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Identification of clinically relevant strains of bacteria increasingly relies on whole genome sequencing. The downstream bioinformatics steps necessary for calling variants from short read sequences are well-established but seldom validated against haploid genomes. We devised anin silicoworkflow to introduce single nucleotide polymorphisms (SNP) and indels into bacterial reference genomes, and computationally generate sequencing reads based on the mutated genomes. We then applied the method toMycobacterium tuberculosisH37Rv and used the synthetic reads as truth sets for evaluating several popular variant callers. Insertions proved especially challenging for most variant callers to correctly identify, relative to deletions and single nucleotide polymorphisms. With adequate read depth, however, variant callers that use high quality soft-clipped reads and base mismatches to perform local realignment consistently had the highest precision and recall in identifying medium-length insertions and deletions.

Microbiome science is difficult to translate back to patients due to an inability to harmonize 16S rRNA gene-based microbiome data, as differences in the technique will result in different amplicon sequence variants (ASV) from the same microbe. Here we demonstrate that placement of ASV onto a common phylogenetic tree of full-length 16S rRNA alleles can harmonize microbiome studies. Using in silico data approximating 100 healthy human stool microbiomes we demonstrated that phylogenetic placement of ASV can recapitulate the true relationships between communities as compared closed-OTU based approaches (Spearman R 0.8 vs 0.2). Using real data from thousands of human gut and vaginal microbiota, we demonstrate phylogenetic placement, but not closed OTUs, were able to group communities by origin (stool vs vaginal) without being confounded by technique and integrate new data into existing ordination/clustering models for precision medicine. This enables meta-analysis of microbiome studies and the microbiome as a biomarker.

The Nugent score is the reference standard for bacterial vaginosis (BV) diagnosis but has not been validated in postmenopausal women. We compared relative abundances from 16S ribosomal RNA gene sequencing of vaginal microbiota with Nugent score in cohorts of premenopausal (n = 220) and postmenopausal (n = 144) women. In premenopausal women, 33 taxa were significantly correlated with Nugent score, including the classic BV-associated taxa Gardnerella, Atopobium, Sneathia, Megasphaera, and Prevotella. In postmenopausal women, 11 taxa were significantly associated with Nugent score, including Prevotella but no other BV-associated genera. High Nugent scores should not be used to infer BV in postmenopausal women.

BACKGROUND Hormonal changes influence the composition of vaginal flora, which is directly related to the health of an individual. Transgender men prescribed testosterone experience a vaginal hormone composition that differs from cisgender women. To the author's knowledge, there are no clinical studies evaluating the influence that testosterone administration has on the vaginal microbiome. METHODS Vaginal swabs were self-collected by a cohort of self-identified healthy transgender men prescribed testosterone for at least 1 year (n = 28) and from cisgender women who were used as the comparator (n = 8). Participants completed a questionnaire to indicate the mode and dose of testosterone administration, sexual history, and vaginal health. Serum was collected for hormone analysis. Bacterial community profiles were assessed with broad-range PCR primers targeting the V3–V4 hypervariable region of the 16S bacterial rRNA, next-generation sequencing, and analysis by phylogenetic placement. RESULTS Compared to cisgender women, the vaginal floras of transgender men were less likely to have Lactobacillus as their primary genus. Intravaginal estrogen administration was positively associated with the presence of Lactobacillus in transgender men (P = 0.045). Transgender men had a significantly increased relative abundance of >30 species and a significantly higher α diversity (P = 0.0003). The presence of Lactobacillus was significantly associated with a lower α diversity index (P = 0.017). CONCLUSIONS The vaginal microbiome of transgender men who were assigned a female sex at birth and use testosterone may differ from that of cisgender women. Intravaginal estrogen administration may reduce these differences by promoting colonization with Lactobacillus species and decreasing α diversity.

The broad-range detection and identification of bacterial DNA from clinical specimens are a foundational approach in the practice of molecular microbiology. However, there are circumstances under which conventional testing may yield false-negative or otherwise uninterpretable results, including the presence of multiple bacterial templates or degraded nucleic acids. Here, we describe an alternative, next-generation sequencing approach for the broad range detection of bacterial DNA using broad-range 16S rRNA gene hybrid capture (“16S Capture”). The method is able to deconvolute multiple bacterial species present in a specimen, is compatible with highly fragmented templates, and can be readily implemented when the overwhelming majority of nucleic acids in a specimen derive from the human host. We find that this approach is sensitive to detecting as few as 17 Staphylococcus aureus genomes from a background of 100 ng of human DNA, providing 19- to 189-fold greater sensitivity for identifying bacterial sequences than standard shotgun metagenomic sequencing, and is able to successfully recover organisms from across the eubacterial tree of life. Application of 16S Capture to a proof-of-principle case series demonstrated its ability to identify bacterial species that were consistent with histological evidence of infection, even when diagnosis could not be established using conventional broad range bacterial detection assays. 16S Capture provides a novel means for the efficient and sensitive detection of bacteria embedded in human tissues and for specimens containing highly fragmented template DNA.

Background Healthcare workers (HCWs) who serve on the front lines of the coronavirus disease 2019 (COVID-19) pandemic have been at increased risk for infection due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in some settings. Healthcare-acquired infection has been reported in similar epidemics, but there are limited data on the prevalence of COVID-19 among HCWs and their associated clinical outcomes in the United States. Methods We established 2 high-throughput employee testing centers in Seattle, Washington, with drive-through and walk-through options for symptomatic employees in the University of Washington Medicine system and its affiliated organizations. Using data from these testing centers, we report the prevalence of SARS-CoV-2 infection among symptomatic employees and describe the clinical characteristics and outcomes among employees with COVID-19. Results Between 12 March 2020 and 23 April 2020, 3477 symptomatic employees were tested for COVID-19 at 2 employee testing centers; 185 (5.3%) employees tested positive for COVID-19. The prevalence of SARS-CoV-2 was similar when comparing frontline HCWs (5.2%) with nonfrontline staff (5.5%). Among 174 positive employees reached for follow-up at least 14 days after diagnosis, 6 reported COVID-related hospitalization; all recovered. Conclusions During the study period, we observed that the prevalence of positive SARS-CoV-2 tests among symptomatic HCWs was comparable to that of symptomatic nonfrontline staff. Reliable and rapid access to testing for employees is essential to preserve the health, safety, and availability of the healthcare workforce during this pandemic and to facilitate the rapid return of SARS-CoV-2–negative employees to work.

Background The vaginal microbiome plays a key role in women’s reproductive health. Use of exogenous hormones, such as intramuscular depot medroxyprogesterone acetate (DMPA-IM), may alter the composition of vaginal bacterial community. Methods Vaginal swab samples were collected from postpartum Kenyan women initiating DMPA-IM or nonhormonal contraception (non-HC). Bacterial vaginosis was assessed by Nugent score (Nugent-BV) and bacterial community composition was evaluated using broad-range 16S ribosomal RNA gene polymerase chain reaction with high-throughput sequencing. Changes in Nugent score, alpha diversity (Shannon diversity index), and total bacterial load between contraceptive groups from enrollment to 3 months after initiation were estimated using multivariable linear mixed effects regression. Results Among 54 human immunodeficiency virus–negative women, 33 choosing DMPA-IM and 21 choosing non-HC, Nugent-BV was more common among DMPA-IM users at enrollment. At follow-up, Nugent score had decreased significantly among DMPA-IM users (change, −1.89; 95% confidence interval [CI], −3.53 to −.25; P = .02) while alpha diversity remained stable (0.03; −.24 to .30; P = .83). Conversely, Nugent score remained relatively stable among non-HC users (change, −0.73; 95% CI, −2.18 to .73; P = .33) while alpha diversity decreased (−0.34; −.67 to −.001; P = .05). The total bacterial load decreased slightly in DMPA-IM users and increased slightly among non-HC users, resulting in a significant difference in change between the contraceptive groups (difference, −0.64 log10 gene copies per swab sample; 95% CI, −1.19 to −.08; P = .02). While significant changes in Nugent score and alpha diversity were observed within contraceptive groups, changes between groups were not significantly different. Conclusions Postpartum vaginal bacterial diversity did not change in DMPA-IM users despite a reduction in Nugent-BV, but it decreased significantly among women using non-HC. Choice of contraception may influence Lactobacillus recovery in postpartum women.

Background Nongonococcal urethritis (NGU) is a common syndrome with no known etiology in ≤50% of cases. We estimated associations between urethral bacteria and NGU in men who have sex with men (MSM) and men who have sex with women (MSW). Methods Urine was collected from NGU cases (129 MSM, 121 MSW) and controls (70 MSM, 114 MSW) attending a Seattle STD clinic. Cases had ≥5 polymorphonuclear leukocytes on Gram stain plus symptoms or discharge; controls had Results Among NGU cases, 45.2% were idiopathic. Based on compositional lasso analysis, we selected Haemophilus influenzae (HI) and Mycoplasma penetrans (MP) for targeted qPCR. Compared with 182 men without NGU, the 249 men with NGU were more likely to have HI (14% vs 2%) and MP (21% vs 1%) (both P ≤ .001). In stratified analyses, detection of HI was associated with NGU among MSM (12% vs 3%, P = .036) and MSW (17% vs 1%, P < .001), but MP was associated with NGU only among MSM (13% vs 1%, P = .004). Associations were stronger in men with idiopathic NGU. Conclusions HI and MP are potential causes of male urethritis. MP was more often detected among MSM than MSW with urethritis.

Optimal clinical decision-making depends on identification of clinically relevant organisms present in a sample. Standard microbiological culture may fail to identify unusual or fastidious organisms and can misrepresent relative abundance of sample constituents. Culture-independent methods have improved our ability to deconvolute polymicrobial patient samples. We used next-generation 16S rRNA gene sequencing (NGS16S) to determine how often cultivatable organisms in complex polymicrobial samples are not reported by standard culture. Twenty consecutive bronchoalveolar lavage (BAL) samples were plated to standard and additional media; bacteria were identified by NGS16S analysis of DNA extracted directly from samples or from washed culture plates. 96% of organisms identified were cultivable, but only 21% were reported by standard culture, indicating that standard work-up provides an incomplete assessment of microbial constituents. Direct NGS16S correlated well with standard culture, identifying the same predominant organism in 50% of samples. When predominant organisms differed, NGS16S most often detected anaerobes, whose growth is unsupported by standard culture conditions for this specimen. NGS16S identified more organisms per sample and allowed identification of fastidious organisms, while culture was better at capturing organisms when bacterial load was low, and allowed incidental recovery of non-bacterial pathogens. Molecular and culture-based methods together detect more organisms than either method alone.

Background Up to 30% of women with vaginal symptoms are not assigned a diagnosis after standard diagnostic assessment. Methods We compared premenopausal women with idiopathic vaginitis (IV) or vulvodynia (VVD) to healthy controls. Microbiota were characterized using rRNA sequencing. Cytokines/chemokines (IL-10, IL-1α, IL-1β, IL-6, IL-8, IL-2, IL-18, IL-4, IL-9, and IL-13) were measured in vaginal lavage fluid using the Meso Scale Discovery platform or ELISA (IL-1ra). Immunoglobulins were measured in vaginal lavage fluid using a bead-based immunoassay (Millipore). Cases and controls were compared using Kruskal-Wallis, analysis of variance, and linear regression or (for microbiome composition) the Bray-Curtis dissimilarity statistic. Results We compared 20 women with IV, 30 with VVD, and 52 controls. Most (80%) had greater than 90% 16S rRNA gene sequences from Lactobacillus crispatus, L. jensenii, L. gasseri, or L. iners. In analyses adjusted for age and hormonal contraception (HC), Gardnerella vaginalis was less prevalent and abundant in women with VVD (2/30, 7%) versus controls (16/52, 31%) or IV (5/20, 25%) (P = 0.030). Bray-Curtis dissimilarity was not significantly different between IV and controls or VVD. Fungal sequences were only detected in 5 participants: 2 control, 1 IV, 2 VVD. In univariate analysis, cytokines were not associated with diagnosis. Median vaginal concentration of IgE (but not other immunoglobulins) was lower in women with VVD (P = 0.006). Conclusions Minimal differences in vaginal microbiota and inflammatory markers between women with IV, VVD or controls suggest no striking association between vaginal bacteria, fungi or inflammation and diagnosis in these women.

During the SARS-CoV-2 pandemic, community-based testing sites have provided free COVID testing to millions of people who would have otherwise faced substantial barriers. In one metropolitan area, over 700,000 tests were performed from September 2020 through February 2021. To make appointments online, people not only provided basic demographic information such as age, gender, and address but also free-text reasons for seeking testing. We sought to examine how reason for testing, coded as free text, in combination with other collected demographic information, correlated with rates of SARS-CoV-2 positivity and measures of infectiousness such as the Real-Time Polymerase Chain Reaction (RT-PCR) Cycle Threshold (Ct) value. Data from the scheduling software used to coordinate the on site testing workflow was extracted and linked to SARS-CoV-2 PCR assay metadata stored in a laboratory data warehouse, including Ct values. We categorized individual text responses into 3 separate categories: symptomatic, exposed but asymptomatic, and asymptomatic without exposure. Symptomatic individuals were identified based on the presence of common keywords and phrases like “symptom”, “fever”, “cough”, and “sore throat.” Similarly, exposed individuals were identified by keywords such as “exposed”, “exposure”, and “roommate.” All others were categorized as asymptomatic. We found that symptomatic individuals had the highest frequency of testing positive at 11.6% (21,150/181,868). Exposed individuals and asymptomatic individuals had lower rates of detection with frequencies of 7.4% (10,266/138,721) and 4.9% (8,987/185,026) respectively. Repeat testing represented 29.9% of the results and positivity rates were calculated using the first result for each individual. In addition, among those who tested positive, symptomatic individuals exhibited the lowest average Ct values, while those who are asymptomatic exhibit the highest average Ct values. Among those who self-reported duration of symptoms, recent onset of symptoms was strongly correlated with lower Ct values. Across more than 700,000 results, self-reported symptomatic people, based on free text responses, have a higher likelihood of testing positive for SARS-CoV-2. People who claim a more recent onset of symptoms are more likely to exhibit higher concentrations of viral RNA.

To examine associations between the composition of the vaginal microbiota and genitourinary menopausal symptoms, serum estrogen, and vaginal glycogen.For this cross-sectional study, 88 women aged 40 to 62 years, enrolled in a hot flash treatment trial, provided vaginal swabs and a blood sample at enrollment. Bacterial communities were characterized using 16S rRNA PCR and deep sequencing targeting the V3-V4 region. Quantities of Lactobacillus crispatus and Lactobacillus iners were measured using qPCR. Self-reported genitourinary symptoms included: presence and severity of individual symptoms and identification of most bothersome symptom. Glycogen was measured fluorometrically in swab eluate. Serum estradiol (E2) and estrone (E1) were measured by liquid chromatography/mass spectrometry. Associations between bacteria, symptoms, glycogen, and serum estrogens were tested by linear regression or Wilcoxon signed-rank test, adjusted for multiple comparisons. Comparisons between groups used Kruskall-Wallis or Fisher's exact test.Of the 88 women, 33 (38%) had a majority of Lactobacillus species, whereas 58 (66%) had any Lactobacillus detected. Over half (53%) reported at least one vulvovaginal symptom (most commonly dryness), but symptoms were not associated with the presence of Lactobacillus species. Women with Lactobacillus-dominant communities had higher unconjugated serum estrone, but no difference in vaginal glycogen levels, compared with those with non-Lactobacillus-dominant communities. Higher serum E2 and E1 were not associated with either higher vaginal glycogen or detection of individual genera.Presence of Lactobacillus-dominant vaginal microbiota was not associated with fewer vulvovaginal symptoms. Serum estrone was higher in women with Lactobacillus dominance, but vaginal-free glycogen was not associated with composition of the vaginal microbiota.

Background Microbiome studies commonly use 16S rRNA gene amplicon sequencing to characterize microbial communities. Errors introduced at multiple steps in this process can affect the interpretation of the data. Here we evaluate the accuracy of operational taxonomic unit (OTU) generation, taxonomic classification, alpha- and beta-diversity measures for different settings in QIIME, MOTHUR and a pplacer-based classification pipeline, using a novel software package: DECARD. Results In-silico we generated 100 synthetic bacterial communities approximating human stool microbiomes to be used as a gold-standard for evaluating the colligative performance of microbiome analysis software. Our synthetic data closely matched the composition and complexity of actual healthy human stool microbiomes. Genus-level taxonomic classification was correctly done for only 50.4–74.8% of the source organisms. Miscall rates varied from 11.9 to 23.5%. Species-level classification was less successful, (6.9–18.9% correct); miscall rates were comparable to those of genus-level targets (12.5–26.2%). The degree of miscall varied by clade of organism, pipeline and specific settings used. OTU generation accuracy varied by strategy (closed, de novo or subsampling), reference database, algorithm and software implementation. Shannon diversity estimation accuracy correlated generally with OTU-generation accuracy. Beta-diversity estimates with Double Principle Coordinate Analysis (DPCoA) were more robust against errors introduced in processing than Weighted UniFrac. The settings suggested in the tutorials were among the worst performing in all outcomes tested. Conclusions Even when using the same classification pipeline, the specific OTU-generation strategy, reference database and downstream analysis methods selection can have a dramatic effect on the accuracy of taxonomic classification, and alpha- and beta-diversity estimation. Even minor changes in settings adversely affected the accuracy of the results, bringing them far from the best-observed result. Thus, specific details of how a pipeline is used (including OTU generation strategy, reference sets, clustering algorithm and specific software implementation) should be specified in the methods section of all microbiome studies. Researchers should evaluate their chosen pipeline and settings to confirm it can adequately answer the research question rather than assuming the tutorial or standard-operating-procedure settings will be adequate or optimal. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1690-0) contains supplementary material, which is available to authorized users.

Introduction/Objective To increase efficiency of blood product ordering and delivery processes and improve appropriateness of orders, a major project to implement clinical decision support (CDS) alerts in the electronic medical record (EMR) was undertaken. A design team was assembled including hospital and laboratory medicine information technology and clinical informatics, transfusion services, nursing and clinical services from medical and surgical specialties. Methods Consensus-derived thresholds in hemoglobin/hematocrit, platelet count, INR, and fibrinogen for red blood cell (RBC), platelet, plasma, and cryoprecipitate blood products CDS alerts were determined. Data from the EMR and laboratory information system were queried from the 12-month period before and after implementation and the data was analyzed. Results During the analysis period, 5813 RBC (avg. monthly = 484), 1040 platelet (avg. monthly = 87), 423 plasma (avg. monthly = 35), and 88 cryoprecipitate (avg. monthly = 7) alerts fired. The average time it took for a user to respond was 5.175 seconds. The total amount of time alerts displayed over 12 months was 5813 seconds (~97 minutes of user time) compared to 56503 blood products transfused. Of active CDS alerts, hemoglobin/RBC alerts fired most often with ~1:5 (31141 RBC units) alert to transfusion ratio and 4% of orders canceled (n=231) when viewing the alert, platelet alerts fired with ~1:15 (15385 platelet units) alert to transfusion ratio and 6% orders canceled (n=66), INR/plasma alerts fired with ~1:21 (8793 plasma units) alert to transfusion ratio and 10% orders canceled (n=41), cryoprecipitate alerts fired with ~1:13 (1184 cryoprecipitate units) alert to transfusion ratio and 10% orders canceled (n=9). Overall monthly blood utilization normalized to 1000 patient discharges did not appear to have statistically significant differences comparing pre- versus post-go-live, except a potentially significant increase in monthly plasma usage at one facility with p = 0.34, although possibly due to an outlier single month of heavy usage. Conclusion Clinical decision support alerts can guide provider ordering with minimal user burden. This resulted in increased safety and quality use of the ordering process, although overall blood utilization did not appear to change significantly.

Enterobacteriaceae represent a diverse and medically important family of bacteria that are difficult to identify to the species level using the standard molecular method of 16S rRNA gene sequencing. Prior work has demonstrated the value of dnaJ gene sequence analysis in resolving different members of the family. However, existing protocols are not optimized for clinical use and exhibit several limitations in practice. Here, we describe an improved assay for dnaJ-based identification of Enterobacteriaceae which boasts increased broad-range specificity across genera, shorter amplicon sizes that are suitable for use with formalin-fixed or direct patient specimens, and enhanced amplification efficiency and assay sensitivity through the incorporation of locked nucleic acid chemistries. Sequence analysis of public databases indicates that the partial dnaJ sequence interrogated by this design retains high discriminatory power among Enterobacteriaceae genera and species, with only particular lineages of Shigella sp. and Escherichia coli proving unresolvable. Limits of detection studies using 8 disparate species indicated that amplification was consistently achievable across organisms and allowed robust dideoxynucleotide chain terminator sequencing from as little as 10 genome equivalents of template, depending on the species interrogated. Retrospective application of the dnaJ assay to patient specimens enabled unambiguous classification of Enterobacteriaceae to the species level in 22 of 27 (81.5%) positive specimens examined, with most remaining cases representing unresolvable calls between closely related Escherichia coli and Shigella species. We expect that this assay will facilitate the accurate molecular identification of species from the Enterobacteriaceae family in a variety of clinical specimens and diagnostic contexts.

Background Laboratories performing clinical high-throughput sequencing for oncology and germline testing are increasingly migrating their data storage to cloud-based solutions. Cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; however, while these solutions tout ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. Methods We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing ( https://ngscosts.info ). Our tool enables quick exploration of dozens of storage options across three major cloud providers, and provides complex cost and usage forecasts over 1–20 year timeframes. Parameters include current test volumes, growth rate, data compression, data retention policies, and case re-access rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Results Two factors were found to markedly decrease the average cost per test: 1) reducing total file size, including through the use of compression, 2) rapid transfer to “cold” or archival storage. In contrast, re-access of data from archival storage tiers was not found to dramatically increase the cost of storage per test. Conclusions Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.

Background Studies suggest that sexual behavior influences the composition of the male urethral microbiota, but this hypothesis has not been tested. Methods From 12/2014–5/2018, we enrolled MSM with NGU attending an STD clinic into a cohort study. Men attended five in-clinic visits at 3-week intervals, collected weekly urine specimens at home, and reported daily antibiotics and sex on weekly diaries. We applied broad-range 16S rRNA gene PCR with deep sequencing to urine. We estimated incidence of insertive oral sex (IOS) only, condomless insertive anal intercourse (CIAI) only, and IOS with CIAI (IOS+CIAI) after NGU diagnosis using Poisson regression with robust standard errors. We estimated the association between urethral sexual exposures (referent group=none) in seven 3-day time windows before specimen collection and Shannon Index (diversity) and log10 number of bacterial species (richness) using generalized estimating equations, adjusting for recent antibiotics, age, race/ethnicity, HIV status, and PrEP use. For each exposure category, we tested whether all seven window coefficients were equal to zero (i.e., no overall association) using a Wald test. Results Among 92 MSM with NGU, median age was 31 (interquartile range [IQR]=28–40); 55% were non-Hispanic white. They contributed 1,095 person-weeks of behavioral data (median=12 diaries/man, IQR=12–13). Incidence of any sex, IOS only, CIAI only, and IOS+CIAI were 1.07 (95% confidence interval [CI]=0.93–1.24), 0.40 (95%CI=0.32–0.49), 0.10 (95%CI=0.07–0.15), and 0.40 (95%CI=0.30–0.52) episodes per person-week, respectively. Among 894 urine specimens (median=10 specimens/man, IQR=8–12), median diversity was 1.33 (IQR=0.76–1.99), and median richness was 14 species (IQR=9–23). Overall, CIAI only (P Conclusion Among MSM after NGU, CIAI only in the prior 21 days was independently associated with diversity and richness of the urethral microbiota. Disclosure No significant relationships.

Background NGU is common with no known etiology in ∼50% of cases. We evaluated the association of urethral bacteria with NGU among men who have sex with men (MSM) and men who have sex with women (MSW). Methods Urine samples were collected from MSM and MSW attending a Seattle STD Clinic and enrolled in a cross-sectional study. Chlamydia trachomatis (CT) and Mycoplasma genitalium (MG) were detected by TMA (Aptima), and adenovirus, HSV-1 and HSV-2 by PCR. NGU was defined as having urethral symptoms or visible discharge and ≥5 PMNs/high powered field (HPF). Absence of CT, MG, adenovirus, and HSV was considered as idiopathic NGU. Men without NGU had no urethral symptoms, no discharge, and Results Of 434 (199 MSM, 235 MSW) urine samples, 330 yielded sequence data. NGU+ men were less likely to yield sequence data (70% vs 84%, Fisher’s p=0.001). Of 328 men with ≥1000 sequence reads/sample, 95 MSM (44 NGU+) and 143 MSW (46 NGU+) were negative for CT, MG, adenovirus, and HSV. Higher relative abundances of Haemophilus influenzae (β=0.0139) and Mycoplasma penetrans (β=0.0095) were positively associated with idiopathic NGU in MSM, while H. influenzae was positively associated with idiopathic NGU in MSW (β=0.0184). The model also identified bacterial species that were negatively associated with NGU in MSM and MSW. Notably, Lactobacillus iners was negatively associated with idiopathic NGU in MSW (β=−0.0006) but not MSM. Conclusion Different bacterial species are associated with NGU in MSM and MSW. We identified two bacterial species infrequently detected in the male urethra as positively associated with NGU. Disclosure No significant relationships.

Background The most common approach for describing bacterial communities is amplification of a taxonomically informative gene (e.g. 16S rRNA) followed by amplicon sequencing and taxonomic assignment of the sequences. Variability can arise from numerous steps in this process including DNA extraction, PCR amplification, and bioinformatics approaches for taxonomic assignment. To better understand sources of variation in describing the vaginal microbiota, we conducted a comparative study across four laboratories. Methods A central laboratory prepared and distributed a specimen set including vaginal swabs from four women with a range of Nugent scores (in vivo samples), three mock communities of vaginal bacteria, and positive and negative controls. For in vivo and mock communities, each laboratory was also provided specimens that underwent DNA extraction by the central laboratory. Laboratories followed their standard laboratory and bioinformatics processes. Results were analyzed by a central group blinded to laboratory. Results For mock and in vivo communities dominated by a mix of Lactobacillus species, all laboratories successfully detected each of the taxa in the sample and reported similar relative abundances. For mock communities containing BV-associated taxa, most laboratories detected all taxa; however, some taxa, including Prevotella amnii and Atopobium vaginae, were not detected by all laboratories and there was more variation in relative abundances across the laboratories (P. amnii relative abundance range= Conclusion Despite differences in methods, in most cases laboratories would have come to the same conclusion regarding dominant taxa in a sample, especially for Lactobacillus-dominant samples. Samples with more diverse communities had more variation in reports of minority taxa and relative abundances. Standardized use of mock communities may improve reproducibility across vaginal microbiota studies. Disclosure No significant relationships.

BACKGROUND Humans suffer from infections caused by single species or more complex polymicrobial communities. Identification of infectious bacteria commonly employs microbiological culture, which depends upon the in vitro propagation and isolation of viable organisms. In contrast, detection of bacterial DNA using next generation sequencing (NGS) allows culture-independent microbial profiling, potentially providing important new insights into the microbiota in clinical specimens. METHODS NGS 16S rRNA gene sequencing (NGS16S) was compared with culture using (a) synthetic polymicrobial samples for which the identity and abundance of organisms present were precisely defined and (b) primary clinical specimens. RESULTS Complex mixtures of at least 20 organisms were well resolved by NGS16S with excellent reproducibility. In mixed bacterial suspensions (107 total genomes), we observed linear detection of a target organism over a 4-log concentration range (500–3 × 106 genomes). NGS16S analysis more accurately recapitulated the known composition of synthetic samples than standard microbiological culture using nonselective media, which distorted the relative abundance of organisms and frequently failed to identify low-abundance pathogens. However, extended quantitative culture using selective media for each of the component species recovered the expected organisms at the proper abundance, validating NGS16S results. In an analysis of sputa from cystic fibrosis patients, NGS16S identified more clinically relevant pathogens than standard culture. CONCLUSIONS Biases in standard, nonselective microbiological culture lead to a distorted characterization of polymicrobial mixtures. NGS16S demonstrates enhanced reproducibility, quantification, and classification accuracy compared with standard culture, providing a more comprehensive, accurate, and culture-free analysis of clinical specimens.

BACKGROUND Women with bacterial vaginosis (BV) have complex communities of anaerobic bacteria. There are no cultivated isolates of several bacteria identified using molecular methods and associated with BV. It is unclear whether this is due to the inability to adequately propagate these bacteria or to correctly identify them in culture. METHODS Vaginal fluid from 15 women was plated on 6 different media using classical cultivation approaches. Individual isolates were identified by 16S ribosomal RNA (rRNA) gene sequencing and compared with validly described species. Bacterial community profiles in vaginal samples were determined using broad-range 16S rRNA gene polymerase chain reaction and pyrosequencing. RESULTS We isolated and identified 101 distinct bacterial strains spanning 6 phyla including (1) novel strains with

Background The growth of clinical NGS testing, together with new CAP checklist items stating that read-level (raw) NGS data must be retained for a minimum of 2 years, has prompted laboratories to reevaluate their NGS data storage solutions. In this context, cloud-based storage has several advantages, such as low per-GB prices, scalability, and minimal fixed costs; nonetheless, several perceived disadvantages, including increased complexity and security/regulatory considerations, have hindered adoption. Furthermore, despite the ostensibly simple usage-based pricing plans, practical cost analysis of cloud storage for NGS data storage is not straightforward. Methods We developed an easy-to-use tool designed specifically for cost and usage estimation for laboratories performing clinical NGS testing (https://ngscosts.info). Our tool enables quick exploration of dozens of storage options across three major cloud providers and provides complex cost and usage forecasts over 10- to 20-year timeframes. Parameters include current test volumes (and customizable file sizes), expected test volume growth rate, data compression techniques, data retention times across storage tiers, and case reaccess rates. Outputs include an easy-to-visualize chart of total data stored, yearly and lifetime costs, and a “cost per test” estimate. Results The per-GB storage prices among major cloud providers have dropped ~6 to 7× over the past 10 years; in addition, there currently exists an up to 20× price difference between storage tiers. Lower prices are typically offset by either increased access/transfer costs or slower retrieval rates. Two factors were found to markedly affect the average cost per test: (1) total file size, including the use of NGS-specific file compression techniques, and (2) storage tiers and rapid transfer to “cold” or archival storage. In contrast, some factors were not found to affect total costs, including costs associated with reaccess from archival storage tiers, and discounts associated with increased volumes. When data from NGS testing were stored in “hot” storage for 1 year, then archived for 9 years, typical costs per exome were ~$3/test and genomes ranged from $40/test to $66/test, depending on compression algorithms used. Overall, reaccess of data from cold storage added 2% or less to the total cost per test (with an estimated reaccess rate of 10% of cases per year). Conclusions Steady declines in cloud storage pricing, as well as new options for storage and retrieval, make storing clinical NGS data on the cloud economical and friendly to laboratory workflows. Taken together, laboratories should not be concerned about reaccess costs but rather should focus on data compression techniques and archival storage tiers for long-term data. Our web-based tool makes it possible to explore and compare cloud storage solutions and provide forecasts specifically for clinical NGS laboratories.

Awareness of turnaround times (TATs) is important for time-sensitive clinical decision making and setting expectations for timing communications with patients. In fact, a significant number of clinicians consider the TAT as the most important measure of laboratory quality (PMID 24839360). TAT definitions vary considerably, ranging from rough estimates to more precisely defined statistics based on historical data, the former more typically provided in online test catalogs and the latter usually reserved for a limited number of tests for internal quality assurance and improvement. As data warehousing of laboratory information is becoming more commonplace, calculation of TATs can more easily be performed. Our goal in this project was to develop a generalized, automated approach to calculate expected TATs for publication in our online test catalog. We began by defining TAT as the amount of time between receipt of the specimen by the laboratory and the reporting of the finalized result in the laboratory information system (using specimen collection time increased median TATs by 21 minutes). The TAT of an order set/panel of tests was calculated using the time stamp of the last finalized test result of the set. For development purposes, a subset of warehouse data corresponding to all tests within a 1-month period was selected (170,363 accession numbers, 2,206,311 total test results). All accessions with “add-on requests” (tests requested by physicians using specimens collected for prior tests) were removed. The median and 90th percentile TATs were calculated using R for each test and panel, as these values are commonly used in quality assurance programs and are thought to be intuitively easy to understand in a test menu. Values were not reported if fewer than 100 test results were available for a given test. A summary table and TAT distribution graphs are provided for review by laboratory personnel before being published in the online test menu. Categorizing tests ordered “stat” versus “nonstat” generated some interesting insights, the most obvious being that, for the majority of our tests, the difference in median TATs is very small. In some cases, however, the median stat TAT was actually longer (eg, hemoglobin A1C), which might be due to blood draws at irregular hours by phlebotomists, then batching of samples with specimens collected later at regular blood draw times, suggesting that elimination of the stat option was appropriate. Distinct advantages of using data warehouses to calculate TATs include being able to provide clinicians with data-driven estimates of expected lab result TATs. Such data can also be used for internal quality assurance purposes. Areas of further development include developing a more rigorous mathematical theory of the precision associated with our median and 90th percentile TATs and tuning the frequency of data query and TAT recalculation.

OBJECTIVE: Identify associations between improvement in genitourinary symptoms of menopause (GSM) and vaginal microbiota, vaginal glycogen and serum estrogen. METHODS: Thirty postmenopausal women enrolled in a hot flash treatment trial (oral estradiol vs. venlafaxine vs. placebo) who reported GSM and provided vaginal swabs at 0, 4 and 8 weeks were studied. Bacterial communities were characterized using deep sequencing targeting the 16S rRNA gene V3–V4 region. Participants selected a most bothersome genitourinary symptom (dryness, discharge, pain, itch/burn or inability to have sex) and rated severity on a 10-point scale at baseline and 8 weeks. Vaginal glycogen and serum estradiol and estrone were measured at enrollment and 8 weeks. Comparisons according to improvement in most bothersome symptom (MBS) were made using chi-square, Wilcoxon signed-rank test, or Hotelling’s t test. RESULTS: Of 30 participants, 21 (70%) had improvement in MBS over the 8-week study, and 9 (30%) had no improvement or worsening of MBS. A higher proportion of women receiving estradiol or venlafaxine reported improvement in MBS (88%, 78%) compared to placebo (54%; p = 0.28). MBS improvement was associated with Lactobacillus-dominant vaginal microbiota at enrollment (57% vs 22%, p = 0.08). Vaginal glycogen, serum estradiol and estrone increased significantly in women whose MBS improved. CONCLUSION: Improvement in GSM was more common in women receiving oral estradiol or venlafaxine and those with Lactobacillus-dominant vaginal microbiota at enrollment but differences were not statistically significant. Larger trials are need to determine whether vaginal microbiota modify or mediate treatment responses in women with GSM.

High-throughput sequencing of the taxonomically informative 16S rRNA gene provides a powerful approach for exploring microbial diversity. Here we compare the performances of two common “benchtop” sequencing platforms, Illumina MiSeq and Ion Torrent Personal Genome Machine (PGM), for bacterial community profiling by 16S rRNA (V1-V2) amplicon sequencing. We benchmarked performance by using a 20-organism mock bacterial community and a collection of primary human specimens. We observed comparatively higher error rates with the Ion Torrent platform and report a pattern of premature sequence truncation specific to semiconductor sequencing. Read truncation was dependent on both the directionality of sequencing and the target species, resulting in organism-specific biases in community profiles. We found that these sequencing artifacts could be minimized by using bidirectional amplicon sequencing and an optimized flow order on the Ion Torrent platform. Results of bacterial community profiling performed on the mock community and a collection of 18 human-derived microbiological specimens were generally in good agreement for both platforms; however, in some cases, results differed significantly. Disparities could be attributed to the failure to generate full-length reads for particular organisms on the Ion Torrent platform, organism-dependent differences in sequence error rates affecting classification of certain species, or some combination of these factors. This study demonstrates the potential for differential bias in bacterial community profiles resulting from the choice of sequencing platform alone.

Introduction There is no known aetiology for up to half of all nongonococcal urethritis (NGU) cases. We sought to characterise the bacterial communities in men with (NGU+) and without (NGU-) NGU among men who have sex with men (MSM) and men who have sex with women (MSW), to determine if the urethral microbiota is associated with NGU status. Methods From December 2014 to December 2015, urine samples were collected from 49 MSM (23 NGU+; 26 NGU-) and 48 MSW (21 NGU+; 27 NGU-) attending the Seattle STD Clinic. NGU was defined as urethral symptoms and/or visible discharge, and >5 PMNs per high powered field. Chlamydia trachomatis (CT) and Mycoplasma genitalium (MG) were detected by transcription mediated amplification (TMA). The urethral microbiota was characterised using broad-range 16S rRNA gene PCR with deep sequencing. Bacterial diversity was calculated using the Shannon index. Results Mean urethral bacterial diversity in NGU+ MSM (0.82) was lower than in NGU- MSM (1.48), Streptococcus (n=7), Corynebacterium (n=5), Haemophilus (n=3), Mycoplasma (n=3), and Lactobacillus iners (n=2). Urethral microbiotas with dominant taxa were more common in NGU+ MSM (95.7%), while NGU+ MSW (61.9%) had microbiotas characterised by the presence of several vaginal bacteria. Among NGU- men, 53.8% MSM and 48.1% MSW had microbiotas with dominant taxa, including Streptococcus (n=19), L. iners (n=5), and Gardnerella vaginalis (n=4). Conclusion The urethral microbiota in NGU is heterogeneous. NGU- MSM had more diverse urethral bacterial communities than NGU+ MSM, and presence of vaginal bacteria in MSW suggests sharing or acquisition from female partners. Future longitudinal studies may help inform if key bacteria predict incident NGU.

Hospital labs are increasingly consolidating into larger hospital systems with centralized testing locations. Specimens collected at disparate sites need to be transported to different locations for testing. Our LIS (Sunquest FlexiLab) does not provide a real-time display of specimens in need of transport to other locations. The current system lists pending specimens in paper printouts. The printouts have a difficult-to-read format, fail to account for certain add-on tests, and leave room for human error. In addition, IT staff must maintain test groups that are specific to each sending and receiving facility pair. This is a costly use of IT resources and is error prone due to test menu differences at each facility. In the current study, we evaluated the current system of batching specimens for transport and then designed a web-based application to provide a dynamic display of transport batches. The web-based application that we developed is called a “transport batch monitor” (TBM). The TBM displays specimens that need to be sent to another location for testing. This application was implemented in Python using the Flask microframework. Data are transferred in JSON format from Sunquest every 3 minutes using custom Caché ObjectScript routines. The application can be accessed with a secure login from any computer with Internet access and is displayed on large, fixed monitors in locations where transport batches are assembled. The display shows pertinent information for lab techs to be able to locate specimens that need to be sent out and is color-coded for ease of use. Metrics used to evaluate the current system included timing studies, surveys of lab staff performing the task, and paper usage. The process of collecting and batching specimens for transport to another facility took on average 24 minutes and required on approximately 22 sheets of paper. Anecdotally, specimens had historically been missed, especially when clinicians ordered add-on tests on specimens from days prior due to the way the LIS generated the printouts. Although 74% of techs surveyed reported that the task was somewhat easy or very easy, 32% were somewhat dissatisfied with the current system, and 40% said that the current paper printouts were either very unclear or somewhat unclear. Postimplementation metrics will be collected once the TBM has been implemented. We hypothesize that the transport batch monitor is a simple, inexpensive web-based tool that can greatly improve the day-to-day functioning of the lab. We hope it will improve efficiency and tech satisfaction, and reduce errors.

Clinical laboratories may have “on-call” residents and fellows, a practice that assists the laboratory in providing quality support to clinicians and provides valuable clinical experience to trainees. A Call Database may be used as a tool to facilitate call handoffs, as an educational resource and knowledge base for trainees, and as a call logging and review tool as required by the Accreditation Council for Graduate Medical Education (ACGME). The Call Database at our institution currently contains over 51,000 entries and is used by all clinical pathology residents and fellows while “on call.” Prior versions of our Call Database (first developed in 2004 and updated in 2014) were self-hosted applications with limited feature sets that were difficult to support; moreover, they provided few guarantees around electronic protected health information (ePHI), a topic of increasing concern as HIPAA compliance among clinical laboratories is increasingly audited. A review of the current application also identified that it has limited usefulness in supporting faculty review of calls, does not easily allow structured data entry for quality improvement projects, and lacks features commonly seen in modern web applications (eg, rich text editing and file attachments). Here we describe the latest update to our existing Call Database addressing these feature limitations. In addition, we discuss our approach to using a modern cloud-based infrastructure design that address prior shortcomings in data security, user management, and ease of development. The updated Call Database is a single-page web application, compatible with a wide range of local and cloud environments. We updated the user interface with features such as auto-saving of entries, rich text entry, file attachments, and topic tagging. Several new features facilitate the faculty review process: (1) a dedicated “review mode” for rapid viewing and commenting of relevant calls, (2) automated weekly emails by topic sent to faculty, and (3) a user “mention” feature so that trainees can solicit the opinion of faculty in the text of a call or comment itself. Finally, we provide support for customizing the structured data fields of call entries, which enables support of quality improvement and monitoring projects via the call database (eg, a customized entry for monitoring use of specific send-out tests, massive transfusion reactions, etc.). Our application delivers a modern, performant, and easy to use call database for use by trainees and faculty alike. We utilize the Amazon Cloud (AWS) to host the application and have developed a specific set of compliance and risk review documents to address HIPAA compliance. Future work will incorporate user feedback and will focus on supporting the implementation of our application at other sites or clinical pathology residency programs.

Next-generation DNA sequencing can be used to catalog individual organisms within complex, polymicrobial specimens. Here, we utilized deep sequencing of 16S rRNA to implicate Actinomadura madurae as the cause of mycetoma in a diabetic patient when culture and conventional molecular methods were overwhelmed by overgrowth of other organisms.

High-throughput sequencing of the taxonomically informative 16S rRNA gene provides a powerful approach for exploring microbial diversity. Here we compare the performances of two common "benchtop" sequencing platforms, Illumina MiSeq and Ion Torrent Personal Genome Machine (PGM), for bacterial community profiling by 16S rRNA (V1-V2) amplicon sequencing. We benchmarked performance by using a 20-organism mock bacterial community and a collection of primary human specimens. We observed comparatively higher error rates with the Ion Torrent platform and report a pattern of premature sequence truncation specific to semiconductor sequencing. Read truncation was dependent on both the directionality of sequencing and the target species, resulting in organism-specific biases in community profiles. We found that these sequencing artifacts could be minimized by using bidirectional amplicon sequencing and an optimized flow order on the Ion Torrent platform. Results of bacterial community profiling performed on the mock community and a collection of 18 human-derived microbiological specimens were generally in good agreement for both platforms; however, in some cases, results differed significantly. Disparities could be attributed to the failure to generate full-length reads for particular organisms on the Ion Torrent platform, organism-dependent differences in sequence error rates affecting classification of certain species, or some combination of these factors. This study demonstrates the potential for differential bias in bacterial community profiles resulting from the choice of sequencing platform alone.

Objectives: To apply techniques for ancestry and sex computation from next-generation sequencing (NGS) data as an approach to confirm sample identity and detect sample processing errors. Methods: We combined a principal component analysis method with k -nearest neighbors classification to compute the ancestry of patients undergoing NGS testing. By combining this calculation with X chromosome copy number data, we determined the sex and ancestry of patients for comparison with self-report. We also modeled the sensitivity of this technique in detecting sample processing errors. Results: We applied this technique to 859 patient samples with reliable self-report data. Our k -nearest neighbors ancestry screen had an accuracy of 98.7% for patients reporting a single ancestry. Visual inspection of principal component plots was consistent with self-report in 99.6% of single-ancestry and mixed-ancestry patients. Our model demonstrates that approximately two-thirds of potential sample swaps could be detected in our patient population using this technique. Conclusions: Patient ancestry can be estimated from NGS data incidentally sequenced in targeted panels, enabling an inexpensive quality control method when coupled with patient self-report.

Background Despite their widespread use, the performance of delta check rules is rarely evaluated because errors are rare and lack a gold standard for detection. In this study we used a simulation-based approach to compare strategies for empirically defining criteria for univariate delta checks, and assessed the performance of these rules for detecting mislabeled specimens in 2 inpatient populations. Methods We performed simulations using historical laboratory test results by randomly sampling pairs of specimens successively drawn from the same patient or two different patients. We evaluated the performance of delta check rules using a variety of thresholds, including those currently in use in our laboratory. Result Mean corpuscular volume had the highest positive predictive value for specimen mislabeling, and produced the fewest false positives. Conversely, rules using other laboratory tests had considerably poorer performance. Several of the “best guess” thresholds historically used in our laboratory, notably those for potassium and anion gap, were predicted to have extremely low yields. In addition, rule performance was not consistent between the two patient populations. Conclusions The low yield of delta checks based on any single analyte should prompt careful evaluation of their practical utility. Furthermore, our results indicate that it may not be possible to generalize delta rules across institutions.

Objective: To reduce wasteful ordering of rare 1,25-OH vitamin D lab tests through use of a noninterruptive decision support tool. Materials and Methods: We conducted a time series quality improvement study at 2 academic hospitals. The titles of vitamin D tests and the order in which they appeared in search results were changed to reflect the purpose and rarity of the tests. We used interruptive time series analyses to evaluate the changes we made. Results: The estimated number of monthly tests ordered at the 2 hospitals increased, by 24.8 and 14.2, following the introduction of computerized provider order entry (CPOE) (both P Discussion: Changing catalog names in CPOE systems for infrequently used tests can reduce unintentional overuse. Users may prefer this to interruptive or restrictive interventions. Conclusion: CPOE vendors and users should refine interfaces by incorporating human factors engineering. Health care institutions should monitor test utilization for unintended changes after CPOE implementation.

Bacterial vaginosis (BV) is characterized by shifts in the vaginal microbiota from Lactobacillus dominant to a microbiota with diverse anaerobic bacteria. Few studies have linked specific metabolites with bacteria found in the human vagina. Here, we report dramatic differences in metabolite compositions and concentrations associated with BV using a global metabolomics approach. We further validated important metabolites using samples from a second cohort of women and a different platform to measure metabolites. In the primary study, we compared metabolite profiles in cervicovaginal lavage fluid from 40 women with BV and 20 women without BV. Vaginal bacterial representation was determined using broad-range PCR with pyrosequencing and concentrations of bacteria by quantitative PCR. We detected 279 named biochemicals; levels of 62% of metabolites were significantly different in women with BV. Unsupervised clustering of metabolites separated women with and without BV. Women with BV have metabolite profiles marked by lower concentrations of amino acids and dipeptides, concomitant with higher levels of amino acid catabolites and polyamines. Higher levels of the signaling eicosanoid 12-hydroxyeicosatetraenoic acid (12-HETE), a biomarker for inflammation, were noted in BV. Lactobacillus crispatus and Lactobacillus jensenii exhibited similar metabolite correlation patterns, which were distinct from correlation patterns exhibited by BV-associated bacteria. Several metabolites were significantly associated with clinical signs and symptoms (Amsel criteria) used to diagnose BV, and no metabolite was associated with all four clinical criteria. BV has strong metabolic signatures across multiple metabolic pathways, and these signatures are associated with the presence and concentrations of particular bacteria., IMPORTANCE Bacterial vaginosis (BV) is a common but highly enigmatic condition that is associated with adverse outcomes for women and their neonates. Small molecule metabolites in the vagina may influence host physiology, affect microbial community composition, and impact risk of adverse health outcomes, but few studies have comprehensively studied the metabolomics profile of BV. Here, we used mass spectrometry to link specific metabolites with particular bacteria detected in the human vagina by PCR. BV was associated with strong metabolic signatures across multiple pathways affecting amino acid, carbohydrate, and lipid metabolism, highlighting the profound metabolic changes in BV. These signatures were associated with the presence and concentrations of particular vaginal bacteria, including some bacteria yet to be cultivated, thereby providing clues as to the microbial origin of many metabolites. Insights from this study provide opportunities for developing new diagnostic markers of BV and novel approaches for treatment or prevention of BV.

Human immunodeficiency virus type 1 (HIV-1) exists as a complex population of multiple genotypic variants in persons with chronic infection. However, acute HIV-1 infection via sexual transmission is a low-probability event in which there is thought to be low genetic complexity in the initial inoculum. In order to assess the viral complexity present during primary HIV-1 infection, the V1/V2 and V3 variable regions of the env gene were examined by using a heteroduplex tracking assay (HTA) capable of resolving these genotypic variants. Blood plasma samples from 26 primary HIV-1-infected subjects were analyzed for their level of diversity. Half of the subjects had more than one V1/V2 viral variant during primary infection, indicating the frequent transmission of multiple variants. This observation is inconsistent with the idea of infrequent transmission based on a small transmitting inoculum of cell-free virus. In chronically infected subjects, the complexity of the viral populations was even greater in both the V1/V2 and the V3 regions than in acutely infected subjects, indicating that in spite of the presence of multiple variants in acute infection, the virus does pass through a genetic bottleneck during transmission. We also examined how well the infecting virus penetrated different anatomical compartments by using the HTA. Viral variants detected in blood plasma were compared to those detected in seminal plasma and/or cerebral spinal fluid of six individuals. The virus in each of these compartments was to a large extent identical to virus in blood plasma, a finding consistent with rapid penetration of the infecting variant(s). The low-probability transmission of multiple variants could be the result of transient periods of hyperinfectiousness or hypersusceptibility. Alternatively, the inefficient transfer of a multiply infected cell could account for both the low probability of transmission and the transfer of multiple variants.