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Staphylococcus epidermidis is part of the commensal microbiota of the skin and mucous membranes, though it can also act as a pathogen in certain scenarios, causing a range of infections, including periprosthetic joint infection (PJI). Transcriptomic profiling may provide insights into mechanisms by which S. epidermidis adapts while in a pathogenic compared to a commensal state. Here, a total RNA-sequencing approach was used to profile and compare the transcriptomes of 19 paired PJI-associated S. epidermidis samples from an in vivo clinical source and grown in in vitro laboratory culture. Genomic comparison of PJI-associated and publicly available commensal-state isolates were also compared. Of the 1919 total transcripts found, 145 were from differentially expressed genes (DEGs) when comparing in vivo or in vitro samples. Forty-two transcripts were upregulated and 103 downregulated in in vivo samples. Of note, metal sequestration-associated genes, specifically those related to staphylopine activity (cntA, cntK, cntL, and cntM), were upregulated in a subset of clinical in vivo compared to laboratory grown in vitro samples. About 70% of the total transcripts and almost 50% of the DEGs identified have not yet been annotated. There were no significant genomic differences between known commensal and PJI-associated S. epidermidis isolates, suggesting that differential genomics may not play a role in S. epidermidis pathogenicity. In conclusion, this study provides insights into phenotypic alterations employed by S epidermidis to adapt to infective and non-infected microenvironments, potentially informing future therapeutic targets for related infections.

The microbial community composition in the human gut has a profound effect on human health. This observation has lead to extensive use of microbiome therapies, including over-the-counter ‘probiotic’ treatments intended to alter the composition of the microbiome. Despite so much promise and commercial interest, the factors that contribute to the success or failure of microbiome-targeted treatments remain unclear. We investigate the biotic interactions that lead to successful engraftment of a novel bacterial strain introduced to the microbiome as in probiotic treatments. We use pairwise genome-scale metabolic modeling with a generalized resource allocation constraint to build a network of interactions between taxa that appear in an experimental engraftment study. We create induced sub-graphs using the taxa present in individual samples and assess the likelihood of invader engraftment based on network structure. To do so, we use a generalized Lotka-Volterra model, which we show has strong ability to predict if a particular invader or probiotic will successfully engraft into an individual’s microbiome. Furthermore, we show that the mechanistic nature of the model is useful for revealing which microbe-microbe interactions potentially drive engraftment.

Abstract Ovarian cancer (OC) is the second most common gynecological malignancy and the fifth leading cause of death due to cancer in women in the United States mainly due to the late-stage diagnosis of this cancer. It is, therefore, critical to identify potential indicators to aid in early detection and diagnosis of this disease. We investigated the microbiome associated with OC and its potential role in detection, progression as well as prognosis of the disease. We identified a distinct OC microbiome with general enrichment of several microbial taxa, including Dialister, Corynebacterium, Prevotella, and Peptoniphilus in the OC cohort in all body sites excluding stool and omentum which were not sampled from the benign cohort. These taxa were, however, depleted in the advanced-stage and high-grade OC patients compared to early-stage and low-grade OC patients suggestive of decrease accumulation in advanced disease and could serve as potential indicators for early detection of OC. Similarly, we also observed the accumulation of these mainly pathogenic taxa in OC patients with adverse treatment outcomes compared to those without events and could also serve as potential indicators for predicting patients’ responses to treatment. These findings provide important insights into the potential use of the microbiome as indicators in (1) early detection of and screening for OC and (2) predicting patients’ response to treatment. Given the limited number of patients enrolled in the study, these results would need to be further investigated and confirmed in a larger study.

Abstract Steroid hormones are one of the presumed modulators of Lactobacillus abundance in the vaginal epithelium. We set out to characterize the vaginal microbiome (VMB) and also provide an in-depth understanding of the relative contribution of estradiol (E2) and progesterone (P1) in shaping the vaginal microbiome of Nigerian women (n = 38) who experienced both uncomplicated term delivery and preterm delivery using samples longitudinally collected during pregnancy (17–21, 27–31, 36–41 weeks gestation) and 6 weeks postpartum. Vaginal swabs and blood samples were aseptically collected. Vaginal swabs were used for microbiome assessment using 16S ribosomal RNA (rRNA) gene sequencing. Blood samples were used for hormonal measurement using a competitive-based enzyme-linked immunosorbent assay (ELISA). Across several maternal covariates, maternal age, pregnancy status and delivery mode were not significantly associated with the vaginal microbiota whereas maternal E2 level (pE2 = 0.006, Omnibus), and P1 level (pP1 = 0.001, Omnibus) were significantly associated with the vaginal microbiome. E2 and P1 concentrations increased throughout pregnancy commensurately with increasing proportions of L. crispatus (pE2 = 0.036, pP1 = 0.034, Linear Mixed Model). An increasing trend of α-diversity was also observed as pregnancy progressed (pobserved ASV = 0.006, LMM). A compositional microbiome shift from Lactobacillus profile to non-Lactobacillus profile was observed in most postnatal women (pCST IV

Enterotoxigenic Bacteroides fragilis (ETBF) has received significant attention for a possible association with, or causal role in, colorectal cancer (CRC). The goal of this review was to assess the status of the published evidence supporting (i) the association between ETBF and CRC and (ii) the causal role of ETBF in CRC. PubMed and Scopus searches were performed in August 2021 to identify human, animal, and cell studies pertaining to the role of ETBF in CRC. Inclusion criteria included the use of cell lines, mice, exposure to BFT or ETBF, and detection of bft. Review studies were excluded, and studies were limited to the English language. Quality of study design and risk of bias analysis was performed on the cell, animal, and human studies using ToxRTools, SYRCLE, and NOS, respectively. Ninety-five eligible studies were identified, this included 22 human studies, 24 animal studies, 43 cell studies, and 6 studies that included both cells and mice studies. We found that a large majority of studies supported an association or causal role of ETBF in CRC, as well as high levels of study bias was detected in the in vitro and in vivo studies. The high-level heterogeneity in study design and reporting made it difficult to synthesize these findings into a unified conclusion, suggesting that the need for future studies that include improved mechanistic models, longitudinal in vitro and in vivo evidence, and appropriate control of confounding factors will be required to confirm whether ETBF has a direct role in CRC etiopathogenesis.

Background: Stromal and immune cell composition alterations in benign breast tissue associate with future cancer risk. Pilot data suggest the innate microbiome of normal breast tissue differs between women with and without breast cancer. Microbiome alterations might explain tissue microenvironment variations associated with disease status. Methods: Prospectively-collected sterile normal breast tissues from women with benign (n=16) or malignant (n=17) disease underwent 16SrRNA sequencing with Illumina MiSeq and Hybrid-denovo pipeline processing. Breast tissue was scored for fibrosis and fat percentages and immune cell infiltrates (lobulitis) classified as absent/mild/moderate/severe. Alpha and beta diversity were calculated on rarefied OTU data and associations analyzed with multiple linear regression and PERMANOVA. Results: Breast tissue stromal fat% was lower and fibrosis% higher in benign disease versus cancer (median 30% versus 60%, p=0.01, 70% versus 30%, p=0.002, respectively). The microbiome varied with stromal composition. Alpha diversity (Chao1) correlated with fat% (r=0.38, p=0.02) and fibrosis% (r=-0.32, p=0.05) and associated with different microbial populations as indicated by beta diversity metrics (weighted UniFrac, p=0.08, fat%, p=0.07, fibrosis%). Permutation testing with FDR control revealed taxa differences for fat% in Firmicutes, Bacilli, Bacillales, Staphylococcaceae and genus Staphylococcus, and fibrosis% in Firmicutes, Spirochaetes, Bacilli, Bacillales, Spirochaetales, Proteobacteria RF32, Sphingomonadales, Staphylococcaceae, and genera Clostridium, Staphylococcus, Spirochaetes, Actinobacteria Adlercreutzia. Moderate/severe lobulitis was more common in cancer (73%) than benign disease (13%), p=0.003, but no significant microbial associations were seen. Conclusion: These data suggest a link between breast tissue stromal alterations and its microbiome, further supporting a connection between the breast tissue microenvironment and breast cancer.

A biologically-interpretable and robust metric that provides insight into one’s health status from a gut microbiome sample is an important clinical goal in current human microbiome research. Herein, the authors introduce a species-level index that predicts the likelihood of having a disease.

ABSTRACT Surgical site infections (SSI) are a significant burden to patients and health care systems. We evaluated the use of Nanopore sequencing (NS) to rapidly detect microbial species and antimicrobial resistance (AMR) genes present in intraoperative bile aspirates. Bile aspirates from 42 patients undergoing pancreatic head resection were included. Three methods of DNA extraction using mechanical cell lysis or protease cell lysis were compared to determine the optimum method of DNA extraction. The impact of host DNA depletion, sequence run duration, and use of different AMR gene databases was also assessed. To determine clinical value, NS results were compared to standard culture (SC) results. NS identified microbial species in all culture positive samples. Mechanical lysis improved NS detection of cultured species from 60% to 76%, enabled detection of fungal species, and increased AMR predictions. Host DNA depletion improved detection of streptococcal species and AMR correlation with SC. Selection of AMR database influenced the number of AMR hits and resistance profile of 13 antibiotics. AMR prediction using CARD and ResFinder 4.1 correctly predicted 79% and 81% of the bile antibiogram, respectively. Sequence run duration positively correlated with detection of AMR genes. A minimum of 6 h was required to characterize the biliary microbes, resulting in a turnaround time of 14 h. Rapid identification of microbial species and AMR genes can be achieved by NS. NS results correlated with SC, suggesting that NS may be useful in guiding early antimicrobial therapy postsurgery. IMPORTANCE Surgical site infections (SSI) are a significant burden to patients and health care systems. They increase mortality rates, length of hospital stays, and associated health care costs. To reduce the risk of SSI, surgical patients are administered broad-spectrum antibiotics that are later adapted to target microbial species detected at the site of surgical incision. Use of broad-spectrum antibiotics can be harmful to the patient. We wanted to develop a rapid method of detecting microbial species and their antimicrobial resistance phenotypes. We developed a method of detecting microbial species and predicting resistance phenotypes using Nanopore sequencing. Results generated using Nanopore sequencing were similar to current methods of detection but were obtained in a significantly shorter amount of time. This suggests that Nanopore sequencing could be used to tailor antibiotics in surgical patients and reduce use of broad-spectrum antibiotics.

Abstract Although Streptococcus agalactiae periprosthetic joint infection (PJI) is not as prevalent as staphylococcal PJI, invasive S. agalactiae infection is not uncommon. Here, RNA‐seq was used to perform transcriptomic analysis of S. agalactiae PJI using fluid derived from sonication of explanted arthroplasties of subjects with S. agalactiae PJI, with results compared to those of S. agalactiae strain NEM316 grown in vitro. A total of 227 genes with outlier expression were found (164 upregulated and 63 downregulated) between PJI sonicate fluid and in vitro conditions. Functional enrichment analysis showed genes involved in mobilome and inorganic ion transport and metabolism to be most enriched. Genes involved in nickel, copper, and zinc transport, were upregulated. Among known virulence factors, cyl operon genes, encoding β‐hemolysin/cytolysin, were consistently highly expressed in PJI versus in vitro. The data presented provide insight into S. agalactiae PJI pathogenesis and may be a resource for identification of novel PJI therapeutics or vaccines against invasive S. agalactiae infections.

Recent evidence suggests an association between endometrial cancer and the understudied bacterial species Porphyromonas somerae. This association was demonstrated in previous work that indicated a significantly enriched abundance of P. somerae in the uterine microbiome of endometrial cancer patients. Given the known associations of the Porphyromonas genus and oral cancer, we hypothesized that P. somerae may play a similar pathogenic role in endometrial cancer via intracellular activity. Before testing our hypothesis, we first characterized P. somerae biology, as current background data is limited. These novel characterizations include growth curves in liquid medium and susceptibility tests to antibiotics. We tested our hypothesis by examining growth changes in response to 17β-estradiol, a known risk factor for endometrial cancer, followed by metabolomic profiling in the presence and absence of 17β-estradiol. We found that P. somerae exhibits increased growth in the presence of 17β-estradiol of various concentrations. However, we did not find significant changes in metabolite levels in response to 17β-estradiol. To study direct host-microbe interactions, we used in vitro invasion assays under hypoxic conditions and found evidence for intracellular invasion of P. somerae in endometrial adenocarcinoma cells. We also examined these interactions in the presence of 17β-estradiol but did not observe changes in invasion frequency. Invasion was shown using three lines of evidence including visualization via differential staining and brightfield microscopy, increased frequency of bacterial recovery after co-culturing, and in silico methods to detail relevant genomic and transcriptomic components. These results underscore potential intracellular phenotypes of P. somerae within the uterine microbiome. Furthermore, these results raise new questions pertaining to the role of P. somerae in the progression of endometrial cancer.

Giving birth too soon accounts for half of all newborn deaths worldwide. Clinical symptoms alone are not sufficient to identify women at risk of giving birth too early, as such a pragmatic approach to reducing the incidence of preterm birth entails developing early strategies for intervention before it materializes.

The combination of bulk and single-cell DNA sequencing data of the same tumor enables the inference of high-fidelity phylogenies that form the input to many important downstream analyses in cancer genomics. While many studies simultaneously perform bulk and single-cell sequencing, some studies have analyzed initial bulk data to identify which mutations to target in a follow-up single-cell sequencing experiment, thereby decreasing cost. Bulk data provide an additional untapped source of valuable information, composed of candidate phylogenies and associated clonal prevalence. Here, we introduce PhyDOSE, a method that uses this information to strategically optimize the design of follow-up single cell experiments. Underpinning our method is the observation that only a small number of clones uniquely distinguish one candidate tree from all other trees. We incorporate distinguishing features into a probabilistic model that infers the number of cells to sequence so as to confidently reconstruct the phylogeny of the tumor. We validate PhyDOSE using simulations and a retrospective analysis of a leukemia patient, concluding that PhyDOSE's computed number of cells resolves tree ambiguity even in the presence of typical single-cell sequencing errors. We also conduct a retrospective analysis on an acute myeloid leukemia cohort, demonstrating the potential to achieve similar results with a significant reduction in the number of cells sequenced. In a prospective analysis, we demonstrate the advantage of selecting cells to sequence across multiple biopsies and that only a small number of cells suffice to disambiguate the solution space of trees in a recent lung cancer cohort. In summary, PhyDOSE proposes cost-efficient single-cell sequencing experiments that yield high-fidelity phylogenies, which will improve downstream analyses aimed at deepening our understanding of cancer biology.

Dynamic flux balance analysis uses a quasi-steady state assumption to calculate an organism's metabolic activity at each time-step of a dynamic simulation, using the well-known technique of flux balance analysis. For microbial communities, this calculation is especially costly and involves solving a linear constrained optimization problem for each member of the community at each time step. However, this is unnecessary and inefficient, as prior solutions can be used to inform future time steps. Here, we show that a basis for the space of internal fluxes can be chosen for each microbe in a community and this basis can be used to simulate forward by solving a relatively inexpensive system of linear equations at most time steps. We can use this solution as long as the resulting metabolic activity remains within the optimization problem's constraints (i.e. the solution to the linear system of equations remains a feasible to the linear program). As the solution becomes infeasible, it first becomes a feasible but degenerate solution to the optimization problem, and we can solve a different but related optimization problem to choose an appropriate basis to continue forward simulation. We demonstrate the efficiency and robustness of our method by comparing with currently used methods on a four species community, and show that our method requires at least 91% fewer optimizations to be solved. For reproducibility, we prototyped the method using Python. Source code is available at https://github.com/jdbrunner/surfin_fba.

ABSTRACT The microorganisms of the vaginal tract are critical for vaginal and reproductive health. However, the regulation of these microorganisms is not well understood. Therefore, we investigated whether different factors regulate the vaginal microbiota of healthy college-aged women (n = 26) with high temporal resolution by collecting daily self-administered vaginal swabs and using 16S rRNA sequencing for bacterial identification. As expected, vaginal microbiota clustered into five predefined community state types. Vaginal microbial diversity, stability, and Lactobacillus abundances were associated with the menstrual cycle and hormonal contraceptive use. Vaginal microbial diversity, as measured using the Shannon index, increased during menses (P < 0.001), while Lactobacillus abundances decreased (P = 0.01). The covariance of these microbial measures with previously established estradiol levels suggests that estrogens can regulate vaginal microbiota. Moreover, the use of hormonal contraceptives may alter the temporal dynamics of the vaginal microbiota and decrease Lactobacillus abundances, depending on hormonal content and release method. Interestingly, intrasample diversity was greater in participants on a vegetarian diet (P = 0.004) and among participants who exercised more (P = 0.04). These findings indicate that ovarian hormones, diet, and exercise can regulate vaginal microbial composition and stability and may impact vaginal and reproductive health. IMPORTANCE The vaginal microbiome is a critical component of women’s sexual and reproductive health, with variations in microbial composition, particularly the loss of Lactobacillus species, being implicated in gynecologic and obstetric diseases. Given that the vaginal microbiome is so crucial, why do vaginal microbial profiles vary strikingly from person to person and even change over time within the same person? In the present study, which tracked the daily vaginal microbiomes of young healthy women through different lifestyles, we found that use of a locally released progestin contraceptive, a vegetarian diet, and intense exercise appear to lead to vaginal microbiome alterations and loss of Lactobacillus species. The impact of these vaginal microbiome changes on immediate and long-term health remain to be investigated.

The interpretation of sampling data plays a crucial role in policy response to the spread of a disease during an epidemic, such as the COVID-19 epidemic of 2020. However, this is a non-trivial endeavor due to the complexity of real world conditions and limits to the availability of diagnostic tests, which necessitate a bias in testing favoring symptomatic individuals. A thorough understanding of sampling confidence and bias is necessary in order make accurate conclusions. In this manuscript, we provide a stochastic model of sampling for assessing confidence in disease metrics such as trend detection, peak detection and disease spread estimation. Our model simulates testing for a disease in an epidemic with known dynamics, allowing us to use Monte-Carlo sampling to assess metric confidence. This model can provide realistic simulated data which can be used in the design and calibration of data analysis and prediction methods. As an example, we use this method to show that trends in the disease may be identified using under 10,000 biased samples each day, and an estimate of disease spread can be made with additional 1,000–2,000 unbiased samples each day. We also demonstrate that the model can be used to assess more advanced metrics by finding the precision and recall of a strategy for finding peaks in the dynamics.

ABSTRACT Few previous studies have assessed stability and “gold-standard” concordance of fecal sample collection methods for whole-genome shotgun metagenomic sequencing (WGSS), an increasingly popular method for studying the gut microbiome. We used WGSS data to investigate ambient temperature stability and putative gold-standard concordance of microbial profiles in fecal samples collected and stored using fecal occult blood test (FOBT) cards, fecal immunochemical test (FIT) tubes, 95% ethanol, or RNAlater. Among 15 Mayo Clinic employees, for each collection method, we calculated intraclass correlation coefficients (ICCs) to estimate stability of fecal microbial profiles after storage for 4 days at ambient temperature and concordance with immediately frozen, no-solution samples (i.e., the putative gold standard). ICCs were estimated for multiple metrics, including relative abundances of select phyla, species, KEGG k-genes (representing any coding sequence that had >70% identity and >70% query coverage with respect to a known KEGG ortholog), KEGG modules, and KEGG pathways; species and k-gene alpha diversity; and Bray-Curtis and Jaccard species beta diversity. ICCs for microbial profile stability were excellent (≥90%) for fecal samples collected via most of the collection methods, except those preserved in 95% ethanol. Concordance with the immediately frozen, no-solution samples varied for all collection methods, but the number of observed species and the beta diversity metrics tended to have higher concordance than other metrics. Our findings, taken together with previous studies and feasibility considerations, indicated that FOBT cards, FIT tubes, and RNAlater are acceptable choices for fecal sample collection methods in future WGSS studies. IMPORTANCE A major direction for future microbiome research is implementation of fecal sample collections in large-scale, prospective epidemiologic studies. Studying microbiome-disease associations likely requires microbial data to be pooled from multiple studies. Our findings suggest collection methods that are most optimal to be used standardly across future WGSS microbiome studies.

ABSTRACT Colorectal adenomas are precancerous lesions of colorectal cancer (CRC) that offer a means of viewing the events key to early CRC development. A number of studies have investigated the changes and roles of gut microbiota in adenoma and carcinoma development, highlighting its impact on carcinogenesis. However, there has been less of a focus on the gut metabolome, which mediates interactions between the host and gut microbes. Here, we investigated metabolomic profiles of stool samples from patients with advanced adenoma (n = 102), matched controls (n = 102), and patients with CRC (n = 36). We found that several classes of bioactive lipids, including polyunsaturated fatty acids, secondary bile acids, and sphingolipids, were elevated in the adenoma patients compared to the controls. Most such metabolites showed directionally consistent changes in the CRC patients, suggesting that those changes may represent early events of carcinogenesis. We also examined gut microbiome-metabolome associations using gut microbiota profiles in these patients. We found remarkably strong overall associations between the microbiome and metabolome data and catalogued a list of robustly correlated pairs of bacterial taxa and metabolomic features which included signatures of adenoma. Our findings highlight the importance of gut metabolites, and potentially their interplay with gut microbes, in the early events of CRC pathogenesis. IMPORTANCE Colorectal adenomas are precursors of CRC. Recently, the gut microbiota, i.e., the collection of microbes residing in our gut, has been recognized as a key player in CRC development. There have been a number of gut microbiota profiling studies for colorectal adenoma and CRC; however, fewer studies have considered the gut metabolome, which serves as the chemical interface between the host and gut microbiota. Here, we conducted a gut metabolome profiling study of colorectal adenoma and CRC and analyzed the metabolomic profiles together with paired microbiota composition profiles. We found several chemical signatures of colorectal adenoma that were associated with some gut microbes and potentially indicative of future CRC. This study highlights potential early-driver metabolites in CRC pathogenesis and guides further targeted experiments and thus provides an important stepping stone toward developing better CRC prevention strategies.

Abstract Background Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. Methods We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks. Results We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC. Conclusions Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers.

Abstract Background Transfer of genetic material from microbes or viruses into the host genome is known as horizontal gene transfer (HGT). The integration of viruses into the human genome is associated with multiple cancers, and these can now be detected using next-generation sequencing methods such as whole genome sequencing and RNA-sequencing. Results We designed a novel computational workflow, HGT-ID, to identify the integration of viruses into the human genome using the sequencing data. The HGT-ID workflow primarily follows a four-step procedure: i) pre-processing of unaligned reads, ii) virus detection using subtraction approach, iii) identification of virus integration site using discordant and soft-clipped reads and iv) HGT candidates prioritization through a scoring function. Annotation and visualization of the events, as well as primer design for experimental validation, are also provided in the final report. We evaluated the tool performance with the well-understood cervical cancer samples. The HGT-ID workflow accurately detected known human papillomavirus (HPV) integration sites with high sensitivity and specificity compared to previous HGT methods. We applied HGT-ID to The Cancer Genome Atlas (TCGA) whole-genome sequencing data (WGS) from liver tumor-normal pairs. Multiple hepatitis B virus (HBV) integration sites were identified in TCGA liver samples and confirmed by HGT-ID using the RNA-Seq data from the matched liver pairs. This shows the applicability of the method in both the data types and cross-validation of the HGT events in liver samples. We also processed 220 breast tumor WGS data through the workflow; however, there were no HGT events detected in those samples. Conclusions HGT-ID is a novel computational workflow to detect the integration of viruses in the human genome using the sequencing data. It is fast and accurate with functions such as prioritization, annotation, visualization and primer design for future validation of HGTs. The HGT-ID workflow is released under the MIT License and available at http://kalarikrlab.org/Software/HGT-ID.html.

The metabolic interactions between gut microbes and host cells play roles in human health. Here, Sunget al. present a literature-curated metabolic network of the human gut microbiota and three human cell types, together with a mathematical approach to identify distinct microbial and metabolic features in gut microbiomes.

ObjectiveObesity is a risk factor for colorectal cancer (CRC), accounting for more than 14% of CRC incidence. Microbial dysbiosis and chronic inflammation are common characteristics in both obesity and CRC. Human and murine studies, together, demonstrate the significant impact of the microbiome in governing energy metabolism and CRC development; yet, little is understood about the contribution of the microbiome to development of obesity-associated CRC as compared to individuals who are not obese.DesignIn this study, we conducted a meta-analysis using five publicly available stool and tissue-based 16S rRNA and whole genome sequencing (WGS) data sets of CRC microbiome studies. High-resolution analysis was employed for 16S rRNA data, which allowed us to achieve species-level information to compare with WGS. ResultsCharacterisation of the confounders between studies, 16S rRNA variable region and sequencing method did not reveal any significant effect on alpha diversity in CRC prediction. Both 16S rRNA and WGS were equally variable in their ability to predict CRC. Results from diversity analysis confirmed lower diversity in obese individuals without CRC; however, no universal differences were found in diversity between obese and non-obese individuals with CRC. When examining taxonomic differences, the probability of being classified as CRC did not change significantly in obese individuals for all taxa tested. However, random forest classification was able to distinguish CRC and non-CRC stool when body mass index was added to the model.ConclusionOverall, microbial dysbiosis was not a significant factor in explaining the higher risk of colon cancer among individuals with obesity.

BACKGROUND:Vector-borne pathogens are a significant public health concern worldwide. Infections with these pathogens, some of which are emerging, are likely under-recognized due to the lack of widely-available laboratory tests. There is an urgent need for further advancement in diagnostic modalities to detect new and known vector-borne pathogens. We evaluated the utility of metagenomic shotgun sequencing (MGS) as a pathogen agnostic approach for detecting vector-borne pathogens from human blood samples. METHODS:Residual whole blood samples from patients with known infection with Babesia microti, Borrelia hermsii, Plasmodium falciparum, Mansonella perstans, Anaplasma phagocytophilum or Ehrlichia chaffeensis were studied. Samples underwent DNA extraction, removal of human DNA, whole genome amplification, and paired-end library preparation, followed by sequencing on Illumina HiSeq 2500. Bioinformatic analysis was performed using the Livermore Metagenomics Analysis Toolkit (LMAT), Metagenomic Phylogenetic Analysis (MetaPhlAn2), Genomic Origin Through Taxonomic CHAllenge (GOTTCHA) and Kraken 2. RESULTS:Eight samples were included in the study (2 samples each for P. falciparum and A. phagocytophilum). An average of 27.5 million read pairs was generated per sample (range, 18.3-38.8 million) prior to removal of human reads. At least one of the analytic tools was able to detect four of six organisms at the genus level, and the organism present in five of eight specimens at the species level. Mansonella and Ehrlichia species were not detected by any of the tools; however, mitochondrial cytochrome c oxidase subunit I amino acid sequence analysis suggested the presence of M. perstans genetic material. CONCLUSIONS:MGS is a promising tool with the potential to evolve as a non-hypothesis driven diagnostic test to detect vector-borne pathogens, including protozoa and helminths.

The gut microbiome likely plays a role in the etiology of multiple health conditions, especially those affecting the gastrointestinal tract. Little consensus exists as to the best, standard methods to collect fecal samples for future microbiome analysis. We evaluated three distinct populations (N = 132 participants) using 16S rRNA gene amplicon sequencing data to investigate the reproducibility, stability, and accuracy of microbial profiles in fecal samples collected and stored via fecal occult blood test (FOBT) or Flinders Technology Associates (FTA) cards, fecal immunochemical tests (FIT) tubes, 70% and 95% ethanol, RNAlater, or with no solution. For each collection method, based on relative abundance of select phyla and genera, two alpha diversity metrics, and four beta diversity metrics, we calculated intraclass correlation coefficients (ICCs) to estimate reproducibility and stability, and Spearman correlation coefficients (SCCs) to estimate accuracy of the fecal microbial profile. Comparing duplicate samples, reproducibility ICCs for all collection methods were excellent (ICCs ≥75%). After 4-7 days at ambient temperature, ICCs for microbial profile stability were excellent (≥75%) for most collection methods, except those collected via no-solution and 70% ethanol. SCCs comparing each collection method to immediately-frozen no-solution samples ranged from fair to excellent for most methods; however, accuracy of genus-level relative abundances differed by collection method. Our findings, taken together with previous studies and feasibility considerations, indicated that FOBT/FTA cards, FIT tubes, 95% ethanol, and RNAlater are excellent choices for fecal sample collection methods in future microbiome studies. Furthermore, establishing standard collection methods across studies is highly desirable.

Abstract Background Endometrial cancer studies have led to a number of well-defined but mechanistically unconnected genetic and environmental risk factors. One of the emerging modulators between environmental triggers and genetic expression is the microbiome. We set out to inquire about the composition of the uterine microbiome and its putative role in endometrial cancer. Methods We undertook a study of the microbiome in samples taken from different locations along the female reproductive tract in patients with endometrial cancer (n = 17), patients with endometrial hyperplasia (endometrial cancer precursor, n = 4), and patients afflicted with benign uterine conditions (n = 10). Vaginal, cervical, Fallopian, ovarian, peritoneal, and urine samples were collected aseptically both in the operating room and the pathology laboratory. DNA extraction was followed by amplification and high-throughput next generation sequencing (MiSeq) of the 16S rDNA V3-V5 region to identify the microbiota present. Microbiota data were summarized using both α-diversity to reflect species richness and evenness within bacterial populations and β-diversity to reflect the shared diversity between bacterial populations. Statistical significance was determined through the use of multiple testing, including the generalized mixed-effects model. Results The microbiome sequencing (16S rDNA V3-V5 region) revealed that the microbiomes of all organs (vagina, cervix, Fallopian tubes, and ovaries) are significantly correlated (p 4.5). Conclusions Our results suggest that the detection of A. vaginae and the identified Porphyromonas sp. in the gynecologic tract combined with a high vaginal pH is statistically associated with the presence of endometrial cancer. Given the documented association of the identified microorganisms with other pathologies, these findings raise the possibility of a microbiome role in the manifestation, etiology, or progression of endometrial cancer that should be further investigated.

ABSTRACT Variability in representation of microbial communities can be caused by differences in microbial composition or artifacts introduced at sample collection or processing. Alterations in community representation introduced by variations in starting DNA concentrations have not been systematically investigated in stool samples. The goal of this study was to evaluate the effect of the genomic DNA (gDNA) concentration in the resulting 16S rRNA gene library composition and compare its effect to other sample processing variables in homogenized human fecal material. Compared to a gDNA input of 1 ng/μl, inputs of ≤1.6 × 10−3 ng/μl resulted in a marked decrease in the concentration of the 16S rRNA gene amplicon (P < 0.001). Low gDNA concentrations (≤1.6 × 10−3 ng/μl) were also associated with a decrease (P < 0.001) in the number of operational taxonomic units and significant divergence in β-diversity profiles (unweighted UniFrac distance, P < 0.001), as characterized by an overestimation of Proteobacteria and underestimation of Firmicutes. Even a gDNA concentration of 4 × 10−2 ng/μl showed a significant impact on the β-diversity profile (unweighted UniFrac distance, P = 0.03). Overall, the gDNA concentration explained 22.4% to 38.1% of the microbiota variation based on various β-diversity measures (P < 0.001). By comparison, the DNA extraction methods and PCR volumes tested did not significantly affect the microbial composition profile, and the PCR cycling method explained less than 3.7% of the microbiota variation (weighted UniFrac distance, P = 0.03). The 16S rRNA gene yield and the microbial community representation of human homogenized stool samples are significantly altered by gDNA template concentrations of ≤1.6 × 10−3 ng/μl. In addition, data from studies with a gDNA input of ≤4 × 10−2 ng/μl should be interpreted with caution. IMPORTANCE The genomic DNA input for stool samples utilized for microbiome composition has not been determined. In this study, we determined the reliable threshold level under which conclusions drawn from the data may be compromised. We also determined the type of microbial bias introduced by less-than-ideal genomic input.

Staphylococcus aureus is a leading cause of bacteremia in hospitalized patients. Whether or not S. aureus bacteremia (SAB) is associated with clonality, implicating potential nosocomial transmission, has not, however, been investigated. Herein, we examined the epidemiology of SAB using whole genome sequencing (WGS). 152 SAB isolates collected over the course of 2015 at a single large Minnesota medical center were studied. Staphylococcus protein A (spa) typing was performed by PCR/Sanger sequencing; multilocus sequence typing (MLST) and core genome MLST (cgMLST) were determined by WGS. Forty-eight isolates (32%) were methicillin-resistant S. aureus (MRSA). The isolates encompassed 66 spa types, clustered into 11 spa clonal complexes (CCs) and 10 singleton types. 88% of 48 MRSA isolates belonged to spa CC-002 or -008. Methicillin-susceptible S. aureus (MSSA) isolates were more genotypically diverse, with 61% distributed across four spa CCs (CC-002, CC-012, CC-008 and CC-084). By MLST, there was 31 sequence types (STs), including 18 divided into 6 CCs and 13 singleton STs. Amongst MSSA isolates, the common MLST clones were CC5 (23%), CC30 (19%), CC8 (15%) and CC15 (11%). Common MRSA clones were CC5 (67%) and CC8 (25%); there were no MRSA isolates in CC45 or CC30. By cgMLST analysis, there were 9 allelic differences between two isolates, with the remaining 150 isolates differing from each other by over 40 alleles. The two isolates were retroactively epidemiologically linked by medical record review. Overall, cgMLST analysis resulted in higher resolution epidemiological typing than did multilocus sequence or spa typing.

The inflammatory tumoral-immune response alters the physiology of the tumor microenvironment, which may attenuate genomic instability. In addition to inducing inflammatory immune responses, several pathogenic bacteria produce genotoxins. However the extent of microbial contribution to the tumor microenvironment biology remains unknown. We utilized The Cancer Genome Atlas, (TCGA) breast cancer data to perform a novel experiment utilizing unmapped and mapped RNA sequencing read evidence to minimize laboratory costs and effort. Our objective was to characterize the microbiota and associate the microbiota with the tumor expression profiles, for 668 breast tumor tissues and 72 non-cancerous adjacent tissues. The prominent presence of Proteobacteria was increased in the tumor tissues and conversely Actinobacteria abundance increase in non-cancerous adjacent tissues. Further, geneset enrichment suggests Listeria spp to be associated with the expression profiles of genes involved with epithelial to mesenchymal transitions. Moreover, evidence suggests H. influenza may reside in the surrounding stromal material and was significantly associated with the proliferative pathways: G2M checkpoint, E2F transcription factors, and mitotic spindle assembly. In summary, further unraveling this complicated interplay should enable us to better diagnose and treat breast cancer patients.

Planctomycetes are distinguished from other Bacteria by compartmentalization of cells via internal membranes, interpretation of which has been subject to recent debate regarding potential relations to Gram-negative cell structure. In our interpretation of the available data, the planctomycete Gemmata obscuriglobus contains a nuclear body compartment, and thus possesses a type of cell organization with parallels to the eukaryote nucleus. Here we show that pore-like structures occur in internal membranes of G.obscuriglobus and that they have elements structurally similar to eukaryote nuclear pores, including a basket, ring-spoke structure, and eight-fold rotational symmetry. Bioinformatic analysis of proteomic data reveals that some of the G. obscuriglobus proteins associated with pore-containing membranes possess structural domains found in eukaryote nuclear pore complexes. Moreover, immunogold labelling demonstrates localization of one such protein, containing a β-propeller domain, specifically to the G. obscuriglobus pore-like structures. Finding bacterial pores within internal cell membranes and with structural similarities to eukaryote nuclear pore complexes raises the dual possibilities of either hitherto undetected homology or stunning evolutionary convergence.

OBJECTIVES/SPECIFIC AIMS: Our primary objective is to determine whether the bacteria exerts its effect intra- or extra-cellularly. We have genomic and microscopy preliminary evidence indicating that the bacteria is capable of invading endometrial cells. Our secondary objective is to identify what type of impact the bacteria have on the host cells and whether they are capable of transforming the host cells from a benign into a malignant phenotype. We are currently testing a putative mechanism by which the bacteria may cause the overexpression of the hypoxia inducible factor (HIF), a hallmark of endometrial cancer. METHODS/STUDY POPULATION: We are utilizing our custom built optofluidics platform (microfluidics platform incorporated into an advanced microscope with optical laser tweezers) to isolate single cells from the endometrial tissues of 150 patients with and without endometrial cancer. We are utilizing single cell whole genome amplification followed by qPCR to identify if the bacteria is present intracellularly. We are coupling this procedure with standard microbiological invasion assays with endometrial cell line cultures and P.somerae. We are also utilizing our optofluidics platform to perform single cell transcriptomic amplification, followed by sequencing of cells invaded or in the presence of the bacteria to determine the impact in the transcriptome of the host cell. We are coupling this with western blots of factors hypothesized to be impacted by the bacteria in the overexpression of HIF. RESULTS/ANTICIPATED RESULTS: Based on our preliminary data we anticipate to find evidence that P.somerae is invading the host cells, in particular the cells in tumor tissues. We also expect to find that the intracellular presence of the bacteria is causing the overexpression of the HIF pathway, hence resulting in a cancerous phenotype. DISCUSSION/SIGNIFICANCE OF IMPACT: Our long-term goal is to develop primary prevention strategies that will reduce endometrial cancer incidence rates. A confirmation of our hypothesis could suggest that it is sufficient for endometrial cancer prevention efforts to eliminate P.somerae, in line with gastric and cervical cancer efforts. It could also mean that targeting P.somerae in cancer treatment is necessary to contain the disease and prevent recurrence.

The clinical utility of microbiome biomarkers depends on the reliable and reproducible nature of comparative results. Underappreciation of the variation associated with common demographic, health, and behavioral factors may confound associations of interest and generate false positives. Here, we present the Midwestern Reference Panel (MWRP), a resource for comparative gut microbiome studies conducted in the Midwestern United States. We analyzed the relationships between demographic and health behavior-related factors and the microbiota in this cohort, and estimated their effect sizes. Most variables investigated were associated with the gut microbiota. Specifically, body mass index (BMI), race, sex, and alcohol use were significantly associated with microbial β-diversity (P < 0.05, unweighted UniFrac). BMI, race and alcohol use were also significantly associated with microbial α-diversity (P < 0.05, species richness). Tobacco use showed a trend toward association with the microbiota (P < 0.1, unweighted UniFrac). The effect sizes of the associations, as quantified by adjusted R2 values based on unweighted UniFrac distances, were small (< 1% for all variables), indicating that these factors explain only a small percentage of overall microbiota variability. Nevertheless, the significant associations between these variables and the gut microbiota suggest that they could still be potential confounders in comparative studies and that controlling for these variables in study design, which is the main objective of the MWRP, is important for increasing reproducibility in comparative microbiome studies.

Second-generation sequencing technologies have granted us greater access to the diversity and genetics of microbial communities that naturally reside endo- and ecto-symbiotically with animal hosts. Substantial research has emerged describing the diversity and broader trends that exist within and between host species and their associated microbial ecosystems, yet the application of these data to our evolutionary understanding of microbiomes appears fragmented. For the most part biological perspectives are based on limited observations of oversimplified communities, while mathematical and/or computational modeling of these concepts often lack biological precedence. In recognition of this disconnect, both fields have attempted to incorporate ecological theories, although their applicability is currently a subject of debate because most ecological theories were developed based on observations of macro-organisms and their ecosystems. For the purposes of this review, we attempt to transcend the biological, ecological and computational realms, drawing on extensive literature, to forge a useful framework that can, at a minimum be built upon, but ideally will shape the hypotheses of each field as they move forward. In evaluating the top-down selection pressures that are exerted on a microbiome we find cause to warrant reconsideration of the much-maligned theory of multi-level selection and reason that complexity must be underscored by modularity.

Background. Preterm Premature Rupture of Membranes (PPROM) is a major leading cause of preterm births. While the cause for PPROM remains unidentified, it is anticipated to be due to subclinical infection, since a large proportion of PPROM patients display signs of chorioamnionitis. Since subclinical infections can be facilitated by dysbiosis, our goal was to characterize the vaginal microbiome and amniotic fluid discharge upon PPROM, through latency antibiotic treatment, and until delivery, to detect the presence of pathogens, microbiota alteration, and microbial response to treatment.Methods. Enrolled subjects (15) underwent routine institutional antenatal care for PPROM, including the administration of latency antibiotics. Serial vaginal swabs were obtained from diagnosis of PPROM through delivery and the sequencing of the V3–V5 region of the 16S rRNA gene was performed for all collected samples.Results. The results show that Lactobacilli species were markedly decreased when compared to vaginal swabs collected from uncomplicated pregnancy subjects with a matched gestational time. Prevotella and Peptoniphilus were the most prevalent taxa in PPROM subjects at presentation. The vaginal microbiome of the PPROM subjects varied substantially intra- and inter-subjects. Several taxa were found to be significantly reduced during and after the antibiotic treatment: Weeksella, Lachnospira, Achromobacter, and Pediococcus. In contrast, Peptostreptococcus and Tissierellaceae ph2 displayed a significant increase after the antibiotic treatment. However, the relative abundance of Lactobacillus, Prevotella, and Peptoniphilus was not substantially impacted during the hospitalization of the PPROM subjects. The deficiency of Lactobacillus, and constancy of known pathogenic species, such as Prevotella and Peptoniphilus during and after antibiotics, highlights the persistent dysbiosis and warrants further investigation into mitigating approaches.Discussion. PPROM is responsible for one third of all preterm births. It is thought that subclinical infection is a crucial factor in the pathophysiology of PPROM because 25–40% of patients present signs of chorioamnionitis on amniocentesis. Here we sought to directly assess the bacterial content of the vagina and leaking amniotic fluid of subjects at presentation, throughout treatment and up until delivery, in order to search for common pathogens, microbiota changes, and microbial response to latency antibiotic treatment. We have found that the vaginal microbiome of PPROM subjects is highly variable and displays significant changes to treatment. However, the unchanging deficiency of Lactobacillus, and persistence of known pathogenic species, such as Prevotella and Peptoniphilus from presentation, through antibiotic treatment and up until delivery, highlights the persistent dysbiosis and warrants further investigation into mitigating approaches.

Genome-scale metabolic models provide a powerful means to harness information from genomes to deepen biological insights. With exponentially increasing sequencing capacity, there is an enormous need for automated reconstruction techniques that can provide more accurate models in a short time frame. Current methods for automated metabolic network reconstruction rely on gene and reaction annotations to build draft metabolic networks and algorithms to fill gaps in these networks. However, automated reconstruction is hampered by database inconsistencies, incorrect annotations, and gap filling largely without considering genomic information. Here we develop an approach for applying genomic information to predict alternative functions for genes and estimate their likelihoods from sequence homology. We show that computed likelihood values were significantly higher for annotations found in manually curated metabolic networks than those that were not. We then apply these alternative functional predictions to estimate reaction likelihoods, which are used in a new gap filling approach called likelihood-based gap filling to predict more genomically consistent solutions. To validate the likelihood-based gap filling approach, we applied it to models where essential pathways were removed, finding that likelihood-based gap filling identified more biologically relevant solutions than parsimony-based gap filling approaches. We also demonstrate that models gap filled using likelihood-based gap filling provide greater coverage and genomic consistency with metabolic gene functions compared to parsimony-based approaches. Interestingly, despite these findings, we found that likelihoods did not significantly affect consistency of gap filled models with Biolog and knockout lethality data. This indicates that the phenotype data alone cannot necessarily be used to discriminate between alternative solutions for gap filling and therefore, that the use of other information is necessary to obtain a more accurate network. All described workflows are implemented as part of the DOE Systems Biology Knowledgebase (KBase) and are publicly available via API or command-line web interface.

To assess the vaginal microbiome throughout full-term uncomplicated pregnancy.Vaginal swabs were obtained from twelve pregnant women at 8-week intervals throughout their uncomplicated pregnancies. Patients with symptoms of vaginal infection or with recent antibiotic use were excluded. Swabs were obtained from the posterior fornix and cervix at 8-12, 17-21, 27-31, and 36-38 weeks of gestation. The microbial community was profiled using hypervariable tag sequencing of the V3-V5 region of the 16S rRNA gene, producing approximately 8 million reads on the Illumina MiSeq.Samples were dominated by a single genus, Lactobacillus, and exhibited low species diversity. For a majority of the patients (n = 8), the vaginal microbiome was dominated by Lactobacillus crispatus throughout pregnancy. Two patients showed Lactobacillus iners dominance during the course of pregnancy, and two showed a shift between the first and second trimester from L. crispatus to L. iners dominance. In all of the samples only these two species were identified, and were found at an abundance of higher than 1% in this study. Comparative analyses also showed that the vaginal microbiome during pregnancy is characterized by a marked dominance of Lactobacillus species in both Caucasian and African-American subjects. In addition, our Caucasian subject population clustered by trimester and progressed towards a common attractor while African-American women clustered by subject instead and did not progress towards a common attractor.Our analyses indicate normal pregnancy is characterized by a microbiome that has low diversity and high stability. While Lactobacillus species strongly dominate the vaginal environment during pregnancy across the two studied ethnicities, observed differences between the longitudinal dynamics of the analyzed populations may contribute to divergent risk for pregnancy complications. This helps establish a baseline for investigating the role of the microbiome in complications of pregnancy such as preterm labor and preterm delivery.

16S rDNA hypervariable tag sequencing has become the de facto method for accessing microbial diversity. Illumina paired-end sequencing, which produces two separate reads for each DNA fragment, has become the platform of choice for this application. However, when the two reads do not overlap, existing computational pipelines analyze data from read separately and underutilize the information contained in the paired-end reads.We created a workflow known as Illinois Mayo Taxon Organization from RNA Dataset Operations (IM-TORNADO) for processing non-overlapping reads while retaining maximal information content. Using synthetic mock datasets, we show that the use of both reads produced answers with greater correlation to those from full length 16S rDNA when looking at taxonomy, phylogeny, and beta-diversity.IM-TORNADO is freely available at http://sourceforge.net/projects/imtornado and produces BIOM format output for cross compatibility with other pipelines such as QIIME, mothur, and phyloseq.

BACKGROUND: The replication of DNA in Archaea and eukaryotes requires several ancillary complexes, including proliferating cell nuclear antigen (PCNA), replication factor C (RFC), and the minichromosome maintenance (MCM) complex. Bacterial DNA replication utilizes comparable proteins, but these are distantly related phylogenetically to their archaeal and eukaryotic counterparts at best. METHODOLOGY/PRINCIPAL FINDINGS: While the structures of each of the complexes do not differ significantly between the archaeal and eukaryotic versions thereof, the evolutionary dynamic in the two cases does. The number of subunits in each complex is constant across all taxa. However, they vary subtly with regard to composition. In some taxa the subunits are all identical in sequence, while in others some are homologous rather than identical. In the case of eukaryotes, there is no phylogenetic variation in the makeup of each complex-all appear to derive from a common eukaryotic ancestor. This is not the case in Archaea, where the relationship between the subunits within each complex varies taxon-to-taxon. We have performed a detailed phylogenetic analysis of these relationships in order to better understand the gene duplications and divergences that gave rise to the homologous subunits in Archaea. CONCLUSION/SIGNIFICANCE: This domain level difference in evolution suggests that different forces have driven the evolution of DNA replication proteins in each of these two domains. In addition, the phylogenies of all three gene families support the distinctiveness of the proposed archaeal phylum Thaumarchaeota.

Next-generation DNA sequencing is increasingly being utilized to probe microbial communities, such as gastrointestinal microbiomes, where it is important to be able to quantify measures of abundance and diversity. The fragmented nature of the 16S rRNA datasets obtained, coupled with their unprecedented size, has led to the recognition that the results of such analyses are potentially contaminated by a variety of artifacts, both experimental and computational. Here we quantify how multiple alignment and clustering errors contribute to overestimates of abundance and diversity, reflected by incorrect OTU assignment, corrupted phylogenies, inaccurate species diversity estimators, and rank abundance distribution functions. We show that straightforward procedural optimizations, combining preexisting tools, are effective in handling large (10(5)-10(6)) 16S rRNA datasets, and we describe metrics to measure the effectiveness and quality of the estimators obtained. We introduce two metrics to ascertain the quality of clustering of pyrosequenced rRNA data, and show that complete linkage clustering greatly outperforms other widely used methods.