Using Electronic Health Record Data to Measure the Latent Tuberculosis Infection Care Cascade in Safety-Net Primary Care Clinics.

Introduction: Prevention of tuberculosis disease through diagnosis and treatment of latent tuberculosis infection is critical for achieving tuberculosis elimination in the U.S. Diagnosis and treatment of latent tuberculosis infection in safety-net primary care settings that serve patients at risk for tuberculosis may increase uptake of this prevention effort and accelerate progress toward elimination. Optimizing tuberculosis prevention in these settings requires measuring the latent tuberculosis infection care cascade (testing, diagnosis, and treatment) and identifying gaps to develop solutions to overcome barriers. We used electronic health record data to describe the latent tuberculosis infection care cascade and identify gaps among a network of safety-net primary care clinics. Methods: Electronic health record data for patients seen in the OCHIN Clinical Network, the largest network of safety-net clinics in the U.S., between 2012 and 2019 were extracted. electronic health record data were used to measure the latent tuberculosis infection care cascade: patients who met tuberculosis screening criteria on the basis of current recommendations were tested for tuberculosis infection, diagnosed with latent tuberculosis infection, and prescribed treatment for latent tuberculosis infection. Outcomes were stratified by diagnostic test and treatment regimen. Results: Among 1.9 million patients in the analytic cohort, 43.5% met tuberculosis screening criteria, but only 21.4% were tested for latent tuberculosis infection; less than half (40.4%) were tested using an interferon-gamma release assay. Among those with a valid result, 10.5% were diagnosed with latent tuberculosis infection, 29.1% of those were prescribed latent tuberculosis infection treatment, and only 33.6% were prescribed a recommended rifamycin-based regimen. Conclusions: Electronic health record data can be used to measure the latent tuberculosis infection care cascade. A large proportion of patients in this safety-net clinical network are at high risk for tuberculosis infection. Addressing identified gaps in latent tuberculosis infection testing and treatment may have a direct impact on improving tuberculosis prevention in primary care clinics and accelerate progress toward elimination.

Examining Test Cutoffs to Optimize Diagnosis of Latent Tuberculosis Infection in People Born Outside the United States.

Rationale: Detection of latent tuberculosis infection (LTBI) in persons born in high tuberculosis (TB) incidence countries living in low TB incidence countries is key to TB elimination in low-incidence countries. Optimizing LTBI tests is critical to targeting treatment. Objectives: To compare the sensitivity and specificity of tuberculin skin test (TST) and two interferon-γ release assays at different cutoffs and of a single test versus dual testing. Methods: We examined a subset (N = 14,167) of a prospective cohort of people in the United States tested for LTBI. We included non-U.S.-born, human immunodeficiency virus-seronegative people ages 5 years and older with valid TST, QuantiFERON-TB Gold-in-Tube (QFT), and T-SPOT.TB (TSPOT) results. The sensitivity/specificity of different test cutoffs and test combinations, obtained from a Bayesian latent class model, were used to construct receiver operating characteristic (ROC) curves and assess the area under the curve (AUC) for each test. The sensitivity/specificity of dual testing was calculated. Results: The AUC of the TST ROC curve was 0.81 (95% credible interval (CrI), 0.78-0.86), with sensitivity/specificity at cutoffs of 5, 10, and 15 mm of 86.5%/61.6%, 81.7%/71.3%, and 55.6%/88.0%, respectively. The AUC of the QFT ROC curve was 0.89 (95% CrI, 0.86-0.93), with sensitivity/specificity at cutoffs of 0.35, 0.7, and 1.0 IU/mL of 77.7%/98.3%, 66.9%/99.1%, and 61.5%/99.4%. The AUC of the TSPOT ROC curve was 0.92 (95% CrI, 0.88-0.96) with sensitivity/specificity for five, six, seven, and eight spots of 79.2%/96.7%, 76.8%/97.7%, 74.0%/98.6%, and 71.8%/99.5%. Sensitivity/specificity of TST-QFT, TST-TSPOT, and QFT-TSPOT at standard cutoffs were 73.1%/99.4%, 64.8%/99.8%, and 65.3%/100%. Conclusion: Interferon-γ release assays have a better predictive ability than TST in people at high risk of LTBI.

Benchmark datasets for SARS-CoV-2 surveillance bioinformatics.

Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease 2019 (COVID-19), has spread globally and is being surveilled with an international genome sequencing effort. Surveillance consists of sample acquisition, library preparation, and whole genome sequencing. This has necessitated a classification scheme detailing Variants of Concern (VOC) and Variants of Interest (VOI), and the rapid expansion of bioinformatics tools for sequence analysis. These bioinformatic tools are means for major actionable results: maintaining quality assurance and checks, defining population structure, performing genomic epidemiology, and inferring lineage to allow reliable and actionable identification and classification. Additionally, the pandemic has required public health laboratories to reach high throughput proficiency in sequencing library preparation and downstream data analysis rapidly. However, both processes can be limited by a lack of a standardized sequence dataset. Methods: We identified six SARS-CoV-2 sequence datasets from recent publications, public databases and internal resources. In addition, we created a method to mine public databases to identify representative genomes for these datasets. Using this novel method, we identified several genomes as either VOI/VOC representatives or non-VOI/VOC representatives. To describe each dataset, we utilized a previously published datasets format, which describes accession information and whole dataset information. Additionally, a script from the same publication has been enhanced to download and verify all data from this study. Results: The benchmark datasets focus on the two most widely used sequencing platforms: long read sequencing data from the Oxford Nanopore Technologies platform and short read sequencing data from the Illumina platform. There are six datasets: three were derived from recent publications; two were derived from data mining public databases to answer common questions not covered by published datasets; one unique dataset representing common sequence failures was obtained by rigorously scrutinizing data that did not pass quality checks. The dataset summary table, data mining script and quality control (QC) values for all sequence data are publicly available on GitHub: https://github.com/CDCgov/datasets-sars-cov-2. Discussion: The datasets presented here were generated to help public health laboratories build sequencing and bioinformatics capacity, benchmark different workflows and pipelines, and calibrate QC thresholds to ensure sequencing quality. Together, improvements in these areas support accurate and timely outbreak investigation and surveillance, providing actionable data for pandemic management. Furthermore, these publicly available and standardized benchmark data will facilitate the development and adjudication of new pipelines.

Using Machine Learning Techniques and National Tuberculosis Surveillance Data to Predict Excess Growth in Genotyped Tuberculosis Clusters.

The early identification of clusters of persons with tuberculosis (TB) that will grow to become outbreaks creates an opportunity for intervention in preventing future TB cases. We used surveillance data (2009-2018) from the United States, statistically derived definitions of unexpected growth, and machine-learning techniques to predict which clusters of genotype-matched TB cases are most likely to continue accumulating cases above expected growth within a 1-year follow-up period. We developed a model to predict which clusters are likely to grow on a training and testing data set that was generalizable to a validation data set. Our model showed that characteristics of clusters were more important than the social, demographic, and clinical characteristics of the patients in those clusters. For instance, the time between cases before unexpected growth was identified as the most important of our predictors. A faster accumulation of cases increased the probability of excess growth being predicted during the follow-up period. We have demonstrated that combining the characteristics of clusters and cases with machine learning can add to existing tools to help prioritize which clusters may benefit most from public health interventions. For example, consideration of an entire cluster, not only an individual patient, may assist in interrupting ongoing transmission.

Effect of Routine Gastric Residual Aspiration on the Preterm Infant Fecal Microbiome.

OBJECTIVE: Enteral feeding tubes are used in neonatal intensive care units (NICUs) to assess feeding tolerance by utilizing preprandial gastric residual aspiration. This study evaluates the effect of gastric residual aspiration on the preterm infant fecal microbiome and gastrointestinal inflammation. STUDY DESIGN: Fifty-one very low birth weight (VLBW) infants (≤32 weeks' gestational age and ≤1,250 g) enrolled in a larger single-center randomized controlled trial evaluating the effects of routine and nonroutine gastric residual aspiration were selected for further analysis. Of those infants, 30 had microbiome analysis performed on stools collected at 6 weeks by sequencing the bacterial V1 to V3 variable regions of the genes encoding for 16S rRNA. In an additional 21 infants, stool samples collected at 3 and 6 weeks were analyzed for intestinal inflammation using a cytokine multiplex panel. RESULTS: Microbial communities between groups were not distinct from each other and there was no difference in intestinal inflammation between groups. Analyses using gene expression packages DESeq2 and edgeR produced statistically significant differences in several taxa, possibly indicating a more commensal intestinal microbiome in infants not undergoing gastric residual aspiration. CONCLUSION: Omission of routine gastric residual aspiration was not associated with intestinal dysbiosis or inflammation, providing additional evidence that monitors preprandial gastric residuals is unnecessary. KEY POINTS: · Omission of routine gastric residual aspiration was not associated with intestinal dysbiosis or inflammation.. · Existing literature indicates preprandial gastric aspiration does not reliably correlate with development of necrotizing enterocolitis but does correlate with delayed enteral nutrition.. · Further study is required but this data that suggest monitoring preprandial gastric residuals are unnecessary..

Decrease in Tuberculosis Cases during COVID-19 Pandemic as Reflected by Outpatient Pharmacy Data, United States, 2020.

We analyzed a pharmacy dataset to assess the 20% decline in tuberculosis (TB) cases reported to the US National Tuberculosis Surveillance System (NTSS) during the coronavirus disease pandemic in 2020 compared with the 2016-2019 average. We examined the correlation between TB medication dispensing data to TB case counts in NTSS and used a seasonal autoregressive integrated moving average model to predict expected 2020 counts. Trends in the TB medication data were correlated with trends in NTSS data during 2006-2019. There were fewer prescriptions and cases in 2020 than would be expected on the basis of previous trends. This decrease was particularly large during April-May 2020. These data are consistent with NTSS data, suggesting that underreporting is not occurring but not ruling out underdiagnosis or actual decline. Understanding the mechanisms behind the 2020 decline in reported TB cases will help TB programs better prepare for postpandemic cases.

Variability of interferon-γ release assays in people at high risk of tuberculosis infection or progression to tuberculosis disease living in the United States.

OBJECTIVES: Interferon-γ release assays, including T-SPOT.TB (TSPOT) and QuantiFERON Gold In-Tube (QFT), are important diagnostic tools for tuberculosis infection, but little work has been done to study the performance of these tests in populations prioritized for tuberculosis testing in the United States, especially those other than health care personnel. METHODS: Participants were enrolled as part of a large, prospective cohort of people at high risk of tuberculosis infection or progression to tuberculosis disease. All participants were administered a tuberculin skin test, TSPOT, and QFT test. A subset of participants had their QFT (n = 919) and TSPOT (n = 885) tests repeated when they returned to get their tuberculin skin test read 2 to 3 days later (repeat study). A total of 531 participants had a TSPOT performed twice on the same sample taken at the same time (split study). RESULTS: The QFT repeat test interpretations were discordant (one test positive and the other negative) for 6.4% of participants (59 of 919), and the TSPOT tests were discordant for 60 of 885 participants in the repeat study (6.8%) and 41 of 531 participants in the split study (7.7%). There was a high degree of variability in the quantitative test results for both QFT and TSPOT, and discordance was not associated with both test results being near the established cut-offs. Furthermore, the proportion of discordance was similar when comparing participants in both the TSPOT repeat and TSPOT split studies. DISCUSSION: Both QFT and TSPOT were 6% to 8% discordant. The results should be interpreted with caution, particularly when seeing a conversion or reversion in serial testing.

Model-based Analysis of Tuberculosis Genotype Clusters in the United States Reveals High Degree of Heterogeneity in Transmission and State-level Differences Across California, Florida, New York, and Texas.

BACKGROUND: Reductions in tuberculosis (TB) transmission have been instrumental in lowering TB incidence in the United States. Sustaining and augmenting these reductions are key public health priorities. METHODS: We fit mechanistic transmission models to distributions of genotype clusters of TB cases reported to the Centers for Disease Control and Prevention during 2012-2016 in the United States and separately in California, Florida, New York, and Texas. We estimated the mean number of secondary cases generated per infectious case (R0) and individual-level heterogeneity in R0 at state and national levels and assessed how different definitions of clustering affected these estimates. RESULTS: In clusters of genotypically linked TB cases that occurred within a state over a 5-year period (reference scenario), the estimated R0 was 0.29 (95% confidence interval [CI], .28-.31) in the United States. Transmission was highly heterogeneous; 0.24% of simulated cases with individual R0 >10 generated 19% of all recent secondary transmissions. R0 estimate was 0.16 (95% CI, .15-.17) when a cluster was defined as cases occurring within the same county over a 3-year period. Transmission varied across states: estimated R0s were 0.34 (95% CI, .3-.4) in California, 0.28 (95% CI, .24-.36) in Florida, 0.19 (95% CI, .15-.27) in New York, and 0.38 (95% CI, .33-.46) in Texas. CONCLUSIONS: TB transmission in the United States is characterized by pronounced heterogeneity at the individual and state levels. Improving detection of transmission clusters through incorporation of whole-genome sequencing and identifying the drivers of this heterogeneity will be essential to reducing TB transmission.

A Cluster-based Method to Quantify Individual Heterogeneity in Tuberculosis Transmission.

BACKGROUND: Recent evidence suggests transmission of Mycobacterium tuberculosis (Mtb) may be characterized by extreme individual heterogeneity in secondary cases (i.e., few cases account for the majority of transmission). Such heterogeneity implies outbreaks are rarer but more extensive and has profound implications in infectious disease control. However, discrete person-to-person transmission events in tuberculosis (TB) are often unobserved, precluding our ability to directly quantify individual heterogeneity in TB epidemiology. METHODS: We used a modified negative binomial branching process model to quantify the extent of individual heterogeneity using only observed transmission cluster size distribution data (i.e., the simple sum of all cases in a transmission chain) without knowledge of individual-level transmission events. The negative binomial parameter k quantifies the extent of individual heterogeneity (generally, indicates extensive heterogeneity, and as transmission becomes more homogenous). We validated the robustness of the inference procedure considering common limitations affecting cluster size data. Finally, we demonstrate the epidemiologic utility of this method by applying it to aggregate US molecular surveillance data from the US Centers for Disease Control and Prevention. RESULTS: The cluster-based method reliably inferred k using TB transmission cluster data despite a high degree of bias introduced into the model. We found that the TB transmission in the United States was characterized by a high propensity for extensive outbreaks (; 95% confidence interval = 0.09, 0.10). CONCLUSIONS: The proposed method can accurately quantify critical parameters that govern TB transmission using simple, more easily obtainable cluster data to improve our understanding of TB epidemiology.

Logically Inferred Tuberculosis Transmission (LITT): A Data Integration Algorithm to Rank Potential Source Cases.

Understanding tuberculosis (TB) transmission chains can help public health staff target their resources to prevent further transmission, but currently there are few tools to automate this process. We have developed the Logically Inferred Tuberculosis Transmission (LITT) algorithm to systematize the integration and analysis of whole-genome sequencing, clinical, and epidemiological data. Based on the work typically performed by hand during a cluster investigation, LITT identifies and ranks potential source cases for each case in a TB cluster. We evaluated LITT using a diverse dataset of 534 cases in 56 clusters (size range: 2-69 cases), which were investigated locally in three different U.S. jurisdictions. Investigators and LITT agreed on the most likely source case for 145 (80%) of 181 cases. By reviewing discrepancies, we found that many of the remaining differences resulted from errors in the dataset used for the LITT algorithm. In addition, we developed a graphical user interface, user's manual, and training resources to improve LITT accessibility for frontline staff. While LITT cannot replace thorough field investigation, the algorithm can help investigators systematically analyze and interpret complex data over the course of a TB cluster investigation. Code available at: https://github.com/CDCgov/TB_molecular_epidemiology/tree/1.0; https://zenodo.org/badge/latestdoi/166261171.

SARS-CoV-2 Transmission Dynamics in a Sleep-Away Camp.

OBJECTIVES: In late June 2020, a large outbreak of coronavirus disease 2019 (COVID-19) occurred at a sleep-away youth camp in Georgia, affecting primarily persons ≤21 years. We conducted a retrospective cohort study among campers and staff (attendees) to determine the extent of the outbreak and assess factors contributing to transmission. METHODS: Attendees were interviewed to ascertain demographic characteristics, known exposures to COVID-19 and community exposures, and mitigation measures before, during, and after attending camp. COVID-19 case status was determined for all camp attendees on the basis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) test results and reported symptoms. We calculated attack rates and instantaneous reproduction numbers and sequenced SARS-CoV-2 viral genomes from the outbreak. RESULTS: Among 627 attendees, the median age was 15 years (interquartile range: 12-16 years); 56% (351 of 627) of attendees were female. The attack rate was 56% (351 of 627) among all attendees. On the basis of date of illness onset or first positive test result on a specimen collected, 12 case patients were infected before arriving at camp and 339 case patients were camp associated. Among 288 case patients with available symptom information, 45 (16%) were asymptomatic. Despite cohorting, 50% of attendees reported direct contact with people outside their cabin cohort. On the first day of camp session, the instantaneous reproduction number was 10. Viral genomic diversity was low. CONCLUSIONS: Few introductions of SARS-CoV-2 into a youth congregate setting resulted in a large outbreak. Testing strategies should be combined with prearrival quarantine, routine symptom monitoring with appropriate isolation and quarantine, cohorting, social distancing, mask wearing, and enhanced disinfection and hand hygiene. Promotion of mitigation measures among younger populations is needed.

Prevalence of Latent Tuberculosis Infection Among Non-US-Born Persons by Country of Birth-United States, 2012-2017.

BACKGROUND: Most tuberculosis (TB) disease in the United States (US) is attributed to reactivation of remotely acquired latent TB infection (LTBI) in non-US-born persons who were likely infected with Mycobacterium tuberculosis in their countries of birth. Information on LTBI prevalence by country of birth could help guide local providers and health departments to scale up the LTBI screening and preventive treatment needed to advance progress toward TB elimination. METHODS: A total of 13 805 non-US-born persons at high risk of TB infection or progression to TB disease were screened for LTBI at 16 clinical sites located across the United States with a tuberculin skin test, QuantiFERON Gold In-Tube test, and T-SPOT.TB test. Bayesian latent class analysis was applied to test results to estimate LTBI prevalence and associated credible intervals (CrIs) for each country or world region of birth. RESULTS: Among the study population, the estimated LTBI prevalence was 31% (95% CrI, 26%-35%). Country-of-birth-level LTBI prevalence estimates were highest for persons born in Haiti, Peru, Somalia, Ethiopia, Vietnam, and Bhutan, ranging from 42% to 55%. LTBI prevalence estimates were lowest for persons born in Colombia, Malaysia, and Thailand, ranging from 8% to 13%. CONCLUSIONS: LTBI prevalence in persons born outside the US varies widely by country. These estimates can help target community outreach efforts to the highest-risk groups.

COVID-19 Among American Indian and Alaska Native Persons - 23 States, January 31-July 3, 2020.

Although non-Hispanic American Indian and Alaska Native (AI/AN) persons account for 0.7% of the U.S. population,* a recent analysis reported that 1.3% of coronavirus disease 2019 (COVID-19) cases reported to CDC with known race and ethnicity were among AI/AN persons (1). To assess the impact of COVID-19 among the AI/AN population, reports of laboratory-confirmed COVID-19 cases during January 22†-July 3, 2020 were analyzed. The analysis was limited to 23 states§ with >70% complete race/ethnicity information and five or more laboratory-confirmed COVID-19 cases among both AI/AN persons (alone or in combination with other races and ethnicities) and non-Hispanic white (white) persons. Among 424,899 COVID-19 cases reported by these states, 340,059 (80%) had complete race/ethnicity information; among these 340,059 cases, 9,072 (2.7%) occurred among AI/AN persons, and 138,960 (40.9%) among white persons. Among 340,059 cases with complete patient race/ethnicity data, the cumulative incidence among AI/AN persons in these 23 states was 594 per 100,000 AI/AN population (95% confidence interval [CI] = 203-1,740), compared with 169 per 100,000 white population (95% CI = 137-209) (rate ratio [RR] = 3.5; 95% CI = 1.2-10.1). AI/AN persons with COVID-19 were younger (median age = 40 years; interquartile range [IQR] = 26-56 years) than were white persons (median age = 51 years; IQR = 32-67 years). More complete case report data and timely, culturally responsive, and evidence-based public health efforts that leverage the strengths of AI/AN communities are needed to decrease COVID-19 transmission and improve patient outcomes.

Characterization of the bacterial microbiome among free-ranging bottlenose dolphins (Tursiops truncatus).

Marine animals represent a dynamic and complex habitat for diverse microbial communities. The microbiota associated with bottlenose dolphins (Tursiops truncatus) are believed to influence their health status, but it remains poorly understood. We therefore characterized and compared the bacterial microbiome of bottlenose dolphins from six different anatomical sites that represent four different body systems (respiratory, digestive, reproductive, and integumentary). In this study, a total of 14 free-ranging bottlenose dolphins were sampled during the 2015 Sarasota Bay Dolphin Health Assessment. Bacterial diversity and abundance were assessed by PCR amplification of the hypervariable V3-V4 regions of the bacterial 16S rRNA gene for each sample, followed by sequencing on an Illumina MiSeq platform. Analysis showed that bottlenose dolphins harbor diverse bacterial communities with a unique microbial community at each body system. Additionally, the bottlenose dolphin bacterial microbiome was clearly distinct to the aquatic microbiome from their surrounding habitat. These results are in close agreement with other cetacean microbiome studies, while our study is the first to explore what was found to be a diverse bottlenose dolphin genital microbiome. The core bacterial communities identified in this study in apparently healthy animals might be informative for future health monitoring of bottlenose dolphins.

Timing of Community Mitigation and Changes in Reported COVID-19 and Community Mobility - Four U.S. Metropolitan Areas, February 26-April 1, 2020.

Community mitigation activities (also referred to as nonpharmaceutical interventions) are actions that persons and communities can take to slow the spread of infectious diseases. Mitigation strategies include personal protective measures (e.g., handwashing, cough etiquette, and face coverings) that persons can use at home or while in community settings; social distancing (e.g., maintaining physical distance between persons in community settings and staying at home); and environmental surface cleaning at home and in community settings, such as schools or workplaces. Actions such as social distancing are especially critical when medical countermeasures such as vaccines or therapeutics are not available. Although voluntary adoption of social distancing by the public and community organizations is possible, public policy can enhance implementation. The CDC Community Mitigation Framework (1) recommends a phased approach to implementation at the community level, as evidence of community spread of disease increases or begins to decrease and according to severity. This report presents initial data from the metropolitan areas of San Francisco, California; Seattle, Washington; New Orleans, Louisiana; and New York City, New York* to describe the relationship between timing of public policy measures, community mobility (a proxy measure for social distancing), and temporal trends in reported coronavirus disease 2019 (COVID-19) cases. Community mobility in all four locations declined from February 26, 2020 to April 1, 2020, decreasing with each policy issued and as case counts increased. This report suggests that public policy measures are an important tool to support social distancing and provides some very early indications that these measures might help slow the spread of COVID-19.

Human Colon Mucosal Biofilms and Murine Host Communicate via Altered mRNA and microRNA Expression during Cancer.

Disrupted interactions between host and intestinal bacteria are implicated in colorectal cancer (CRC) development. However, activities derived from these bacteria and their interplay with the host are unclear. Here, we examine this interplay by performing mouse and microbiota RNA sequencing on colon tissues and 16S and small RNA sequencing on stools from germfree (GF) and gnotobiotic ApcMin Δ 850/+ ;Il10-/- mice associated with microbes from biofilm-positive human CRC tumor (BF+T) and biofilm-negative healthy (BF-bx) tissues. The bacteria in BF+T mice differentially expressed (DE) >2,900 genes, including genes related to bacterial secretion, virulence, and biofilms but affected only 62 host genes. Small RNA sequencing of stools from these cohorts revealed eight significant DE host microRNAs (miRNAs) based on biofilm status and several miRNAs that correlated with bacterial taxon abundances. Additionally, computational predictions suggest that some miRNAs preferentially target bacterial genes while others primarily target mouse genes. 16S rRNA sequencing of mice that were reassociated with mucosa-associated communities from the initial association revealed a set of 13 bacterial genera associated with cancer that were maintained regardless of whether the reassociation inoculums were initially obtained from murine proximal or distal colon tissues. Our findings suggest that complex interactions within bacterial communities affect host-derived miRNA, bacterial composition, and CRC development.IMPORTANCE Bacteria and bacterial biofilms have been implicated in colorectal cancer (CRC), but it is still unclear what genes these microbial communities express and how they influence the host. MicroRNAs regulate host gene expression and have been explored as potential biomarkers for CRC. An emerging area of research is the ability of microRNAs to impact growth and gene expression of members of the intestinal microbiota. This study examined the bacteria and bacterial transcriptome associated with microbes derived from biofilm-positive human cancers that promoted tumorigenesis in a murine model of CRC. The murine response to different microbial communities (derived from CRC patients or healthy people) was evaluated through RNA and microRNA sequencing. We identified a complex interplay between biofilm-associated bacteria and the host during CRC in mice. These findings may lead to the development of new biomarkers and therapeutics for identifying and treating biofilm-associated CRCs.

Human colon mucosal biofilms from healthy or colon cancer hosts are carcinogenic.

Mucus-invasive bacterial biofilms are identified on the colon mucosa of approximately 50% of colorectal cancer (CRC) patients and approximately 13% of healthy subjects. Here, we test the hypothesis that human colon biofilms comprise microbial communities that are carcinogenic in CRC mouse models. Homogenates of human biofilm-positive colon mucosa were prepared from tumor patients (tumor and paired normal tissues from surgical resections) or biofilm-positive biopsies from healthy individuals undergoing screening colonoscopy; homogenates of biofilm-negative colon biopsies from healthy individuals undergoing screening colonoscopy served as controls. After 12 weeks, biofilm-positive, but not biofilm-negative, human colon mucosal homogenates induced colon tumor formation in 3 mouse colon tumor models (germ-free ApcMinΔ850/+;Il10-/- or ApcMinΔ850/+ and specific pathogen-free ApcMinΔ716/+ mice). Remarkably, biofilm-positive communities from healthy colonoscopy biopsies induced colon inflammation and tumors similarly to biofilm-positive tumor tissues. By 1 week, biofilm-positive human tumor homogenates, but not healthy biopsies, displayed consistent bacterial mucus invasion and biofilm formation in mouse colons. 16S rRNA gene sequencing and RNA-Seq analyses identified compositional and functional microbiota differences between mice colonized with biofilm-positive and biofilm-negative communities. These results suggest human colon mucosal biofilms, whether from tumor hosts or healthy individuals undergoing screening colonoscopy, are carcinogenic in murine models of CRC.

Gut Microbiota Composition and Blood Pressure.

Animal models support a role for the gut microbiota in the development of hypertension. There has been a lack of epidemiological cohort studies to confirm these findings in human populations. We examined cross-sectional associations between measures of gut microbial diversity and taxonomic composition and blood pressure (BP) in 529 participants of the biracial (black and white) CARDIA study (Coronary Artery Risk Development in Young Adults). We sequenced V3-V4 regions of the 16S ribosomal RNA marker gene using DNA extracted from stool samples collected at CARDIA's Year 30 follow-up examination (2015-2016; aged 48-60 years). We quantified associations between BP (hypertension [defined as systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg or antihypertension medication use] and systolic BP) and within and between-person diversity measures. We conducted genera-specific multivariable-adjusted regression analysis, accounting for multiple comparisons using the false discovery rate. Hypertension and systolic BP were inversely associated with measures of α-diversity, including richness and the Shannon Diversity Index, and were distinguished with respect to principal coordinates based on a similarity matrix of genera abundance. Several specific genera were significantly associated with hypertension and systolic BP, though results were attenuated with adjustment for body mass index. Our findings support associations between within-person and between-person gut microbial community diversity and taxonomic composition and BP in a diverse population-based cohort of middle-aged adults. Future study is needed to define functional pathways that underlie observed associations and identify specific microbial targets for intervention.

Initial microbial community of the neonatal stomach immediately after birth.

The purpose of this prospective cross-sectional cohort pilot study is to explore the initial microbial community of gastric aspirate fluid as collected immediately after birth and its relationships with mode of delivery and preterm birth. Twenty-nine gastric aspirate samples collected immediately after birth from infants born between 24-40 weeks gestation were analyzed for microbial composition. Total microbial content was low in many samples, with a substantial number sharing taxonomic composition with negative controls. qPCR targeting the 16S rRNA gene showed that infants delivered vaginally had a higher microbial load than infants delivered by C-section. Some pre-term samples showed high relative abundance of genus Ureaplasma, consistent with previous literature that has implicated infections with this taxon as a potential cause of pre-term birth. Vaginally born term infant samples, by contrast, had significantly higher levels of genus Lactobacillus with Lactobacillus crispatus the most dominant species. Microbial evaluation showed that vaginally born term infant gastric aspirate samples had higher levels of lactobacilli than pre-terms. Samples from many infants had low microbial load near the edge of the detection limit.

Whole Genome Sequencing detects Inter-Facility Transmission of Carbapenem-resistant Klebsiella pneumoniae.

OBJECTIVES: To identify transmission patterns of Carbapenem-resistant Klebsiella pneumoniae infection during an outbreak at a large, tertiary care hospital and to detect whether the outbreak organisms spread to other facilities in the integrated healthcare network. METHODS: We analyzed 71 K. pneumoniae whole genome sequences collected from clinical specimens before, during and after the outbreak and reviewed corresponding patient medical records. Sequence and patient data were used to model probable transmissions and assess factors associated with the outbreak. RESULTS: We identified close genetic relationships among carbapenem-resistant K. pneumoniae isolates sampled during the study period. Transmission tree analysis combined with patient records uncovered extended periods of silent colonization in many study patients and transmission routes that were likely the result of asymptomatic patients transitioning between facilities. CONCLUSIONS: Detecting how and where Carbapenem-resistant K. pneumoniae infections spread is challenging in an environment of rising prevalence, asymptomatic carriage and mobility of patients. Whole genome sequencing improved the precision of investigating inter-facility transmissions. Our results emphasize that containment of Carbapenem-resistant K. pneumoniae infections requires coordinated efforts between healthcare networks and settings of care that acknowledge and mitigate transmission risk conferred by undetected carriage and by patient transfers between facilities.