used the Manila clam Ruditapes philippinarum, a bivalve mollusk of high commercial interest with worldwide distribution, collected in the Venice lagoon sites subjected to prohibition of clam harvesting because of chemical contamination as well as in authorized clam harvesting areas. The results obtained demonstrated that the geographic origin of Manila clam may be more accurately determined basing on microbiome data than single nucleotide polymorphisms. In particular, combining microbiome data with machine-learning techniques, we provide the experimental evidence that it is possible to trace the clam place of origin at high spatial resolution. Considering its low cost and portability, NGS-analysis of microbiome data might represent a cost-effective, high-resolution tool for reliable food traceability.

Evaluation of DESS as a storage medium for microbial community analysis

Microbial ecology research requires sampling strategies that accurately represent the microbial community under study. These communities must typically be transported from the collection location to the laboratory and then stored until they can be processed. However, there is a lack of consensus on how best to preserve microbial communities during transport and storage. Here, we evaluated dimethyl sulfoxide, ethylenediamine tetraacetic acid, saturated salt (DESS) solution as a broadly applicable preservative for microbial ecology experiments. We stored fungus gardens grown by the ant Trachymyrmex septentrionalis in DESS, 15% glycerol, and phosphate buffered saline (PBS) to test their impact on the fungus garden microbial community. Variation in microbial community structure due to differences in preservative type was minimal when compared to variation between ant colonies. Additionally, DESS preserved the structure of a defined mock community more faithfully than either 15% glycerol or PBS. DESS is inexpensive, easy to transport, and effective in preserving microbial community structure. We therefore conclude that DESS is a valuable preservative for use in microbial ecology research.

Comparative genomics of Bacteria commonly identified in the built environment

BACKGROUND

The microbial community of the built environment (BE) can impact the lives of people and has been studied for a variety of indoor, outdoor, underground, and extreme locations. Thus far, these microorganisms have mainly been investigated by culture-based methods or amplicon sequencing. However, both methods have limitations, complicating multi-study comparisons and limiting the knowledge gained regarding in-situ microbial lifestyles. A greater understanding of BE microorganisms can be achieved through basic information derived from the complete genome. Here, we investigate the level of diversity and genomic features (genome size, GC content, replication strand skew, and codon usage bias) from complete genomes of bacteria commonly identified in the BE, providing a first step towards understanding these bacterial lifestyles.

RESULTS

Here, we selected bacterial genera commonly identified in the BE (or "Common BE genomes") and compared them against other prokaryotic genera ("Other genomes"). The "Common BE genomes" were identified in various climates and in indoor, outdoor, underground, or extreme built environments. The diversity level of the 16S rRNA varied greatly between genera. The genome size, GC content and GC skew strength of the "Common BE genomes" were statistically larger than those of the "Other genomes" but were not practically significant. In contrast, the strength of selected codon usage bias (S value) was statistically higher with a large effect size in the "Common BE genomes" compared to the "Other genomes."

CONCLUSION

Of the four genomic features tested, the S value could play a more important role in understanding the lifestyles of bacteria living in the BE. This parameter could be indicative of bacterial growth rates, gene expression, and other factors, potentially affected by BE growth conditions (e.g., temperature, humidity, and nutrients). However, further experimental evidence, species-level BE studies, and classification by BE location is needed to define the relationship between genomic features and the lifestyles of BE bacteria more robustly.

High-throughput identification and diagnostics of pathogens and pests: Overview and practical recommendations

High-throughput identification technologies provide efficient tools for understanding the ecology and functioning of microorganisms. Yet, these methods have been only rarely used for monitoring and testing ecological hypotheses in plant pathogens and pests in spite of their immense importance in agriculture, forestry and plant community dynamics. The main objectives of this manuscript are the following: (a) to provide a comprehensive overview about the state-of-the-art high-throughput quantification and molecular identification methods used to address population dynamics, community ecology and host associations of microorganisms, with a specific focus on antagonists such as pathogens, viruses and pests; (b) to compile available information and provide recommendations about specific protocols and workable primers for bacteria, fungi, oomycetes and insect pests; and (c) to provide examples of novel methods used in other microbiological disciplines that are of great potential use for testing specific biological hypotheses related to pathology. Finally, we evaluate the overall perspectives of the state-of-the-art and still evolving methods for diagnostics and population- and community-level ecological research of pathogens and pests.

Progress in characterizing the linkage between Fusobacterium nucleatum and gastrointestinal cancer

Microbiome research is a rapidly advancing field in human cancers. Fusobacterium nucleatum is an oral bacterium, indigenous to the human oral cavity, that plays a role in periodontal disease. Recent studies have found that F. nucleatum can promote gastrointestinal tumor progression and affect the prognosis of the disease. In addition, F. nucleatum may contribute to the chemo-resistance of gastrointestinal cancers. This review summarizes recent progress in the pathogenesis of F. nucleatum and its impact on gastrointestinal cancer.

Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments

Background

Taxonomic profiling of ribosomal RNA (rRNA) sequences has been the accepted norm for inferring the composition of complex microbial ecosystems. Quantitative Insights Into Microbial Ecology (QIIME) and mothur have been the most widely used taxonomic analysis tools for this purpose, with MAPseq and QIIME 2 being two recently released alternatives. However, no independent and direct comparison between these four main tools has been performed. Here, we compared the default classifiers of MAPseq, mothur, QIIME, and QIIME 2 using synthetic simulated datasets comprised of some of the most abundant genera found in the human gut, ocean, and soil environments. We evaluate their accuracy when paired with both different reference databases and variable sub-regions of the 16S rRNA gene.

Findings

We show that QIIME 2 provided the best recall and F-scores at genus and family levels, together with the lowest distance estimates between the observed and simulated samples. However, MAPseq showed the highest precision, with miscall rates consistently <2%. Notably, QIIME 2 was the most computationally expensive tool, with CPU time and memory usage almost 2 and 30 times higher than MAPseq, respectively. Using the SILVA database generally yielded a higher recall than using Greengenes, while assignment results of different 16S rRNA variable sub-regions varied up to 40% between samples analysed with the same pipeline.

Conclusions

Our results support the use of either QIIME 2 or MAPseq for optimal 16S rRNA gene profiling, and we suggest that the choice between the two should be based on the level of recall, precision, and/or computational performance required.

Impact of DNA Sequencing and Analysis Methods on 16S rRNA Gene Bacterial Community Analysis of Dairy Products

DNA sequencing and analysis methods were compared for 16S rRNA V4 PCR amplicon and genomic DNA (gDNA) mock communities encompassing nine bacterial species commonly found in milk and dairy products. The two communities comprised strain-specific DNA that was pooled before (gDNA) or after (PCR amplicon) the PCR step. The communities were sequenced on the Illumina MiSeq and Ion Torrent PGM platforms and then analyzed using the QIIME 1 (UCLUST) and Divisive Amplicon Denoising Algorithm 2 (DADA2) analysis pipelines with taxonomic comparisons to the Greengenes and Ribosomal Database Project (RDP) databases. Examination of the PCR amplicon mock community with these methods resulted in operational taxonomic units (OTUs) and amplicon sequence variants (ASVs) that ranged from 13 to 118 and were dependent on the DNA sequencing method and read assembly steps. The additional 4 to 109 OTUs/ASVs (from 9 OTUs/ASVs) included assignments to spurious taxa and sequence variants of the 9 species included in the mock community. Comparisons between the gDNA and PCR amplicon mock communities showed that combining gDNAs from the different strains prior to PCR resulted in up to 8.9-fold greater numbers of spurious OTUs/ASVs. However, the DNA sequencing method and paired-end read assembly steps conferred the largest effects on predictions of bacterial diversity, with effect sizes of 0.88 (Bray-Curtis) and 0.32 (weighted Unifrac), independent of the mock community type. Overall, DNA sequencing performed with the Ion Torrent PGM and analyzed with DADA2 and the Greengenes database resulted in the most accurate predictions of the mock community phylogeny, taxonomy, and diversity.IMPORTANCE Validated methods are urgently needed to improve DNA sequence-based assessments of complex bacterial communities. In this study, we used 16S rRNA PCR amplicon and gDNA mock community standards, consisting of nine, dairy-associated bacterial species, to evaluate the most commonly applied 16S rRNA marker gene DNA sequencing and analysis platforms used in evaluating dairy and other bacterial habitats. Our results show that bacterial metataxonomic assessments are largely dependent on the DNA sequencing platform and read curation method used. DADA2 improved sequence annotation compared with QIIME 1, and when combined with the Ion Torrent PGM DNA sequencing platform and the Greengenes database for taxonomic assignment, the most accurate representation of the dairy mock community standards was reached. This approach will be useful for validating sample collection and DNA extraction methods and ultimately investigating bacterial population dynamics in milk- and dairy-associated environments.

The contribution of respiratory microbiome analysis to a treatable traits model of care

The composition of the airway microbiome in patients with chronic airway diseases, such as severe asthma, chronic obstructive pulmonary disease (COPD), bronchiectasis and cystic fibrosis (CF), has the potential to inform a precision model of clinical care. Patients with these conditions share overlapping disease characteristics, including airway inflammation and airflow limitation. The clinical management of chronic respiratory conditions is increasingly moving away from a one-size-fits-all model based on primary diagnosis, towards care targeting individual disease traits, and is particularly useful for subgroups of patients who respond poorly to conventional therapies. Respiratory microbiome analysis is an important potential contributor to such a 'treatable traits' approach, providing insight into both microbial drivers of airways disease, and the selective characteristics of the changing lower airway environment. We explore the potential to integrate respiratory microbiome analysis into a treatable traits model of clinical care and provide a practical guide to the application and clinical interpretation of respiratory microbiome analysis.

Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in “omic” data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent “omics” approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.

Rabbit Microbiota Changes Throughout the Intestinal Tract

To gain insight into the importance of carefully selecting the sampling area for intestinal microbiota studies, cecal and fecal microbial communities of Caldes meat rabbit were characterized. The animals involved in the study were divided in two groups according to the feed intake level they received during the fattening period; ad libitum (n = 10) or restricted to 75% of ad libitum intake (n = 11). Cecum and internal hard feces were sampled from sacrificed animals. Assessment of bacterial and archaeal populations was performed by means of Illumina sequencing of 16S rRNA gene amplicons in a MiSeq platform. A total of 596 operational taxonomic units (OTUs) were detected using QIIME software. Taxonomic assignment revealed that microbial diversity was dominated by phyla Firmicutes (76.42%), Tenericutes (7.83%), and Bacteroidetes (7.42%); kingdom Archaea was presented at low percentage (0.61%). No significant differences were detected between sampling origins in microbial diversity or richness assessed using two alpha-diversity indexes: Shannon and the observed number of OTUs. However, the analysis of variance at genus level revealed a higher presence of genera Clostridium, Anaerofustis, Blautia, Akkermansia, rc4-4, and Bacteroides in cecal samples. By contrast, genera Oscillospira and Coprococcus were found to be overrepresented in feces, suggesting that bacterial species of these genera would act as fermenters at the end of feed digestion process. At the lowest taxonomic level, 83 and 97 OTUs in feces and cecum, respectively, were differentially represented. Multivariate statistical assessment revealed that sparse partial least squares discriminant analysis (sPLS-DA) was the best approach for this purpose. Interestingly, the majority of the most discriminative OTUs selected by sPLS-DA were found to be differentially represented between sampling origins in univariate analysis. Our study provides evidence that the choice of intestinal sampling area is relevant due to important differences in some taxa's relative abundance that have been revealed between rabbits' cecal and fecal microbiota. An appropriate sampling intestinal area should be chosen in each microbiota assessment.

Comprehensive Functional Analysis of the Enterococcus faecalis Core Genome Using an Ordered, Sequence-Defined Collection of Insertional Mutations in Strain OG1RF

The robust ability of Enterococcus faecalis to survive outside the host and to spread via oral-fecal transmission and its high degree of intrinsic and acquired antimicrobial resistance all complicate the treatment of hospital-acquired enterococcal infections. The conserved E. faecalis core genome serves as an important genetic scaffold for evolution of this bacterium in the modern health care setting and also provides interesting vaccine and drug targets. We used an innovative pooling/sequencing strategy to map a large collection of arrayed transposon insertions in E. faecalis OG1RF and generated an arrayed library of defined mutants covering approximately 70% of the OG1RF genome. Then, we performed high-throughput transposon sequencing experiments using this library to determine core genomic determinants of bile resistance in OG1RF. This collection is a valuable resource for comprehensive, functional enterococcal genomics using both traditional and high-throughput approaches and enables immediate recovery of mutants of interest.

Enterococcus faecalis is a common commensal bacterium in animal gastrointestinal (GI) tracts and a leading cause of opportunistic infections of humans in the modern health care setting. E. faecalis OG1RF is a plasmid-free strain that contains few mobile elements yet retains the robust survival characteristics, intrinsic antibiotic resistance, and virulence traits characteristic of most E. faecalis genotypes. To facilitate interrogation of the core enterococcal genetic determinants for competitive fitness in the GI tract, biofilm formation, intrinsic antimicrobial resistance, and survival in the environment, we generated an arrayed, sequence-defined set of chromosomal transposon insertions in OG1RF. We used an orthogonal pooling strategy in conjunction with Illumina sequencing to identify a set of mutants with unique, single Himar-based transposon insertions. The mutants contained insertions in 1,926 of 2,651 (72.6%) annotated open reading frames and in the majority of hypothetical protein-encoding genes and intergenic regions greater than 100 bp in length, which could encode small RNAs. As proof of principle of the usefulness of this arrayed transposon library, we created a minimal input pool containing 6,829 mutants chosen for maximal genomic coverage and used an approach that we term SMarT (sequence-defined marinertechnology) transposon sequencing (TnSeq) to identify numerous genetic determinants of bile resistance in E. faecalis OG1RF. These included several genes previously associated with bile acid resistance as well as new loci. Our arrayed library allows functional screening of a large percentage of the genome with a relatively small number of mutants, reducing potential effects of bottlenecking, and enables immediate recovery of mutants following competitions.

IMPORTANCE The robust ability of Enterococcus faecalis to survive outside the host and to spread via oral-fecal transmission and its high degree of intrinsic and acquired antimicrobial resistance all complicate the treatment of hospital-acquired enterococcal infections. The conserved E. faecalis core genome serves as an important genetic scaffold for evolution of this bacterium in the modern health care setting and also provides interesting vaccine and drug targets. We used an innovative pooling/sequencing strategy to map a large collection of arrayed transposon insertions in E. faecalis OG1RF and generated an arrayed library of defined mutants covering approximately 70% of the OG1RF genome. Then, we performed high-throughput transposon sequencing experiments using this library to determine core genomic determinants of bile resistance in OG1RF. This collection is a valuable resource for comprehensive, functional enterococcal genomics using both traditional and high-throughput approaches and enables immediate recovery of mutants of interest.

Microbiome Interaction Networks and Community Structure From Laboratory-Reared and Field-Collected Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus Mosquito Vectors

Microbial interactions are an underappreciated force in shaping insect microbiome communities. Although pairwise patterns of symbiont interactions have been identified, we have a poor understanding regarding the scale and the nature of co-occurrence and co-exclusion interactions within the microbiome. To characterize these patterns in mosquitoes, we sequenced the bacterial microbiome of Aedes aegypti, Ae. albopictus, and Culex quinquefasciatus caught in the field or reared in the laboratory and used these data to generate interaction networks. For collections, we used traps that attracted host-seeking or ovipositing female mosquitoes to determine how physiological state affects the microbiome under field conditions. Interestingly, we saw few differences in species richness or microbiome community structure in mosquitoes caught in either trap. Co-occurrence and co-exclusion analysis identified 116 pairwise interactions substantially increasing the list of bacterial interactions observed in mosquitoes. Networks generated from the microbiome of Ae. aegypti often included highly interconnected hub bacteria. There were several instances where co-occurring bacteria co-excluded a third taxa, suggesting the existence of tripartite relationships. Several associations were observed in multiple species or in field and laboratory-reared mosquitoes indicating these associations are robust and not influenced by environmental or host factors. To demonstrate that microbial interactions can influence colonization of the host, we administered symbionts to Ae. aegypti larvae that either possessed or lacked their resident microbiota. We found that the presence of resident microbiota can inhibit colonization of particular bacterial taxa. Our results highlight that microbial interactions in mosquitoes are complex and influence microbiome composition.

Quantification of variation and the impact of biomass in targeted 16S rRNA gene sequencing studies

BACKGROUND

Recent advances in sequencing technologies and bioinformatics tools have allowed for large-scale microbiome studies that are rapidly advancing medical research. However, small changes in technique or analysis can significantly alter the results and lead to conflicting findings. Quantifying the technical versus biological variation expected in targeted 16S rRNA gene sequencing studies and how this variation changes with input biomass is critical to guide meaningful interpretation of the current literature and plan future research.

RESULTS

Data were compiled from 469 sequencing libraries across 19 separate targeted 16S rRNA gene sequencing runs over a 2.5-year time period. Following removal of contaminant sequences identified from negative controls, 244 samples retained sufficient reads for further analysis. Coefficients of variation for intra- and inter-assay variation from repeated measurements of a bacterial mock community ranged from 8.7 to 37.6% (intra) and 15.6 to 80.5% (inter) for all but one genus of bacteria whose relative abundance was greater than 1%. Intra- versus inter-assay Bray-Curtis pairwise distances for a single stool sample were 0.11 versus 0.31, whereas intra-assay variation from repeat stool samples from the same donor was greater at 0.38 (Wilcoxon p = 0.001). A dilution series of the bacterial mock community was used to assess the effect of input biomass on variability. Pairwise distances increased with more dilute samples, and estimates of relative abundance became unreliable below approximately 100 copies of the 16S rRNA gene per microliter. Using this data, we created a prediction model to estimate the expected variation in microbiome measurements for given input biomass and relative abundance values.

CONCLUSIONS

Well-controlled microbiome studies are sufficiently robust to capture small biological effects and can achieve levels of variability consistent with clinical assays. Relative abundance is negatively associated with measures of variability and has a stronger effect on variability than does absolute biomass, suggesting that it is feasible to detect differences in bacterial populations in very low-biomass samples. Further, by quantifying the effect of biomass and relative abundance on compositional variability, we developed a tool for defining the expected variance in a given microbiome study.

Comparison of microbiomes of cold-water corals Primnoa pacifica and Primnoa resedaeformis, with possible link between microbiome composition and host genotype

Cold-water corals provide critical habitats for a multitude of marine species, but are understudied relative to tropical corals. Primnoa pacifica is a cold-water coral prevalent throughout Alaskan waters, while another species in the genus, Primnoa resedaeformis, is widely distributed in the Atlantic Ocean. This study examined the V4-V5 region of the 16S rRNA gene after amplifying and pyrosequencing bacterial DNA from samples of these species. Key differences between the two species' microbiomes included a robust presence of bacteria belonging to the Chlamydiales order in most of the P. pacifica samples, whereas no more than 2% of any microbial community from P. resedaeformis comprised these bacteria. Microbiomes of P. resedaeformis exhibited higher diversity than those of P. pacifica, and the two species largely clustered separately in a principal coordinate analysis. Comparison of P. resedaeformis microbiomes from samples collected in two submarine canyons revealed a significant difference between locations. This finding mirrored significant genetic differences among the P. resedaeformis from the two canyons based upon population genetic analysis of microsatellite loci. This study presents the first report of microbiomes associated with these two coral species.

Comparing 16S rDNA amplicon sequencing and hybridization capture for pea aphid microbiota diversity analysis

OBJECTIVE

Targeted sequencing of 16S rDNA amplicons is routinely used for microbial community profiling but this method suffers several limitations such as bias affinity of universal primers and short read size. Gene capture by hybridization represents a promising alternative. Here we used a metagenomic extract from the pea aphid Acyrthosiphon pisum to compare the performances of two widely used PCR primer pairs with DNA capture, based on solution hybrid selection.

RESULTS

All methods produced an exhaustive description of the 8 bacterial taxa known to be present in this sample. In addition, the methods yielded similar quantitative results, with the number of reads strongly correlating with quantitative PCR controls. Both methods can thus be considered as qualitatively and quantitatively robust on such a sample with low microbial complexity.

MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

Motivation

Microbial communities play important roles in the function and maintenance of various biosystems, ranging from the human body to the environment. A major challenge in microbiome research is the classification of microbial communities of different environments or host phenotypes. The most common and cost-effective approach for such studies to date is 16S rRNA gene sequencing. Recent falls in sequencing costs have increased the demand for simple, efficient and accurate methods for rapid detection or diagnosis with proved applications in medicine, agriculture and forensic science. We describe a reference- and alignment-free approach for predicting environments and host phenotypes from 16S rRNA gene sequencing based on k-mer representations that benefits from a bootstrapping framework for investigating the sufficiency of shallow sub-samples. Deep learning methods as well as classical approaches were explored for predicting environments and host phenotypes.

Results

A k-mer distribution of shallow sub-samples outperformed Operational Taxonomic Unit (OTU) features in the tasks of body-site identification and Crohn's disease prediction. Aside from being more accurate, using k-mer features in shallow sub-samples allows (i) skipping computationally costly sequence alignments required in OTU-picking and (ii) provided a proof of concept for the sufficiency of shallow and short-length 16S rRNA sequencing for phenotype prediction. In addition, k-mer features predicted representative 16S rRNA gene sequences of 18 ecological environments, and 5 organismal environments with high macro-F1 scores of 0.88 and 0.87. For large datasets, deep learning outperformed classical methods such as Random Forest and Support Vector Machine.

Availability and implementation

The software and datasets are available at https://llp.berkeley.edu/micropheno.

Supplementary information

Supplementary data are available at Bioinformatics online.

Activity-Based Protein Profiling-Enabling Multimodal Functional Studies of Microbial Communities

Microorganisms living in community are critical to life on Earth, playing numerous and profound roles in the environment and human and animal health. Though their essentiality to life is clear, the mechanistic underpinnings of community structure, interactions, and functions are largely unexplored and in need of function-dependent technologies to unravel the mysteries. Activity-based protein profiling offers unprecedented molecular-level characterization of functions within microbial communities and provides an avenue to determine how external exposures result in functional alterations to microbiomes. Herein, we illuminate the current state and prospective contributions of ABPP as it relates to microbial communities. We provide details on the design, development, and validation of probes, challenges associated with probing in complex microbial communities, provide some specific examples of the biological applications of ABPP in microbes and microbial communities, and highlight potential areas for development. The future of ABPP holds real promise for understanding and considerable impact in microbiome studies associated with personalized medicine, precision agriculture, veterinary health, environmental studies, and beyond.

Head and neck manifestations of neurofibromatosis

Neurofibromatosis is a neurocutaneous systemic disease that occurs in 1:2500 to 3300 live births. Prevalence figures have shown it to be as common as cystic fibrosis or Down's syndrome and more than twice as common as muscular dystrophy. In this study, our experience with 257 cases of neurofibromatosis seen since 1972 is reviewed. Intracranial, bony, and extracranial anomalies are described in the 223 patients (87%) who presented with, or ultimately developed, head and neck manifestations of the disease. The most common intracranial tumor was optic glioma, found in 35 patients (14%), 19 younger than 10 years of age. Acoustic neuromas were diagnosed in eight individuals (3%) and were bilateral in three. The most common skull anomaly was macrocephaly, noted 78 times (30%). Absence of the sphenoid wing occurred in 11 patients (4%) and 19 others (7%) had facial asymmetry due to other skull abnormalities. Extracranial manifestations included neurofibromas of the plexiform and nonplexiform type, Lisch nodules, and cafe-au-lait spots.