Metabolic Synergy between Human Symbionts Bacteroides and Methanobrevibacter.

Trophic interactions between microbes are postulated to determine whether a host microbiome is healthy or causes predisposition to disease. Two abundant taxa, the Gram-negative heterotrophic bacterium Bacteroides thetaiotaomicron and the methanogenic archaeon Methanobrevibacter smithii, are proposed to have a synergistic metabolic relationship. Both organisms play vital roles in human gut health; B. thetaiotaomicron assists the host by fermenting dietary polysaccharides, whereas M. smithii consumes end-stage fermentation products and is hypothesized to relieve feedback inhibition of upstream microbes such as B. thetaiotaomicron. To study their metabolic interactions, we defined and optimized a coculture system and used software testing techniques to analyze growth under a range of conditions representing the nutrient environment of the host. We verify that B. thetaiotaomicron fermentation products are sufficient for M. smithii growth and that accumulation of fermentation products alters secretion of metabolites by B. thetaiotaomicron to benefit M. smithii. Studies suggest that B. thetaiotaomicron metabolic efficiency is greater in the absence of fermentation products or in the presence of M. smithii. Under certain conditions, B. thetaiotaomicron and M. smithii form interspecies granules consistent with behavior observed for syntrophic partnerships between microbes in soil or sediment enrichments and anaerobic digesters. Furthermore, when vitamin B12, hematin, and hydrogen gas are abundant, coculture growth is greater than the sum of growth observed for monocultures, suggesting that both organisms benefit from a synergistic mutual metabolic relationship. IMPORTANCE The human gut functions through a complex system of interactions between the host human tissue and the microbes which inhabit it. These diverse interactions are difficult to model or examine under controlled laboratory conditions. We studied the interactions between two dominant human gut microbes, B. thetaiotaomicron and M. smithii, using a seven-component culturing approach that allows the systematic examination of the metabolic complexity of this binary microbial system. By combining high-throughput methods with machine learning techniques, we were able to investigate the interactions between two dominant genera of the gut microbiome in a wide variety of environmental conditions. Our approach can be broadly applied to studying microbial interactions and may be extended to evaluate and curate computational metabolic models. The software tools developed for this study are available as user-friendly tutorials in the Department of Energy KBase.

p-Nitrophenyl esters provide new insights and applications for the thiolase enzyme OleA.

The OleA enzyme is distinct amongst thiolase enzymes in binding two long (≥C8) acyl chains into structurally-opposed hydrophobic channels, denoted A and B, to carry out a non-decarboxylative Claisen condensation reaction and initiate the biosynthesis of membrane hydrocarbons and ß-lactone natural products. OleA has now been identified in hundreds of diverse bacteria via bioinformatics and high-throughput screening using p-nitrophenyl alkanoate esters as surrogate substrates. In the present study, p-nitrophenyl esters were used to probe the reaction mechanism of OleA and shown to be incorporated into Claisen condensation products for the first time. p-Nitrophenyl alkanoate substrates alone were shown not to undergo Claisen condensation, but co-incubation of p-nitrophenyl esters and CoA thioesters produced mixed Claisen products. Mixed product reactions were shown to initiate via acyl group transfer from a p-nitrophenyl carrier to the enzyme active site cysteine, C143. Acyl chains esterified to p-nitrophenol were synthesized and shown to undergo Claisen condensation with an acyl-CoA substrate, showing potential to greatly expand the range of possible Claisen products. Using p-nitrophenyl 1-13C-decanoate, the Channel A bound thioester chain was shown to act as the Claisen nucleophile, representing the first direct evidence for the directionality of the Claisen reaction in any OleA enzyme. These results both provide new insights into OleA catalysis and open a path for making unnatural hydrocarbon and ß-lactone natural products for biotechnological applications using cheap and easily synthesized p-nitrophenyl esters.

Global analysis of adenylate-forming enzymes reveals ß-lactone biosynthesis pathway in pathogenic Nocardia.

Enzymes that cleave ATP to activate carboxylic acids play essential roles in primary and secondary metabolism in all domains of life. Class I adenylate-forming enzymes share a conserved structural fold but act on a wide range of substrates to catalyze reactions involved in bioluminescence, nonribosomal peptide biosynthesis, fatty acid activation, and ß-lactone formation. Despite their metabolic importance, the substrates and functions of the vast majority of adenylate-forming enzymes are unknown without tools available to accurately predict them. Given the crucial roles of adenylate-forming enzymes in biosynthesis, this also severely limits our ability to predict natural product structures from biosynthetic gene clusters. Here we used machine learning to predict adenylate-forming enzyme function and substrate specificity from protein sequences. We built a web-based predictive tool and used it to comprehensively map the biochemical diversity of adenylate-forming enzymes across >50,000 candidate biosynthetic gene clusters in bacterial, fungal, and plant genomes. Ancestral phylogenetic reconstruction and sequence similarity networking of enzymes from these clusters suggested divergent evolution of the adenylate-forming superfamily from a core enzyme scaffold most related to contemporary CoA ligases toward more specialized functions including ß-lactone synthetases. Our classifier predicted ß-lactone synthetases in uncharacterized biosynthetic gene clusters conserved in >90 different strains of Nocardia. To test our prediction, we purified a candidate ß-lactone synthetase from Nocardia brasiliensis and reconstituted the biosynthetic pathway in vitro to link the gene cluster to the ß-lactone natural product, nocardiolactone. We anticipate that our machine learning approach will aid in functional classification of enzymes and advance natural product discovery.

In Vivo Assay Reveals Microbial OleA Thiolases Initiating Hydrocarbon and ß-Lactone Biosynthesis.

OleA, a member of the thiolase superfamily, is known to catalyze the Claisen condensation of long-chain acyl coenzyme A (acyl-CoA) substrates, initiating metabolic pathways in bacteria for the production of membrane lipids and ß-lactone natural products. OleA homologs are found in diverse bacterial phyla, but to date, only one homodimeric OleA has been successfully purified to homogeneity and characterized in vitro A major impediment for the identification of new OleA enzymes has been protein instability and time-consuming in vitro assays. Here, we developed a bioinformatic pipeline to identify OleA homologs and a new rapid assay to screen OleA enzyme activity in vivo and map their taxonomic diversity. The screen is based on the discovery that OleA displayed surprisingly high rates of p-nitrophenyl ester hydrolysis, an activity not shared by other thiolases, including FabH. The high rates allowed activity to be determined in vitro and with heterologously expressed OleA in vivo via the release of the yellow p-nitrophenol product. Seventy-four putative oleA genes identified in the genomes of diverse bacteria were heterologously expressed in Escherichia coli, and 25 showed activity with p-nitrophenyl esters. The OleA proteins tested were encoded in variable genomic contexts from seven different phyla and are predicted to function in distinct membrane lipid and ß-lactone natural product metabolic pathways. This study highlights the diversity of unstudied OleA proteins and presents a rapid method for their identification and characterization.IMPORTANCE Microbially produced ß-lactones are found in antibiotic, antitumor, and antiobesity drugs. Long-chain olefinic membrane hydrocarbons have potential utility as fuels and specialty chemicals. The metabolic pathway to both end products share bacterial enzymes denoted as OleA, OleC, and OleD that transform acyl-CoA cellular intermediates into ß-lactones. Bacteria producing membrane hydrocarbons via the Ole pathway additionally express a ß-lactone decarboxylase, OleB. Both ß-lactone and olefin biosynthesis pathways are initiated by OleA enzymes that define the overall structure of the final product. There is currently very limited information on OleA enzymes apart from the single representative from Xanthomonas campestris In this study, bioinformatic analysis identified hundreds of new, putative OleA proteins, 74 proteins were screened via a rapid whole-cell method, leading to the identification of 25 stably expressed OleA proteins representing seven bacteria phyla.

New-Onset Diabetes Educator to Educate Children and Their Caregivers About Diabetes at the Time of Diagnosis: Usability Study.

BACKGROUND: Diabetes self-management education is essential at the time of diagnosis. We developed the New-Onset Diabetes Educator (NODE), an animation-based educational web application for type 1 diabetes mellitus patients. OBJECTIVE: Our hypothesis is that NODE is a feasible, effective and user-friendly intervention in improving diabetes self-management education delivery to child/caregiver-dyads at the time of diagnosis. METHODS: We used a pragmatic parallel randomized trial design. Dyads were recruited within 48 hours of diagnosis and randomized into a NODE-enhanced diabetes self-management education or a standard diabetes self-management education group. Dyads randomized in the NODE group received the intervention on an iPad before receiving the standard diabetes self-management education with a nurse educator. The Diabetes Knowledge Test 2 assessed disease-specific knowledge pre- and postintervention in both groups, and was compared using t tests. Usability of the NODE mobile health intervention was assessed in the NODE group. RESULTS: We recruited 16 dyads (mean child age 10.75, SD 3.44). Mean Diabetes Knowledge Test 2 scores were 14.25 (SD 4.17) and 18.13 (SD 2.17) pre- and postintervention in the NODE group, and 15.50 (SD 2.67) and 17.38 (SD 2.26) in the standard diabetes self-management education group. The effect size was medium (Δ=0.56). Usability ratings of NODE were excellent. CONCLUSIONS: NODE is a feasible mobile health strategy for type 1 diabetes education. It has the potential to be an effective and scalable tool to enhance diabetes self-management education at time of diagnosis, and consequently, could lead to improved long-term clinical outcomes for patients living with the disease.

A 3-minute test of cardiorespiratory fitness for use in primary care clinics.

BACKGROUND: Cardiorespiratory fitness (CRF) is the only major risk factor that is not routinely assessed in the clinical setting, for preventive medicine. A valid and practical CRF test is needed for use in the clinics. The objective of this study is to demonstrate the validity of a 3-minute squat test to assess CRF in primary care. METHODS: A cross-sectional study in which the participants performed both the Ruffier squat test and the Balke maximal treadmill test. The study was conducted in a clinical setting from September 2016 to March 2017. We recruited a convenient sample of 40 adults between 18 and 64 years from the general U.S. population. Participants completed 30 squats in 45 seconds, paced by a metronome. Heart rate was measured at rest (P1), immediately after the test (P2), one minute after the test (P3). [Formula: see text] was measured using the Balke maximal treadmill fitness test. RESULTS: Of the 40 participants, there were 18 men and 22 women. Mean age was 31.2 years (SD = 9.9). We found that the best [Formula: see text] predictors were HR features P1/height and (P2-P3)/age3. Our best-performing model using these two features predicted individuals' CRF levels with an adjusted R2 of 0.637, sensitivity of 0.79, and specificity of 0.56. CONCLUSIONS: The study provided strong evidence for the validity of the squat test in the clinical setting. Further, the equation of our model along with [Formula: see text] normative tables provides an efficient and easy way to assess CRF in a primary care setting.

Mobile Device Accuracy for Step Counting Across Age Groups.

BACKGROUND: Only one in five American meets the physical activity recommendations of the Department of Health and Human Services. The proliferation of wearable devices and smartphones for physical activity tracking has led to an increasing number of interventions designed to facilitate regular physical activity, in particular to address the obesity epidemic, but also for cardiovascular disease patients, cancer survivors, and older adults. However, the inconsistent findings pertaining to the accuracy of wearable devices for step counting needs to be addressed, as well as factors known to affect gait (and thus potentially impact accuracy) such as age, body mass index (BMI), or leading arm. OBJECTIVE: We aim to assess the accuracy of recent mobile devices for counting steps, across three different age groups. METHODS: We recruited 60 participants in three age groups: 18-39 years, 40-64 years, and 65-84 years, who completed two separate 1000 step walks on a treadmill at a self-selected speed between 2 and 3 miles per hour. We tested two smartphones attached on each side of the waist, and five wrist-based devices worn on both wrists (2 devices on one wrist and 3 devices on the other), as well as the Actigraph wGT3X-BT, and swapped sides between each walk. All devices were swapped dominant-to-nondominant side and vice-versa between the two 1000 step walks. The number of steps was recorded with a tally counter. Age, sex, height, weight, and dominant hand were self-reported by each participant. RESULTS: Among the 60 participants, 36 were female (60%) and 54 were right-handed (90%). Median age was 53 years (min=19, max=83), median BMI was 24.1 (min=18.4, max=39.6). There was no significant difference in left- and right-hand step counts by device. Our analyses show that the Fitbit Surge significantly undercounted steps across all age groups. Samsung Gear S2 significantly undercounted steps only for participants among the 40-64 year age group. Finally, the Nexus 6P significantly undercounted steps for the group ranging from 65-84 years. CONCLUSIONS: Our analysis shows that apart from the Fitbit Surge, most of the recent mobile devices we tested do not overcount or undercount steps in the 18-39-year-old age group, however some devices undercount steps in older age groups. This finding suggests that accuracy in step counting may be an issue with some popular wearable devices, and that age may be a factor in undercounting. These results are particularly important for clinical interventions using such devices and other activity trackers, in particular to balance energy requirements with energy expenditure in the context of a weight loss intervention program.

Assessing the Quality of Mobile Exercise Apps Based on the American College of Sports Medicine Guidelines: A Reliable and Valid Scoring Instrument.

BACKGROUND: Regular physical activity can not only help with weight management, but also lower cardiovascular risks, cancer rates, and chronic disease burden. Yet, only approximately 20% of Americans currently meet the physical activity guidelines recommended by the US Department of Health and Human Services. With the rapid development of mobile technologies, mobile apps have the potential to improve participation rates in exercise programs, particularly if they are evidence-based and are of sufficient content quality. OBJECTIVE: The goal of this study was to develop and test an instrument, which was designed to score the content quality of exercise program apps with respect to the exercise guidelines set forth by the American College of Sports Medicine (ACSM). METHODS: We conducted two focus groups (N=14) to elicit input for developing a preliminary 27-item scoring instruments based on the ACSM exercise prescription guidelines. Three reviewers who were no sports medicine experts independently scored 28 exercise program apps using the instrument. Inter- and intra-rater reliability was assessed among the 3 reviewers. An expert reviewer, a Fellow of the ACSM, also scored the 28 apps to create criterion scores. Criterion validity was assessed by comparing nonexpert reviewers' scores to the criterion scores. RESULTS: Overall, inter- and intra-rater reliability was high with most coefficients being greater than .7. Inter-rater reliability coefficients ranged from .59 to .99, and intra-rater reliability coefficients ranged from .47 to 1.00. All reliability coefficients were statistically significant. Criterion validity was found to be excellent, with the weighted kappa statistics ranging from .67 to .99, indicating a substantial agreement between the scores of expert and nonexpert reviewers. Finally, all apps scored poorly against the ACSM exercise prescription guidelines. None of the apps received a score greater than 35, out of a possible maximal score of 70. CONCLUSIONS: We have developed and presented valid and reliable scoring instruments for exercise program apps. Our instrument may be useful for consumers and health care providers who are looking for apps that provide safe, progressive general exercise programs for health and fitness.