Health and disease markers correlate with gut microbiome composition across thousands of people.

Variation in the human gut microbiome can reflect host lifestyle and behaviors and influence disease biomarker levels in the blood. Understanding the relationships between gut microbes and host phenotypes are critical for understanding wellness and disease. Here, we examine associations between the gut microbiota and ~150 host phenotypic features across ~3,400 individuals. We identify major axes of taxonomic variance in the gut and a putative diversity maximum along the Firmicutes-to-Bacteroidetes axis. Our analyses reveal both known and unknown associations between microbiome composition and host clinical markers and lifestyle factors, including host-microbe associations that are composition-specific. These results suggest potential opportunities for targeted interventions that alter the composition of the microbiome to improve host health. By uncovering the interrelationships between host diet and lifestyle factors, clinical blood markers, and the human gut microbiome at the population-scale, our results serve as a roadmap for future studies on host-microbe interactions and interventions

Genetic Predisposition Impacts Clinical Changes in a Lifestyle Coaching Program.

Both genetic and lifestyle factors contribute to an individual\u27s disease risk, suggesting a multi-omic approach is essential for personalized prevention. Studies have examined the effectiveness of lifestyle coaching on clinical outcomes, however, little is known about the impact of genetic predisposition on the response to lifestyle coaching. Here we report on the results of a real-world observational study in 2531 participants enrolled in a commercial Scientific Wellness program, which combines multi-omic data with personalized, telephonic lifestyle coaching. Specifically, we examined: 1) the impact of this program on 55 clinical markers and 2) the effect of genetic predisposition on these clinical changes. We identified sustained improvements in clinical markers related to cardiometabolic risk, inflammation, nutrition, and anthropometrics. Notably, improvements in HbA1c were akin to those observed in landmark trials. Furthermore, genetic markers were associated with longitudinal changes in clinical markers. For example, individuals with genetic predisposition for higher LDL-C had a lesser decrease in LDL-C on average than those with genetic predisposition for average LDL-C. Overall, these results suggest that a program combining multi-omic data with lifestyle coaching produces clinically meaningful improvements, and that genetic predisposition impacts clinical responses to lifestyle change

Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome.

The Pioneer 100 Wellness Project involved quantitatively profiling 108 participants\u27 molecular physiology over time, including genomes, gut microbiomes, blood metabolomes, blood proteomes, clinical chemistries, and data from wearable devices. Here, we present a longitudinal analysis focused specifically around the Pioneer 100 gut microbiomes. We distinguished a subpopulation of individuals with reduced gut diversity, elevated relative abundance of the genus Prevotella, and reduced levels of the genus Bacteroides We found that the relative abundances of Bacteroides and Prevotella were significantly correlated with certain serum metabolites, including omega-6 fatty acids. Primary dimensions in distance-based redundancy analysis of clinical chemistries explained 18.5% of the variance in bacterial community composition, and revealed a Bacteroides/Prevotella dichotomy aligned with inflammation and dietary markers. Finally, longitudinal analysis of gut microbiome dynamics within individuals showed that direct transitions between Bacteroides-dominated and Prevotella-dominated communities were rare, suggesting the presence of a barrier between these states. One implication is that interventions seeking to transition between Bacteroides- and Prevotella-dominated communities will need to identify permissible paths through ecological state-space that circumvent this apparent barrier

CRISPR-Cas9 interrogation of a putative fetal globin repressor in human erythroid cells

Sickle Cell Disease and beta-thalassemia, which are caused by defective or deficient adult beta-globin (HBB) respectively, are the most common serious genetic blood diseases in the world. Persistent expression of the fetal beta-like globin, also known gamma-globin, can ameliorate both disorders by serving in place of the adult beta-globin as a part of the fetal hemoglobin tetramer (HbF). Here we use CRISPR-Cas9 gene editing to explore a potential gamma-globin silencer region upstream of the delta-globin gene identified by comparison of naturally-occurring deletion mutations associated with up-regulated gamma-globin. We find that deletion of a 1.7 kb consensus element or select 350 bp sub-regions from bulk populations of cells increases levels of HbF. Screening of individual sgRNAs in one sub-region revealed three single guides that caused increases gamma-globin expression. Deletion of the 1.7 kb region in HUDEP-2 clonal sublines, and in colonies derived from CD34+ hematopoietic stem/progenitor cells (HSPCs), does not cause significant up-regulation of gamma-globin. These data suggest that the 1.7 kb region is not an autonomous gamma-globin silencer, and thus by itself is not a suitable therapeutic target for gene editing treatment of beta-hemoglobinopathies.Peer reviewe

Respondent-Driven Sampling of Injection Drug Users in Two U.S.–Mexico Border Cities: Recruitment Dynamics and Impact on Estimates of HIV and Syphilis Prevalence

Respondent-driven sampling (RDS), a chain referral sampling approach, is increasingly used to recruit participants from hard-to-reach populations, such as injection drug users (IDUs). Using RDS, we recruited IDUs in Tijuana and Ciudad (Cd.) Juárez, two Mexican cities bordering San Diego, CA and El Paso, TX, respectively, and compared recruitment dynamics, reported network size, and estimates of HIV and syphilis prevalence. Between February and April 2005, we used RDS to recruit IDUs in Tijuana (15 seeds, 207 recruits) and Cd. Juárez (9 seeds, 197 recruits), Mexico for a cross-sectional study of behavioral and contextual factors associated with HIV, HCV and syphilis infections. All subjects provided informed consent, an anonymous interview, and a venous blood sample for serologic testing of HIV, HCV, HBV (Cd. Juárez only) and syphilis antibody. Log-linear models were used to analyze the association between the state of the recruiter and that of the recruitee in the referral chains, and population estimates of the presence of syphilis antibody were obtained, correcting for biased sampling using RDS-based estimators. Sampling of the targeted 200 recruits per city was achieved rapidly (2 months in Tijuana, 2 weeks in Cd. Juárez). After excluding seeds and missing data, the sample prevalence of HCV, HIV and syphilis were 96.6, 1.9 and 13.5% respectively in Tijuana, and 95.3, 4.1, and 2.7% respectively in Cd. Juárez (where HBV prevalence was 84.7%). Syphilis cases were clustered in recruitment trees. RDS-corrected estimates of syphilis antibody prevalence ranged from 12.8 to 26.8% in Tijuana and from 2.9 to 15.6% in Ciudad Juárez, depending on how recruitment patterns were modeled, and assumptions about how network size affected an individual’s probability of being included in the sample. RDS was an effective method to rapidly recruit IDUs in these cities. Although the frequency of HIV was low, syphilis prevalence was high, particularly in Tijuana. RDS-corrected estimates of syphilis prevalence were sensitive to model assumptions, suggesting that further validation of RDS is necessary

A wellness study of 108 individuals using personal, dense, dynamic data clouds.

Personal data for 108 individuals were collected during a 9-month period, including whole genome sequences; clinical tests, metabolomes, proteomes, and microbiomes at three time points; and daily activity tracking. Using all of these data, we generated a correlation network that revealed communities of related analytes associated with physiology and disease. Connectivity within analyte communities enabled the identification of known and candidate biomarkers (e.g., gamma-glutamyltyrosine was densely interconnected with clinical analytes for cardiometabolic disease). We calculated polygenic scores from genome-wide association studies (GWAS) for 127 traits and diseases, and used these to discover molecular correlates of polygenic risk (e.g., genetic risk for inflammatory bowel disease was negatively correlated with plasma cystine). Finally, behavioral coaching informed by personal data helped participants to improve clinical biomarkers. Our results show that measurement of personal data clouds over time can improve our understanding of health and disease, including early transitions to disease states

Differential Effects of Migration and Deportation on HIV Infection among Male and Female Injection Drug Users in Tijuana, Mexico

HIV prevalence is rising, especially among high risk females in Tijuana, Baja California, a Mexico-US border city situated on major migration and drug trafficking routes. We compared factors associated with HIV infection among male and female injection drug users (IDUs) in Tijuana in an effort to inform HIV prevention and treatment programs. IDUs aged ≥18 years were recruited using respondent-driven sampling and underwent testing for HIV, syphilis and structured interviews. Logistic regression identified correlates of HIV infection, stratified by gender. Among 1056 IDUs, most were Mexican-born but 67% were born outside Tijuana. Reasons for moving to Tijuana included deportation from the US (56% for males, 29% for females), and looking for work/better life (34% for females, 15% for males). HIV prevalence was higher in females versus males (10.2% vs. 3.5%, p = 0.001). Among females (N = 158), factors independently associated with higher HIV prevalence included younger age, lifetime syphilis infection and living in Tijuana for longer durations. Among males (N = 898), factors independently associated with higher HIV prevalence were syphilis titers consistent with active infection, being arrested for having ‘track-marks’, having larger numbers of recent injection partners and living in Tijuana for shorter durations. An interaction between gender and number of years lived in Tijuana regressed on HIV infection was significant (p = 0.03). Upon further analysis, deportation from the U.S. explained the association between shorter duration lived in Tijuana and HIV infection among males; odds of HIV infection were four-fold higher among male injectors deported from the US, compared to other males, adjusting for all other significant correlates (p = 0.002). Geographic mobility has a profound influence on Tijuana's evolving HIV epidemic, and its impact is significantly modified by gender. Future studies are needed to elucidate the context of mobility and HIV acquisition in this region, and whether US immigration policies adversely affect HIV risk

Optimal Sampling Strategies for Therapeutic Drug Monitoring of First-Line Tuberculosis Drugs in Patients with Tuberculosis

BACKGROUND: The 24-h area under the concentration-time curve (AUC24)/minimal inhibitory concentration ratio is the best predictive pharmacokinetic/pharmacodynamic (PK/PD) parameter of the efficacy of first-line anti-tuberculosis (TB) drugs. An optimal sampling strategy (OSS) is useful for accurately estimating AUC24; however, OSS has not been developed in the fed state or in the early phase of treatment for first-line anti-TB drugs. METHODS: An OSS for the prediction of AUC24 of isoniazid, rifampicin, ethambutol and pyrazinamide was developed for TB patients starting treatment. A prospective, randomized, crossover trial was performed during the first 3 days of treatment in which first-line anti-TB drugs were administered either intravenously or in fasting or fed conditions. The PK data were used to develop OSS with best subset selection multiple linear regression. The OSS was internally validated using a jackknife analysis and externally validated with other patients from different ethnicities and in a steady state of treatment. RESULTS: OSS using time points of 2, 4 and 8 h post-dose performed best. Bias was < 5% and imprecision was < 15% for all drugs except ethambutol in the fed condition. External validation showed that OSS2-4-8 cannot be used for rifampicin in steady state conditions. CONCLUSION: OSS at 2, 4 and 8 h post-dose enabled an accurate and precise prediction of AUC24 values of first-line anti-TB drugs in this population. TRIAL REGISTRATION: ClinicalTrials.gov (NCT02121314)