Operational Taxonomic Unit Tables Research Articles

Complete pipeline for Oxford Nanopore Technology amplicon sequencing (ONT-AmpSeq): from pre-processing to creating an operational taxonomic unit table.

Amplicon sequencing has long served as a robust method for characterising microbial communities, and despite inherent resolution limitations, it remains a preferred technique, offering cost- and time-effective insights into bacterial compositions. Here, we introduce ONT-AmpSeq, a user-friendly pipeline designed for processing amplicon sequencing data generated from Oxford Nanopore Technology (ONT) devices. Our pipeline enables efficient creation of taxonomically annotated operational taxonomic unit (OTU) tables from ONT sequencing data, with the flexibility to multiplex amplicons on the same barcode. The pipeline encompasses six main steps-statistics, quality filtering, alignment, clustering, polishing, and taxonomic classification-integrating various state-of-the-art software tools. We provide a detailed description of each step, along with performance tests and robustness evaluations using both test data and a ZymoBIOMICS® Microbial Community Standard mock community dataset. Our results demonstrate the ability of ONT-AmpSeq to effectively process ONT amplicon data, offering valuable insights into microbial community composition. Additionally, we discuss the influence of polishing tools on taxonomic insight and the impact of taxonomic annotation methods on the derived microbial composition. Overall, ONT-AmpSeq represents a comprehensive solution for analysing ONT amplicon sequencing data, facilitating streamlined and reliable microbial community analysis. The pipeline, along with test data, is freely available for public use.

A prospective pilot study of gut microbiome in cerebral vasospasm and delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

A recent systematic review indicated that gut–microbiota–brain axis contributes to growth and rupture of intracranial aneurysms. However, gaps were detected in the role of intestinal microbiome in cerebral vasospasm (CVS) after aneurysmal subarachnoid hemorrhage (aSAH). This is the first pilot study aiming to test study feasibility and identify differences in gut microbiota between subjects with and without CVS following aSAH. A prospective nested case–control pilot study with 1:1 matching was conducted recruiting subjects with aSAH: cases with CVS; and controls without CVS based on the clinical picture and structured bedside transcranial Doppler (TCD). Fecal samples for microbiota analyses by means of 16S rRNA gene amplicon sequencing were collected within the first 96 h after ictus. Operational taxonomic unit tables were constructed, diversity metrics calculated, phylogenetic trees built, and differential abundance analysis (DAA) performed. At baseline, the groups did not differ significantly in basic demographic and aneurysm-related characteristics (p > 0.05). Alpha-diversity (richness and Shannon Index) was significantly reduced in cases of middle cerebral artery (MCA) vasospasm (p < 0.05). In DAA, relative abundance of genus Acidaminococcus was associated with MCA vasospasm (p = 0.00013). Two butyrate-producing genera, Intestinimonas and Butyricimonas, as well as [Clostridium] innocuum group had the strongest negative correlation with the mean blood flow velocity in anterior cerebral arteries (p < 0.01; rho = − 0.63; − 0.57, and − 0.57, respectively). In total, 16 gut microbial genera were identified to correlate with TCD parameters, and two intestinal genera correlated with outcome upon discharge. In this pilot study, we prove study feasibility and present the first preliminary evidence of gut microbiome signature associating with CVS as a significant cause of stroke in subjects with aSAH.

Effects of astaxanthin on gut microbiota of polo ponies during deconditioning and reconditioning periods.

To determine the effects of astaxanthin (ASTX) supplementation on the equine gut microbiota during a deconditioning-reconditioning cycle, 12 polo ponies were assigned to a control (CON; n = 6) or supplemented (ASTX; 75 mg ASTX daily orally; n = 6) group. All horses underwent a 16-week deconditioning period, with no forced exercise, followed by a 16-week reconditioning program where physical activity gradually increased. Fecal samples were obtained at the beginning of the study (Baseline), after deconditioning (PostDecon), after reconditioning (PostRecon), and 16 weeks after the cessation of ASTX supplementation (Washout). Following DNA extraction from fecal samples, v4 of 16S was amplified and sequenced to determine operational taxonomic unit tables and α-diversity and β-diversity indices. The total number of observed species was greater at Baseline than PostDecon, PostRecon, and Washout (p ≤ 0.02). A main effect of ASTX (p = 0.01) and timepoint (p = 0.01) was observed on β-diversity, yet the variability of timepoint was greater (13%) than ASTX (6%), indicating a greater effect of timepoint than ASTX. Deconditioning and reconditioning periods affected the abundance of the Bacteroidetes and Fibrobacteres phyla. Physical activity and ASTX supplementation affect the equine gut microbiome, yet conditioning status may have a greater impact.

Towards a unified medical microbiome ecology of the OMU for metagenomes and the OTU for microbes

BackgroundMetagenomic sequencing technologies offered unprecedented opportunities and also challenges to microbiology and microbial ecology particularly. The technology has revolutionized the studies of microbes and enabled the high-profile human microbiome and earth microbiome projects. The terminology-change from microbes to microbiomes signals that our capability to count and classify microbes (microbiomes) has achieved the same or similar level as we can for the biomes (macrobiomes) of plants and animals (macrobes). While the traditional investigations of macrobiomes have usually been conducted through naturalists’ (Linnaeus & Darwin) naked eyes, and aerial and satellite images (remote-sensing), the large-scale investigations of microbiomes have been made possible by DNA-sequencing-based metagenomic technologies. Two major types of metagenomic sequencing technologies—amplicon sequencing and whole-genome (shotgun sequencing)—respectively generate two contrastingly different categories of metagenomic reads (data)—OTU (operational taxonomic unit) tables representing microorganisms and OMU (operational metagenomic unit), a new term coined in this article to represent various cluster units of metagenomic genes.ResultsThe ecological science of microbiomes based on the OTU representing microbes has been unified with the classic ecology of macrobes (macrobiomes), but the unification based on OMU representing metagenomes has been rather limited. In a previous series of studies, we have demonstrated the applications of several classic ecological theories (diversity, composition, heterogeneity, and biogeography) to the studies of metagenomes. Here I push the envelope for the unification of OTU and OMU again by demonstrating the applications of metacommunity assembly and ecological networks to the metagenomes of human gut microbiomes. Specifically, the neutral theory of biodiversity (Sloan’s near neutral model), Ning et al.stochasticity framework, core-periphery network, high-salience skeleton network, special trio-motif, and positive-to-negative ratio are applied to analyze the OMU tables from whole-genome sequencing technologies, and demonstrated with seven human gut metagenome datasets from the human microbiome project.ConclusionsAll of the ecological theories demonstrated previously and in this article, including diversity, composition, heterogeneity, stochasticity, and complex network analyses, are equally applicable to OMU metagenomic analyses, just as to OTU analyses. Consequently, I strongly advocate the unification of OTU/OMU (microbiomes) with classic ecology of plants and animals (macrobiomes) in the context of medical ecology.

Detection of cardiovascular disease using explainable artificial intelligence and gut microbiota data

Purpose:Gut microbiota are defined as the microbial population of the intestines. They include various types of bacteria which can influence and predict the existence or onset of some specific diseases. Therefore, it is a common practice in medicine to analyze the gut microbiota for diagnostic purposes by analyzing certain measurable biochemical features associated with the disease under investigation. However, the evaluation of all the data collected from the gut microbiota is a labor-intensive process. Artificial Intelligence (AI) may be a helpful tool to identify the hidden patterns in gut microbiota for the detection of disease and other classification problems. Methods:In this study, we propose a deep neural model based on a one-dimensional convolutional neural network (1D-CNN) to detect cardiovascular disease using bacterial taxonomy and OTU (Operational Taxonomic Unit) table data. The developed AI method is compared to classical machine learning algorithms, regression, boosting algorithms and a deep model, Tabular Network (TabNet), developed for tabular data and obtained outperforming classification results. Results:According to AUC (Area Under Curve) values, boosting and regression methods outperformed the classical machine learning methods. However, the highest value of 97.09 AUC was obtained with the developed 1D-CNN model by using bacterial taxonomy data even with less then expected number of samples. Using explainable AI, nine bacteria were identified which the models find important for classification. Conclusion:The proposed method is robust and well adapted to taxonomy data in tabular form. It can be easily adapted to detect other diseases by using taxonomy data. The study also investigated the effect on barcode sequence for the classification, but the result showed that barcode sequences do not contribute to the bacterial taxonomy data for the estimation of CVD disease.

Reorganizing heterogeneous information from host–microbe interaction reveals innate associations among samples

AbstractThe information on host–microbe interactions contained in the operational taxonomic unit (OTU) abundance table can serve as a clue to understanding the biological traits of OTUs and samples. Some studies have inferred the taxonomies or functions of OTUs by constructing co‐occurrence networks, but co‐occurrence networks can only encompass a small fraction of all OTUs due to the high sparsity of the OTU table. There is a lack of studies that intensively explore and use the information on sample‐OTU interactions. This study constructed a sample‐OTU heterogeneous information network and represented the nodes in the network through the heterogeneous graph embedding method to form the OTU space and sample space. Taking advantage of the represented OTU and sample vectors combined with the original OTU abundance information, an Integrated Model of Embedded Taxonomies and Abundance (IMETA) was proposed for predicting sample attributes, such as phenotypes and individual diet habits. Both the OTU space and sample space contain reasonable biological or medical semantic information, and the IMETA using embedded OTU and sample vectors can have stable and good performance in the sample classification tasks. This suggests that the embedding representation based on the sample‐OTU heterogeneous information network can provide more useful information for understanding microbiome samples. This study conducted quantified representations of the biological characteristics within the OTUs and samples, which is a good attempt to increase the utilization rate of information in the OTU abundance table, and it promotes a deeper understanding of the underlying knowledge of human microbiome.

Optimal sampling and analysis methods for clinical diagnostics of vaginal microbiome

Next-generation sequencing-based microbiological analysis is a complex way to profile vaginal microbiome samples since each step affects the results gained. Methodologies for sample collection lack golden standards. We compared Puritan DNA/RNA swab (PS) and Copan FLOQ swab (CS) and provided consistent and reliable microbiome profiles analyzed by 16S rRNA gene sequencing. We collected two consecutive vaginal samples utilizing PS with room temperature storing and CS with instant freezing from 26 women. Variable region 4 of bacterial 16S rRNA gene was amplified with single PCR by custom-designed dual-indexed primers and sequenced with Illumina MiSeq system. Read quality control, operational taxonomic unit tables, and alpha and beta diversities analysis were performed, and community richness, diversity, and evenness were evaluated and compared between the two samplings and tests. Nineteen sample pairs produced detectable, intact DNA during the extraction protocol and/or further microbial profiles. Alpha bacterial diversity indices were independent on the collection protocol. No significant statistical differences were found in the measured beta diversity metrics between the collection methods. Of the women, 43% had Lactobacillus-dominated vaginal microbiome profile despite of collection method. Previously reported important vaginal microbiome phyla Actinobacteria, Bacteroidetes, Firmicutes, Fusobacteria, and Proteobacteria were present in the sample set although their relative abundances varied among individuals. PS and CS enable constant vaginal microbiota sampling. The PS method with no need for instant freezing is suitable for on-site collections at clinics. Furthermore, it seems to be possible to take two samples instead of one with constant microbiological results.

Characterizing the vaginal microbiome in patients with high grade cervical dysplasia (277)

Objectives: The influence of the vaginal microbiome on risk for cervical dysplasia and cancer is unknown. It is hypothesized that the microbiome can impact the infectivity of human papillomavirus, and some bacteria may even have pro-oncogenic effects. The impact of dysplasia and cancer treatment on the microbiome is also unknown. The objective of this study was to characterize the vaginal microbiome of patients with high-grade cervical dysplasia and determine whether microbiome phylotypes changed after the cervical loop electrosurgical excision procedure (LEEP). Methods: Pre-menopausal women undergoing LEEP for high-grade cervical dysplasia were recruited. Vaginal swabs were collected prior to the LEEP procedure and six weeks post-procedure. DNA was extracted, and the hypervariable V4 region of the bacterial and archaeal 16S rRNA gene was amplified and sequenced using Illumina next-generation sequencing. Sequenced reads were processed and quality-filtered using AdapterRemoval and clustered into operational taxonomic units (OTUs) using a closed-reference OTU-picking strategy implemented in QIIME. A 97% identity threshold was used, and taxonomy was assigned using the EZTaxon database. The resulting OTU table was rarefied to 10,000 sequences. Conclusions: Our findings are consistent with existing data demonstrating an association between an L. iners-dominant microbiome and severe cervical dysplasia. Additionally, a more diverse and less Lactobacillus-dominated vaginal microbiome has been shown to be more prevalent amongst HPV-positive women. In contrast to published data, our findings, while based on a small number of cases, did not demonstrate consistent alterations in vaginal microbiota after the LEEP procedure. We plan to collect more longitudinal data on the persistence and progression of dysplasia in association with vaginal microbiomes to better understand the relationship between these two entities. Objectives: The influence of the vaginal microbiome on risk for cervical dysplasia and cancer is unknown. It is hypothesized that the microbiome can impact the infectivity of human papillomavirus, and some bacteria may even have pro-oncogenic effects. The impact of dysplasia and cancer treatment on the microbiome is also unknown. The objective of this study was to characterize the vaginal microbiome of patients with high-grade cervical dysplasia and determine whether microbiome phylotypes changed after the cervical loop electrosurgical excision procedure (LEEP). Methods: Pre-menopausal women undergoing LEEP for high-grade cervical dysplasia were recruited. Vaginal swabs were collected prior to the LEEP procedure and six weeks post-procedure. DNA was extracted, and the hypervariable V4 region of the bacterial and archaeal 16S rRNA gene was amplified and sequenced using Illumina next-generation sequencing. Sequenced reads were processed and quality-filtered using AdapterRemoval and clustered into operational taxonomic units (OTUs) using a closed-reference OTU-picking strategy implemented in QIIME. A 97% identity threshold was used, and taxonomy was assigned using the EZTaxon database. The resulting OTU table was rarefied to 10,000 sequences. Conclusions: Our findings are consistent with existing data demonstrating an association between an L. iners-dominant microbiome and severe cervical dysplasia. Additionally, a more diverse and less Lactobacillus-dominated vaginal microbiome has been shown to be more prevalent amongst HPV-positive women. In contrast to published data, our findings, while based on a small number of cases, did not demonstrate consistent alterations in vaginal microbiota after the LEEP procedure. We plan to collect more longitudinal data on the persistence and progression of dysplasia in association with vaginal microbiomes to better understand the relationship between these two entities.

Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation.

ObjectiveAn approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where analysis methods are rapidly evolving. This re-analysis of an existing dataset aimed to determine whether updated bioinformatic and statistical techniques affect clinical inferences.MethodsA prior study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We obtained unprocessed sequencing data from multiple variable regions, processed operational taxonomic unit (OTU) tables from the original analysis, and de-identified clinical data. We re-processed sequencing data with DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to the original OTU tables; taxa from different variable regions after updated processing were also compared. Bayesian graphical compositional regression (BGCR) was used to test for associations between microbial compositions and clinical phenotypes (e.g., MUI versus control) while adjusting for clinical covariates. Several techniques were used to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, again while adjusting for potentially confounding variables.ResultsOf taxa identified through updated bioinformatic processing, only 40% were identified originally, though taxa identified through both methods represented >99% of the sequencing data in terms of relative abundance. Different 16S rRNA gene regions resulted in different recovered taxa. With BGCR analysis, there was a low (33.7%) probability of an association between overall microbial compositions and clinical phenotype. However, when microbial data are clustered into bacterial communities, we confirmed that bacterial communities are associated with MUI. Contrary to the originally published analysis, we did not identify different associations by age group, which may be due to the incorporation of different covariates in statistical models.ConclusionsUpdated bioinformatic processing techniques recover different taxa compared to earlier techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. While overall microbial compositions are not associated with MUI, we confirmed associations between certain communities of bacteria and MUI. Incorporation of several covariates that are associated with the urinary microbiome improved inferences when assessing for associations between bacterial communities and MUI in multivariable models.

Time after time: detecting annual patterns in stream bacterial biofilm communities.

SummaryTo quantify the major environmental drivers of stream bacterial population dynamics, we modelled temporal differences in stream bacterial communities to quantify community shifts, including those relating to cyclical seasonal variation and more sporadic bloom events. We applied Illumina MiSeq 16S rRNA bacterial gene sequencing of 892 stream biofilm samples, collected monthly for 36‐months from six streams. The streams were located a maximum of 118 km apart and drained three different catchment types (forest, urban and rural land uses). We identified repeatable seasonal patterns among bacterial taxa, allowing their separation into three ecological groupings, those following linear, bloom/trough and repeated, seasonal trends. Various physicochemical parameters (light, water and air temperature, pH, dissolved oxygen, nutrients) were linked to temporal community changes. Our models indicate that bloom events and seasonal episodes modify biofilm bacterial populations, suggesting that distinct microbial taxa thrive during these events including non‐cyanobacterial community members. These models could aid in determining how temporal environmental changes affect community assembly and guide the selection of appropriate statistical models to capture future community responses to environmental change.

A specific microbiota signature is associated to various degrees of ulcerative colitis as assessed by a machine learning approach

ABSTRACT Ulcerative colitis (UC) is a complex immune-mediated disease in which the gut microbiota plays a central role, and may determine prognosis and disease progression. We aimed to assess whether a specific microbiota profile, as measured by a machine learning approach, can be associated with disease severity in patients with UC. In this prospective pilot study, consecutive patients with active or inactive UC and healthy controls (HCs) were enrolled. Stool samples were collected for fecal microbiota assessment analysis by 16S rRNA gene sequencing approach. A machine learning approach was used to predict the groups’ separation. Thirty-six HCs and forty-six patients with UC (20 active and 26 inactive) were enrolled. Alpha diversity was significantly different between the three groups (Shannon index: p-values: active UC vs HCs = 0.0005; active UC vs inactive UC = 0.0273; HCs vs inactive UC = 0.0260). In particular, patients with active UC showed the lowest values, followed by patients with inactive UC, and HCs. At species level, we found high levels of Bifidobacterium adolescentis and Haemophilus parainfluenzae in inactive UC and active UC, respectively. A specific microbiota profile was found for each group and was confirmed with sparse partial least squares discriminant analysis, a machine learning-supervised approach. The latter allowed us to observe a perfect class prediction and group separation using the complete information (full Operational Taxonomic Unit table), with a minimal loss in performance when using only 5% of features. A machine learning approach to 16S rRNA data identifies a bacterial signature characterizing different degrees of disease activity in UC. Follow-up studies will clarify whether such microbiota profiling are useful for diagnosis and management.

Reprocessing 16S rRNA Gene Amplicon Sequencing Studies: (Meta)Data Issues, Robustness, and Reproducibility.

High-throughput sequencing technology provides an efficient method for evaluating microbial ecology. Different bioinformatics pipelines can be used to convert 16S ribosomal RNA gene amplicon sequencing data into an operational taxonomic unit (OTU) table that is used to analyze microbial communities. It is important to assess the robustness of these pipelines, each with specific algorithms and/or parameters, and their influence on the outcome of statistical tests. Articles with publicly available datasets on the oral microbiome were searched for, and five datasets were retrieved. These were from studies on changes in microbiota related to smoking, oral cancer, caries, diabetes, or periodontitis. Next, the data was processed with four pipelines based on VSEARCH, USEARCH, mothur, and UNOISE3. OTU tables were rarefied, and differences in α-diversity and β-diversity were tested for different groups in a dataset. Finally, these results were checked for consistency among these example pipelines. Of articles that deposited data, only 57% made all sequencing and metadata available. When processing the datasets, issues were encountered, caused by read characteristics and differences between tools and their defaults in combination with a lack of detail in the methodology of the articles. In general, the four mainstream pipelines provided similar results, but importantly, P-values sometimes differed between pipelines beyond the significance threshold. Our results indicated that for published articles, the description of bioinformatics methods and data deposition should be improved, and regarding reproducibility, that analysis of multiple subsamples is required when using rarefying as library-size normalization method.

Interaction of environmental eukaryotic microorganisms and fungi in the pond-cultured carps: new insights into the potential pathogenic fungi in the freshwater aquaculture

The quality and safety of the aquatic products have gradually become the focus of global attention. In this study, the environmental eukaryotic and fungi communities in pond-cultured grass carp (Ctenopharyngodon idellus) and the koi carp (Cyprinus carpio L.) were investigated. For comparative analysis, the alpha diversity shows that the environmental microbial abundance in the koi carp groups were higher than that in the grass carp groups, while beta diversity reveals that the differences of the microbial community composition and structures in the grass carp groups were significantly higher than those in the koi carp groups. Meanwhile, the environmental microbial diversity of grass carp groups was higher than that of koi carp groups at phylum level, but showed no significant difference at genus level. Additionally, the dominant total phyla were Opisthokonta, Stramenopiles plusAlveolates plusRhizaria, Archaeplastida, Cryptophyceae, and Centrohelida for the 18S rRNA gene and Ciliophora, Chlorophyta, and Ascomycota for the ITS2 rRNA gene in both of the two carp groups. Additionally, annotation analysis showed that the biomarkers in the grass carp groups are significantly higher than those of the koi carp groups. Furthermore, the functional prediction of Funguild showed significant difference in outputs, while similarity in trophic modes and guild types between the two carp groups. Meanwhile, the total relative abundances of animal pathogen, fungal parasite, and plant pathogen were extremely similar between the two carp groups. Surprisingly, one pathogenic fungus of genus Fusarium was identified in both the environments of two carp groups based on filtered operational taxonomic unit tables. Overall, this is the first robust report to understand the characteristics of environmental eukaryotic microorganisms and fungi in the edible and ornamental carps. Our results also provide the basic data for the prevention of fungal diseases and the healthy culture of the carps.

Hierarchical Structured Component Analysis for Microbiome Data Using Taxonomy Assignments.

The recent advent of high-throughput sequencing technology has enabled us to study the associations between human microbiome and diseases. The DNA sequences of microbiome samples are clustered as operational taxonomic units (OTUs) according to their similarity. The OTU table containing counts of OTUs present in each sample is used to measure correlations between OTUs and disease status and find key microbes for prediction of the disease status. Various statistical methods have been proposed for such microbiome data analysis. However, none of these methods reflects the hierarchy of taxonomy information. In this paper, we propose a hierarchical structural component model for microbiome data (HisCoM-microb) using taxonomy information as well as OTU table data. The proposed HisCoM-microb consists of two layers: one for OTUs and the other for taxa at the higher taxonomy level. Then we calculate simultaneously coefficient estimates of OTUs and taxa of the two layers inserted in the hierarchical model. Through this analysis, we can infer the association between taxa or OTUs and disease status, considering the impact of taxonomic structure on disease status. Both simulation study and real microbiome data analysis show that HisCoM-microb can successfully reveal the relations between each taxon and disease status and identify the key OTUs of the disease at the same time.

Assessing and Interpreting the Metagenome Heterogeneity With Power Law.

There are two major sequencing technologies for investigating the microbiome: the amplicon sequencing that generates the OTU (operational taxonomic unit) tables of marker genes (e.g., bacterial 16S-rRNA), and the metagenomic shotgun sequencing that generates metagenomic gene abundance (MGA) tables. The OTU table is the counterpart of species abundance tables in macrobial ecology of plants and animals, and has been the target of numerous ecological and network analyses in recent gold rush for microbiome research and in great efforts for establishing an inclusive theoretical ecology. Nevertheless, MGA analyses have been largely limited to bioinformatics pipelines and ad hoc statistical methods, and systematic approaches to MGAs guided by classic ecological theories are still few. Here, we argue that, the difference between “gene kinds” and “gene species” are nominal, and the metagenome that a microbiota carries is essentially a ‘community’ of metagenomic genes (MGs). Each row of a MGA table represents a metagenome of a microbiota, and the whole MGA table represents a ‘meta-metagenome’ (or an assemblage of metagenomes) of N microbiotas (microbiome samples). Consequently, the same ecological/network analyses used in OTU analyses should be equally applicable to MGA tables. Here we choose to analyze the heterogeneity of metagenome by introducing classic Taylor’s power law (TPL) and its recent extensions in community ecology. Heterogeneity is a fundamental property of metagenome, particularly in the context of human microbiomes. Recent studies have shown that the heterogeneity of human metagenomes is far more significant than that of human genomes. Therefore, without deep understanding of the human metagenome heterogeneity, personalized medicine of the human microbiome-associated diseases is hardly feasible. The TPL extensions have been successfully applied to measure the heterogeneity of human microbiome based on amplicon-sequencing reads of marker genes (e.g., 16s-rRNA). In this article, we demonstrate the analysis of the metagenomic heterogeneity of human gut microbiome at whole metagenome scale (with type-I power law extension) and metagenomic gene scale (type-III), as well as the heterogeneity of gene clusters, respectively. We further examine the influences of obesity, IBD and diabetes on the heterogeneity, which is of important ramifications for the diagnosis and treatment of human microbiome-associated diseases.

Abstract 3326: Mucositis, candidiasis, and associations with the oral microbiome in treatment naive patients with oropharyngeal cancer

Abstract Purpose: Oropharyngeal cancer (OPC) incidence continues to increase dramatically in the US. Accumulating evidence suggests that oral microbiota (communities of microorganisms that reside in the oral cavity) may influence cancer treatment-related toxicities. We sought to examine the composition and diversity of oral microbiota in OPC patients prior to treatment, and identify associations between oral microbiota and subsequent development of oral mucositis (inflammation of mucous membranes) and candidiasis (fungal infection with Candida yeast). Methods: 60 newly diagnosed, treatment naïve, OPC patients were recruited from Moffitt Cancer Center (2015-2017). Patients provided mouthwash-based oral gargles before starting chemoradiation or radiation. DNA was isolated using the QIAGEN DNeasy PowerSoil kit, and the V1-V3 region of the bacterial 16S rRNA gene was sequenced. An operational taxonomic unit table was generated and Ribosomal Database Project Classifier used to assign taxonomy. The development of oral mucositis (no/mild vs. severe) and candidiasis (no vs. yes) during treatment was abstracted from medical records. Comparisons of bacterial relative abundance (Mann Whitney U), alpha diversity (Chao1, Shannon, and Simpson), and beta diversity (Bray-Curtis) were performed in R and its extension (Phyloseq). Results: Of 60 OPC patients, 65% were stage IV, 48% tonsillar and 42% base of tongue, and 40% never smokers. Pre-treatment levels of genus Klebsiella and class Gammaproteobacteria were significantly greater in the oral cavity of patients who had no/mild mucositis vs. those who developed severe mucositis (p&amp;lt;0.04). Genera Atopobium and Mogibacterium, as well as families Coriobacteriaceae and Clostridiales Incertae Sedis, were significantly more abundant in patients who did not develop oral candidiasis compared to those who did (p&amp;lt;0.04). Oral bacterial alpha diversity (intra-subject) was higher in patients who developed vs. did not develop severe mucositis, and lower in patients who developed vs. did not develop candidiasis during treatment. Analyses of beta diversity (inter-subject) showed that the microbial community structures were not significantly different by mucositis or candidiasis development. Conclusions: Microbiome profiling may hold promise as a prognostic biomarker of treatment-related toxicities. Several potentially predictive taxa were identified to be differentially abundant in OPC patients who subsequently developed oral mucositis or candidiasis. Future analyses will evaluate the role of oral microbial resilience (the rate of recovery after disturbance) using oral specimens collected 3 mo. after treatment. Translational research focused on understanding how oral microbiota influence local immune and inflammatory responses may aid in the development of microbiota-based interventions to minimize adverse events. Citation Format: Christine M. Pierce, Stephanie Hogue, Shirlene Paul, Bo-Young Hong, Wildson Vieira da Silva, Maria F. Gomez, Anna R. Giuliano, Jimmy J. Caudell, George M. Weinstock. Mucositis, candidiasis, and associations with the oral microbiome in treatment naive patients with oropharyngeal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3326.

Rarity of microbial species: In search of reliable associations.

The role of microbial interactions in defining the properties of microbiota is a topic of key interest in microbial ecology. Microbiota contain hundreds to thousands of operational taxonomic units (OTUs), most of them rare. This feature of community structure can lead to methodological difficulties: simulations have shown that methods for detecting pairwise associations between OTUs, which presumably reflect interactions, yield problematic results. The performance of association detection tools is impaired when there is a high proportion of zeros in OTU tables. Our goal was to understand the impact of OTU rarity on the detection of associations. We explored the utility of common statistics for testing associations; the sensitivity of alternative association measures; and the performance of network inference tools. We found that a large proportion of pairwise associations, especially negative associations, cannot be reliably tested. This constraint could hamper the identification of candidate biological agents that could be used to control rare pathogens. Identifying testable associations could serve as an objective method for filtering datasets in lieu of current empirical approaches. This trimming strategy could significantly reduce the computational time needed to infer networks and network inference quality. Different possibilities for improving the analysis of associations within microbiota are discussed.

Lower airway microbiota associates with inflammatory phenotype in severe preschool wheeze

Lower airway microbiota associates with inflammatory phenotype in severe preschool wheeze

Linking Associations of Rare Low-Abundance Species to Their Environments by Association Networks.

Studies of microbial communities by targeted sequencing of rRNA genes lead to recovering numerous rare low-abundance taxa with unknown biological roles. We propose to study associations of such rare organisms with their environments by a computational framework based on transformation of the data into qualitative variables. Namely, we analyze the sparse table of putative species or OTUs (operational taxonomic units) and samples generated in such studies, also known as an OTU table, by collecting statistics on co-occurrences of the species and on shared species richness across samples. Based on the statistics we built two association networks, of the rare putative species and of the samples respectively, using a known computational technique, Association networks (Anets) developed for analysis of qualitative data. Clusters of samples and clusters of OTUs are then integrated and combined with metadata of the study to produce a map of associated putative species in their environments. We tested and validated the framework on two types of microbiomes, of human body sites and that of the Populus tree root systems. We show that in both studies the associations of OTUs can separate samples according to environmental or physiological characteristics of the studied systems.

Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates

DNA metabarcoding is promising for cost-effective biodiversity monitoring, but reliable diversity estimates are difficult to achieve and validate. Here we present and validate a method, called LULU, for removing erroneous molecular operational taxonomic units (OTUs) from community data derived by high-throughput sequencing of amplified marker genes. LULU identifies errors by combining sequence similarity and co-occurrence patterns. To validate the LULU method, we use a unique data set of high quality survey data of vascular plants paired with plant ITS2 metabarcoding data of DNA extracted from soil from 130 sites in Denmark spanning major environmental gradients. OTU tables are produced with several different OTU definition algorithms and subsequently curated with LULU, and validated against field survey data. LULU curation consistently improves α-diversity estimates and other biodiversity metrics, and does not require a sequence reference database; thus, it represents a promising method for reliable biodiversity estimation.